Signal and Information Processing, Networking and Computers: Proceedings of the 10th International Conference on Signal and Information Processing, Networking and Computers (ICSINC) 9811999678, 9789811999673

Table of contents :
Preface
Committee Members
Contents
Information Technology Session 1
A New Multi-instance Learning Algorithm Integrated with TF-IDF for Entity Relation Extraction in Electronic Medical Records
1 Introduction
1.1 Background
1.2 Our Innovation
2 Model Architecture
2.1 Overview
2.2 Neural Network
2.3 Attention Mechanism
3 Experiment and Analysis
4 Conclusion
References
A Deep Learning Algorithm Using Feature Engineering to Adjust Attention Mechanisms and Neural Network for Cloud Security Detection
1 Introduction
2 Model Architecture
2.1 Overview
2.2 Neural Network
2.3 Attention Mechanism
3 Experiment
3.1 Data Preprocessing
3.2 Test and Result
4 Conclusion
References
The Design and Implementation of a Ranging System Based on the DSSS
1 Introduction
2 The Principle of Incoherent Spread Spectrum Ranging
3 PN Code Selection
4 Implementation of the Ranging
5 Despreading and Demodulation Principle
6 System Performance Test Verification
7 Conclusion
References
Application of Grey Approximation Ideal Solution Ranking Methods in Optimal Selection of Satellite Initial Orbits
1 Introduction
2 Design Idea of Technique for Grey Approximation Ideal Solution Ranking Method (GAISRM)
3 Solving Steps of GAISRM
3.1 Optimal Theoretical Solution
3.2 Optimal Weighted Solution
4 Example Calculation
4.1 Optimal Theoretical Solution
4.2 Optimal Weighted Solution
5 Comparative Analysis of SPEARMAN’S Correlation Coefficient
6 Conclusions
References
Filter and Closed Loop Control Technology of Piezoelectric Drivers
1 The Introduction
2 Design of Filtering Closed-Loop Control Algorithm
2.1 PID Control Algorithm Design
2.2 Butterworth Filter Design
3 Test Verification and Result Analysis
4 Conclusion
References
A Novel Back-Feed Double-Layer Microstrip Antenna
1 Introduction
2 Antenna Structure Design
3 Simulation and Measured Results
4 Conclusion
References
A Full-Digital Test Method for Satellite-Borne Software
1 Foreword
2 Full-Digital Software Testing Environment Construction
2.1 Digital Test Platform Construction
2.2 External Input Modeling
3 Test Case Design
4 Situation of Application
4.1 Posture and Orbit Control Software Test
4.2 Energy Management Software Testing
5 End
References
3D Warehousing: Enabling Intelligent Warehousing Visualization Based on Three.js
1 Introduction
2 Key Technology
2.1 WebGL Technology and Three.js Engine
2.2 3D Model Production
3 System Architecture
4 System Function
4.1 Warehousing Data Visualization
4.2 Device Data Visualization
4.3 Security Monitoring Visualization
4.4 Warehouse Area Roaming and Inventory Increases and Decreases Visualization
5 System Test
6 Conclusion
References
Deep Learning Neural Networks with Auto-adjustable Attention Mechanism for Server Fault Diagnosis Under Log Data
1 Introduction
2 Model Architecture
2.1 Overview
2.2 Pre-processing
2.3 Seq2Seq with Attention Mechanism
3 Experiment
4 Conclusion
References
A Hierarchical Classification Algorithm of Spacecraft Telemetry Data Based on Time Series Characteristics
1 Introduction
2 Classification Algorithm
2.1 Pre-processing Module
2.2 Analysis Module
2.3 Feature Process Module
2.4 Clustering Module
2.5 Classification Module
3 Automated Procedures
3.1 Clustering Period
3.2 Classification Period
4 Data Validation
5 Conclusion
References
A Saliency-Transformer Combined Knowledge Distillation Guided Network for Infrared Small Target Detection
1 Introduction
2 Related Work
3 The Proposed Method
3.1 The Overall Architecture of ST-KDNet
3.2 Transformer-Based Segmentation Branch
3.3 Saliency Detection Branch
3.4 Optimization of ST-KDNet
4 Experiments and Analysis
5 Conclusion
References
Research on Cloud-Edge Collaboration Based on Object Storage for Internet of Things
1 Introduction
2 Related Work
3 Cloud-Edge Collaboration Architecture Based on Object Storage for Internet of Things
3.1 Object Storage on Edge Side
4 Conclusion
References
Industrial Defect Detection System Based on Edge-Cloud Collaboration and Task Scheduling Technology
1 Introduction
2 Defect Detection System
3 Resource Perception and Task Scheduling
3.1 Adjust Production Line Task
3.2 Task Assignment on the Edge-Side
3.3 Joint Execution Strategy
4 Defect Detection of SMT Production Line
5 Conclusion
References
Actuator Grouping Optimization Method of Antenna Reflector Based-on Ant Colony Algorithm
1 Introduction
2 Reflector System Modeling
2.1 Description of the Grid Reflector
2.2 Finite Element Model
2.3 Out-of-Plane Displacement Description
3 Optimization Model and Processes
4 Results and Discussions
5 Conclusions
References
A Method to Measure the Quality of a Cloud Network Across Multiple Resource Pools
1 Introduction
2 Related Works
3 Method
3.1 User Experience Index System
3.2 Experience Quality Satisfaction Evaluation Method
3.3 Experience Quality Satisfaction Evaluation Method
4 Implementation Details
5 Conclusion and Future Work
References
A New Cloud Computing Deployment Model: Proprietary Cloud
1 Introduction
2 Related Works
3 Characteristics of Proprietary Cloud
3.1 Architecture
3.2 The Difference Between the Two Models
3.3 Common Deployment Policies
4 Conclusion and Future Work
References
Overview of the Development of Secure Access Service Edge
1 Introduction
1.1 Development Background
1.2 The Meaning of SASE
2 The Key Capabilities and Architectures
2.1 The Key Capability of SASE
2.2 The Deployment Architectures of SASE
3 The Future Trends of SASE in China
References
Optimization and Numerical Investigation of Micro-pin-Fin Structure on Heat Sink with Checkerboard Nozzles
1 Introduction
2 Geometric and Numerical Models
2.1 Geometric Model
2.2 Numerical Models
3 Results and Discussions
3.1 Heat Transfer and Fluid Flow
3.2 Optimization and Comparison of Variable Flow
4 Conclusion
References
A Method of Design and Evaluation of X-ray Communication in Black-Out Area
1 Introduction
2 Method
2.1 Main Scheme
2.2 Communication Performance Analysis
3 Conclusion
References
A Robust DOA Estimation Method Based on Auxiliary Sensors and Power Inversion
1 Introduction
2 Signal Model
3 Proposed Method
3.1 Using Auxiliary Sensors Eliminating the Effect of Sensor Coupling
3.2 Using Matrix Transformation Eliminating the Effect of Coherent Signals
3.3 Using the Power Inversion Method to Get the Spatial Spectral in Gaussian Colored Noise
4 Simulation Results
5 Conclusion
References
Diffusion Convolution Graph Attention Network for Spatial-Temporal Prediction
1 Introduction
2 Related Work
3 The DCGAT Model
3.1 Diffusion Bidirectional Random Walks
3.2 Graph Attention Network
3.3 Convolution-LSTM
3.4 Autoregressive
4 The DCGAT Model
4.1 Performance Comparison
5 Conclusion
References
Fast Estimation Technology of Orbit Information for Non-cooperative Space Targets Based on AREKF Filtering Theory
1 Introduction
2 Modeling and Analysis of Relative Motion of Non-cooperative Target
3 Design of AREKF Filtering Algorithm
4 Simulation Analysis
5 Conclusion
References
Information Technology Session 2
Design of Intelligent Power Supply and Distribution Test System for Small Satellite Based on Distributed Structure
1 Introduction
2 Design of Distributed Power Supply and Distribution Test System
2.1 Design of Main Control System
2.2 Design of Test Station System
3 Features and Benefits
4 Conclusion
References
Design of Small Satellite Power Supply Interface Test System Based on PXI Bus
1 Introduction
2 Function and Performance of Power Supply Interface Test System
3 Design of Power Supply Interface Test System
3.1 Hardware Design
3.2 Software Design
4 System Verification
5 Conclusion
References
Research on Method of On-Orbit Evolution for Aerospace Control Software
1 Introduction
2 Software Evolution Overview
3 Motivation
3.1 Uncertain Environmental Changes
3.2 Unknown On-Orbit Faults
3.3 Changeable Requirements
4 Evolution Process of Aerospace Control Software
5 Key Technologies
5.1 Architecture Modeling
5.2 Unified Description of Adaptive Elements
5.3 On-Orbit Evolution Knowledge Base Construction
5.4 On-Orbit Evolution Requirement Perception and Event Recognition Mechanism
5.5 Policy-Based Adaptive Decision Making
6 Conclusion
References
Research on Automatic Assembly Technology for Spacecraft System
1 Introduction
2 Establishment of the System
2.1 System Composition
2.2 Working Principle
3 Key Technology Applications
3.1 Visual Guidance and Recognition
3.2 Path Planning and Obstacle Avoidance
3.3 Product Grab
3.4 Flexible Assembly
4 Experimentation Validation
4.1 Experimentation Items
4.2 Experimentation Results
5 Conclusion
References
Design and Research of Multi-user Distributed Configuration Management Based on Zookeeper
1 Introduction
2 Zookeeper Introduction
3 Implementation Scheme
4 Test
5 Summary
References
A Complete Ensemble Local Mean Decomposition and Its Application in Doppler Radar Vital Signs Monitoring System
1 Introduction
2 Materials and Methods
2.1 Local Mean Decomposition
2.2 Ensemble Local Mean Decomposition
2.3 Complete Ensemble Local Mean Decomposition
3 Application to Doppler Radar Data
4 Conclusions
References
The Key Research of High Speed Camera Based on Multiple Core CMOS Sensors
1 Introduction
2 The Key Technology of High Speed Camera
3 The Process of High Speed Camera
4 Conclusion
References
Research on Cargo Level Optimization Based on Multi-objective Optimization Algorithm
1 Introduction
2 Literature Review
2.1 Multi-objective Optimization
2.2 Crossover Method
2.3 Mutation Mode
3 Cargo Space Planning Problem Model
3.1 Problem Description
3.2 Adaptation Function Design
3.3 Cargo Space Capacity Formula Design
3.4 Algorithm Flow
4 Experiment and Analysis
5 Conclusion
References
The Application of Single Pulse Output Module Integrated by FPGA in On-Board Electronic Products
1 Introduction
2 Common Problems in Practical Applications
3 The Concealment of the Problem
4 Design Optimization Scheme and Verification
5 Conclusion
References
Design of Steady Speed Control System for Space Borne Scanning Loads
1 Introduction
2 Design of System
3 Factors of Influencing Speed Ripple
3.1 Design of Motor with Minimized Torque Ripple
3.2 Analysis of Speed Quantization Error
4 Design of Algorithm
4.1 Modeling of PMSM Current Loop
4.2 Modeling of PMSM Speed Loop
5 Simulation and Test Results
6 Conclusion
References
Research on Slotting Optimization Based on MOEA/D
1 Introduction
2 Overview of Multi-objective Slotting Optimization Problem
3 Algorithm Description
3.1 The Algorithmic Framework of MOEA/D
3.2 Evolutionary Operator Selection
4 Instance Testing
4.1 Parameter Setting
4.2 Operator Selection
5 Conclusion
References
An Improved Density Peak Clustering Algorithm Based on Gravity Peak
1 Introduction
2 Related Works
3 Method
3.1 Discovery of Gravity Centers
3.2 Clustering Process of IDPC-GP
4 Experiments
4.1 Datasets
4.2 Parameter Test
4.3 Performance Comparison Test
5 Conclusion
References
A DPC Based Recommendation Algorithm for Internship Positions
1 Introduction
2 Related works
3 Method
4 Experiments
4.1 Datasets
4.2 Algorithm Implementation and Comparison Test
5 Conclusion
References
Optimal Method of Air Cargo Loading Under Multi-constraint Conditions
1 Introduction
2 Construction of Cargo Aircraft Loading Optimization Model
2.1 Problem Description
2.2 Model Construction
3 Algorithm Design
4 Example Analysis
5 Conclusion
References
Research on Software Synthesis Method for Spacecraft Control System
1 Introduction
2 Abstract Expression and Semantic Model for Software IP
2.1 Enlightenment of IC Design Thought for Software Reusable Assets
2.2 Model Definition of Software IP
3 Domain Oriented Software IP Construction
3.1 Data Sample Software IP: DataSample
3.2 Attitude Calculation Software IP: AttCalc
3.3 Control Calculation Software IP: CtrlCalc
3.4 Phase Transition Software IP: PhaseCvt
4 Domain Oriented Experimental Verification
5 Conclusion and Prospects
References
Prediction and Analysis of Infectious Disease Visit Data Based on DPC
1 Introduction
2 Related Work
3 Method
3.1 Data Preprocessing
3.2 Data Analysis and Visualization
4 Experiments
5 Conclusion
References
Research on the Method of Improving the Accuracy of MRP Calculation Through Big Data in Aerospace Enterprise
1 Background
2 Problems
3 Solution Ideas
4 Application Cases
5 Summary
References
Design and Application of Bad Block Management Strategy for On-Board Solid-State Memory
1 Introduction
2 On-Board Solid-State Memory Architecture
2.1 System Composition and Principle
2.2 Traditional Bad Block Management Strategy
3 Bad Block Detection Replacement Strategy
4 Implementation of Bad Block Management Strategy
4.1 Data Detection Module
4.2 Bad Block Marking Module
4.3 Telemetry Output Module
4.4 Bad Block Replacement Module
5 Comparison Between Traditional Method and Real-Time Detection Strategy
6 Conclusion
References
An Automated Scoring System for Photoshop Course in Secondary Vocational Colleges
1 Introduction
2 Proposed PsIQA Method
2.1 Edge Similarity
2.2 Color Similarity
2.3 Texture Similarity
3 Proposed Scoring System for Photoshop Course
3.1 Environment
3.2 System Function
4 Experimental Results
4.1 Evaluation Protocols
4.2 System Test
4.3 Computational Cost
5 Conclusion
References
Discussion on the Application of Contributing Student Pedagogy in Vocational Education “Computer Network Technology”
1 Introduction
2 Contributing Student Pedagogy
3 Current Situation of Vocational Education Teaching and Necessity of Shared Teaching Method
3.1 Vocational Education Reform and Transformation
3.2 The Advent of the Information Age and the Development of Smart Education
3.3 The Necessity of Shared Teaching Method
4 Cases of Teaching Design and Contributing Student Pedagogy in the Course “Computer Network Technology”
4.1 Case Introduction
4.2 Teaching Objectives and Requirements
4.3 Learning Target
4.4 Task Design
4.5 Evaluation Method
5 Conclusion and Future Outlook
References
Research on NURBS Interpolation Algorithm Based on Newton Iteration and Adams Equation
1 Introduction
2 NURBS Function and Interpolation Theory
2.1 The NURBS Formulation
2.2 Parametric Curve Interpolation Theory
3 NURBS Interpolation Algorithm
3.1 Velocity-Adaptive Module
3.2 Real-Time Interpolation
4 Simulation and Experimental Verification
5 Conclusion
References
Fake AP Detection Technology Based on Improved DBSCAN Algorithm
1 Introduction
2 CSI Phase Analysis
3 Fingerprint Feature Extraction
3.1 Preliminary Experiment and Analysis
3.2 Phase Unwrapping
3.3 Phase Filter
3.4 Fingerprint Feature Extraction
4 Algorithm Improvement
5 Simulation Experiment and Analysis
5.1 Experimental Setup
5.2 Simulation Analysis
6 Conclusion
References
Vehicles Location Estimation and Prediction in Integrated Sensing and Communications: Sensing Vehicles as Extended Targets
1 Introduction
2 System Model
3 Parameter Estimation with OTFS Modulation
3.1 General Process of Sensing-Assisted Predictive Beam Tracking
3.2 Sensing Parameters of the CR
3.3 Downlink Communication
4 Simulations
4.1 Simulation of Angle Estimation
4.2 Simulation of Location Estimation
4.3 Comparison of Communication Performance
5 Conclusion
References
Research on Vehicle and Cargo Matching of Electric Materials Based on Weed Optimization Algorithm
1 Introduction
2 Vehicle and Cargo Matching Mathematical Model
2.1 Vehicle and Cargo Matching Mechanism
2.2 Application of Weed Optimization Algorithm in Vehicle and Cargo Matching
3 Experimental Results and Discussion
4 Conclusion
References
Improved MSFLA-Based Scheduling Method of Electric Power Emergency Materials
1 Mathematical Model of Vehicle Scheduling Problem
1.1 Classical Vehicle Scheduling Problem
1.2 Vehicle Scheduling Problem with Time Window
2 Design of MSFLA
2.1 Algorithm Types and Selection
2.2 The Principle of Shuffled Frog Leaping Algorithm
2.3 Mathematical Model of SFLA
2.4 Application of MSFLA in Power Warehouse Cargo Transportation
3 Experimental Results and Analysis
4 Conclusions
References
A Survey of Class Activation Mapping for the Interpretability of Convolution Neural Networks
1 Introduction
2 Class Activation Mapping (CAM)
2.1 Pros and Cons of CAM
3 Grad-CAM
3.1 Pros and Cons of Grad-CAM
4 Related Works
4.1 Grad-CAM++ 
4.2 Score-CAM
4.3 LayerCAM
5 Discussion
5.1 Significance of CAM-Related Methods
5.2 Challenges Raised by CAM-Related Methods
5.3 Future Research
6 Conclusion
References
Robust Multi-person Reconstruction in Right Spatial Arrangement from In-the-Wild Scenes
1 Introduction
2 Body Parametric Representation
3 Human Reconstruction Framework
3.1 Pose-Depth Feature Extractor
3.2 SMPL Parametric Regressor
3.3 Parametric Refine Regressor
3.4 Loss Functions
3.5 Implementation Details
4 Experimental Results
4.1 Qualitative Comparisons
4.2 Quantitative Comparison
4.3 Ablation Experiment
5 Conclusions
References
Research on the Curriculum System of Electronic Information Engineering for the Certification of Engineering Education
1 Introduction
2 Introduction of Electronic Information Engineering
3 Design Requirements of Curriculum System
4 The Curriculum System
5 Conclusion
References
Multimedia and Communication Networks
An S Band Antenna Using Meta-surface for Satellite TT & C Test
1 Introduction
2 Design Structure and Mentality of the Antenna
3 The Characteristics and Applications of the Antenna
4 Conclusions
References
Design and Implementation of a Test and Verification System for Spaceborne Synthetic Aperture Radar
1 Introduction
2 System Design
3 Design of Echo Simulator
3.1 Hardware Design
3.2 Software Design
4 Design of Quick-Look System
4.1 Hardware Design
4.2 Software Design
5 Application
6 Conclusion
References
An Optimized Scheme for Telecommand Transmission
1 Introduction
2 Design of Telecommand Interface
2.1 Hardware Design
2.2 Software Design
3 Traditional Scheme for Telecommand Transmission
4 Optimized Scheme for Telecommand Transmission
5 Conclusion
References
Transmission Efficiency Improvement of Ka-Band VCM Satellite-to-Ground Data Transmission System of LEO Remote Sensing Satellite Based on DVB-S2X
1 Introduction
2 Simulation Parameters
2.1 Link Parameters
2.2 Modulation and Coding Mode Selection
3 Simulation and Evaluation
3.1 Definition of Transmission Efficiency Factor
3.2 Link Availability
3.3 Simulation Results of Transmission Efficiency Factor
3.4 Transmission Efficiency Improvement
4 Conclusions
References
Anti-interference Control for On-Orbit Servicing Spacecraft Formation Under Communication Resource Limitation
1 Introduction
2 System Model
3 Extended State Observer Design
4 Event-Triggered Adaptive Control Base on ESO
5 Simulation and Analysis
6 Conclusion
References
FPGA-Based High-Speed Remote Sensing Satellite Image Data Transmission System
1 Introduction
2 Design and Research of High-Speed Remote Sensing Satellite Image Data Transmission System Based on Fpga
2.1 Image Features
2.2 The Principle of Ship Target Detection
2.3 Selection Principle of Characteristic Parameters
2.4 Remote Sensing Image Compression Algorithm
3 Experimental Research on High Speed Remote Sensing Satellite Image Data Transmission System Based on FPGA
3.1 FPGA Design Ideas
3.2 FPGA Engineering Development Process
3.3 Cloud Detection Technology in Remote Sensing Cloud Images
4 Experiment Analysis of High Speed Remote Sensing Satellite Image Data Transmission System Based on FPGA
4.1 Usage of Device Resources
4.2 Comparison of Different Lossless Compression Algorithms for Binary Cloud Mask
5 Conclusions
References
Application of Frequency Domain Filtering in Edge Detection
1 Introduction
2 Method Principle
2.1 Image Frequency Domain
2.2 Fourier Transform
2.3 Canny Operator and Otsu Dual-Threshold Segmentation
3 Experiment and Analysis
3.1 The Effect of Frequency Domain Filtering on Edge Detection
3.2 Frequency Domain Filters for Specific Problems
4 Summary
References
Research on the Anti-interference of a Satellite Telecommand Interface
1 Introduction
2 Description of Satellite Interference
3 Satellite Telecommand Operation Process
4 Anti-interference Analysis of Telecommand Load
4.1 Anti-interference Analysis of Relay Telecommand Load
4.2 Anti-interference Analysis of Optocoupler Telecommand Load
4.3 Anti-interference Analysis of Other Telecommand Load
5 Conclusion
References
Intelligent Propagation Prediction Model for Wireless Radio Channel Based on CNN
1 Introduction
2 Feature Engineering of the Wireless Radio Channel
2.1 Empirical Models of Wireless Radio Channel
2.2 Features Design and Selection of Wireless Radio Channel
3 CNN-Based Wireless Intelligent Propagation Model
3.1 Data Preprocessing
3.2 CNN-Based Wireless Intelligent Propagation Model
4 Simulation Results
5 Conclusion
References
Research on Application Efficiency Analysis Method of Remote Sensing Satellite for Emergency
1 Introduction
2 Analysis on Influencing Factors of Remote Sensing Satellite Application Efficiency
2.1 Influencing Factors of Routine Application Efficiency
2.2 Influencing Factors of Emergency Application Efficiency
2.3 System Comprehensive Efficiency
3 Efficiency Analysis Method of Remote Sensing Satellite for Emergency Application
3.1 Quantification of Efficiency Influencing Factors
3.2 Confirmation of Efficiency Influencing Factors
4 Application Effectiveness Evaluation Test
4.1 Simulation Scene Settings
4.2 Satellite Application Efficiency Calculation
5 Conclusions
References
Research on Blind Restoration Algorithm of Motion Blurred Remote Sensing Image
1 Introduction
2 Image Deterioration Model
2.1 Description of Deterioration Model
2.2 Remote Sensing Image Motion Degradation Model
3 Remote Sensing Image Restoration Method
3.1 Estimation of Motion Angle Based on Radon Transform
3.2 Estimation of Blur Scale Based on Autocorrelation Function
3.3 Wiener Filter Restoration Model
4 Experimental Results
4.1 Comparative Experiment
4.2 Statistical Experiment
5 Conclusions
References
A Fast Algorithm of CU Block Division Based on Frequency Domain on VVC
1 Overview
2 Basic Theory
2.1 VVC Coding Framework
2.2 DCT Transformation
2.3 Main Idea
3 Main Work and Contribution
3.1 Determine the Eigenvalues of the Current Block
3.2 Determining the Coefficient R Threshold
3.3 Further Optimization
4 Experiment Result
5 Conclusion
References
Optimization of AVS3 Intra Derived-Tree Mode Algorithm Based on DCT Coefficients
1 Introduction
2 Proposed Algorithm
2.1 DCT and Its Coefficients
2.2 Proposed Algorithm
3 Experimental Results
4 Conclusion
References
Integrated Sensing and Communications with OTFS: Application in V2I Communications
1 Introduction
2 Related Work
2.1 Radar-Based Predictive Beamforming
2.2 Vision-Based Predictive Beamforming
2.3 OTFS-Based Predictive Beamforming
3 System Model
3.1 Signal Model
3.2 Communication Model
4 Parameter Estimation and Prediction
4.1 Parameter Estimation
4.2 Prediction of Location, Distance and Angle
5 Simulations
5.1 Performance of Parameters Estimation
5.2 Performance of Downlink Communication
6 Conclusion
References
Space Technology Session 1
Fault Analysis and Evaluation of Control Moment Gyroscope on Orbit
1 Preface
2 Fault Analysis on Orbit
2.1 Abnormal Increasing of Local Impedance in Low Speed Frame Conductive Ring
2.2 Friction Moment of Bearing Increases Due to Wear of High Speed Bearing Cage
3 Evaluation Method for Performance of CMG
4 Conclusion
References
Precision Orbit Determination for LEO Based on BDS Navigation Data
1 Introduction
2 Dynamic Compensation Orbit Determination
3 Perturbation Model
4 Data Analysis
5 Summary
References
Research and Practice of Electrostatic Grounding Technology of Aerospace Electronic Products Based on Intelligent Active Protection Concept
1 Hazards and Loss of ESD to Electronic Products
2 Development Overview
3 Key Points and Difficulties
3.1 Identification of Protection Requirements of Static-Sensitive Components of Xi’an Branch
3.2 Identification of Protection of Electrostatic Sensitive Electronic Products
3.3 Identification of Static Electricity Prevention and Control of Insulated Articles and Isolated Conductors
4 How to Establish Electrostatic Grounding Measures for Aerospace Electronic Products Based on the Concept of Intelligent Active Protection
4.1 Innovate the Anti-static Grounding Monitoring Technology to Improve
4.2 Use Anti-static Door Machine Chain Detection Technical Measures to Strengthen the Active Control Ability of Static Electricity Protection
5 Actual Effect
5.1 Cure and Form a Complete Anti-static Grounding Management System
5.2 Help to Build a National “Five-Star” Demonstration Site, and Improve the Active Control Ability of EPA
5.3 The Quality Problem of Static Damage Products in Xi’an Branch Has Decreased Significantly
6 Conclusion
References
Investigation on Feasibility of Covert TT&C Scheme Using Satellite Payload Channel
1 Introduction
2 Covert TT&C Scheme
2.1 Interference of Repeater Signal on TT&C Signal
2.2 Interference of TT&C Signal on Repeater Signal
3 Model Validation and Discussion
4 Conclusion
References
A Satellite Imaging Stability Monitoring Method Based on Lunar Calibration Technology
1 Introduction
2 Key Technology of Lunar Calibration
2.1 The Imaging Window of Lunar Calibration
2.2 Satellite Attitude for Lunar Calibration
2.3 Imaging Parameters of the Camera
3 Imaging Stability Monitoring Experiment
3.1 Experimental Condition
3.2 Lunar Calibration
3.3 Imaging Stability Monitoring
4 Conclusion
References
Design and Verification of mK Temperature Fluctuation Control for Gravity Gradiometer
1 Introduction
2 The Gradiometer
3 Methods of Thermal Control Design for the Gradiometer
3.1 Build a Component-Level Resistance-Capacitance (RC) Filtering Network
3.2 System-Level Thermal Insulation Design
3.3 High-Precision Multi-stage Active Thermal Control Method
3.4 Control Method of Cable Heat Loss
4 Thermal Analysis
4.1 The Thermal Mathematical Model and Meshing
4.2 Thermal Performance
5 Thermal Balance Test
5.1 Overview
5.2 Analysis of Test Results
6 Conclusions
References
A Perigee Orbit Change Method for Geostationary Satellite
1 Introduction
2 Geostationary Orbit Satellite Working Condition Analysis
2.1 Satellite Profile
2.2 Routine Working Process of Apogee Orbit Change
3 Perigee Orbit Change Control Method
4 Perigee Orbit Change Effect
5 Conclusion
References
Optimal Design of Power Drive Unit Layout Based on Improved Discrete Particle Swarm Optimization
1 Introduction
2 Establishment of PDU Layout Optimization Model
3 Solution of PDU Optimal Layout Model
3.1 Description of Discrete Particle Swarm Optimization
3.2 Algorithm Improvement Strategies
4 Application Case Analysis
5 Conclusion
References
Preliminary Discussion on the Application of Digital Thread in the Model-Based Spacecraft Development
1 Introduction
2 Analysis of the Model-Based Spacecraft Development Mode
3 The Digital Thread and Its Construction Method
3.1 Overview of the Digital Thread
3.2 The Construction Method of Spacecraft Digital Thread
4 Realization and Application of Digital Thread
4.1 Realization of Digital Thread in the PLM System
4.2 The Construction Practice of Spacecraft Digital Thread
5 Conclusion
References
Study on the Construction Method of Spacecraft System Model Based on Meta-model
1 Introduction
2 The Construction Method of Spacecraft Domain Meta-model
2.1 Meta Object Facility
2.2 Spacecraft Ontology Concept Modeling
2.3 The Customization of Domain Metal-Model on Modeling Tools
3 The Realization of Spacecraft System Model Based on Domain Meta-model
3.1 Ontology Modeling
3.2 View Template Modeling
4 Case Study
5 Conclusion
References
The Design and Implementation of Magnetic Control of Ultra Quiet Control System in Gravity Satellite
1 Introduction
2 Principle of Designing Magnetic Control of Ultra Quiet Control System with High Precision
3 Measurement Technology of Magnetometer with High Precision
3.1 Temperature Compensation Technology of Magnetometer
3.2 Whole Satellite Compensation Technology of Magnetometer
4 Magnetic Torquer Control Technology with High Precision
4.1 Induced Magnetic Moment Control of Magnetic Torquer Body
4.2 Driving Circuit Technology of Magnetic Torquer with High Precision
5 Flight Results of Magnetic Control in Orbit
6 Conclusion
References
Intercomparison of On-Orbit Consistency of Radiometric Calibration Between Two Sensor Calibration Spectrometers of HY Satellite
1 Introduction
2 Onboard Calibration of HY-1C(1D)/SCS
2.1 Calibration Principle of SCS
2.2 Cross Calibration Method Based on Spectral Reconstruction
3 Experiment and Analysis
3.1 SCS and MODIS
3.2 HY-1C/SCS and HY-1D/SCS
4 Conclusion
References
Loose Formation Keeping Control for Low-Earth Orbit Satellite Cluster Considering the Interaction Between Cluster Satellites
1 Introduction
2 Relative Motion Description of Satellite Cluster
3 Relative Distance Maintenance in the Along-Track Direction
4 Collision Avoidance in the xz Plane
5 Relative Motion Amplitude Suppression in the Cross-Track Direction
6 Simulation Results
7 Conclusions
References
Research on Data Fusion Architecture of GNC Subsystem of China Space Station
1 Introduction
2 Data Fusion Related Research
3 Data Fusion Architecture
3.1 Data Pool
3.2 Communication Layer Data Fusion
3.3 Resolution Layer Data Fusion
3.4 Algorithm Layer Data Fusion
4 Application Validation
5 Conclusion
References
Application in Emergency Tasks Schedule with the GFDM-1 Satellite
1 Introduction
2 Emergency Task Schedule Process Design and Mode Selection
3 Test Results and Analysis
3.1 High Priority Tasks Replace Low Priority Tasks
3.2 Effect of Avoiding Cloud Cover by Multi-angle Imaging
3.3 Area Extraction Data Processing On-Board to Accelerate Data Delivery to Users
3.4 Data Transmission to Relay Satellites
4 Conclusion
References
Emergency Scheduling Process Design for ZY303 Satellite
1 Introduction
2 Problem Formulation and Research Background
3 Mission Scheduling Model and Task Insertion Strategy
3.1 Emergency Need Analysis Module
3.2 Emergency Scheduling Strategy Design
3.3 Mission Uploading Module
4 Conclusion
References
Research on the Application of TDRSS for Manned Spacecraft
1 Introduction
2 Characteristics of Manned Spacecraft TT&C
2.1 High Reliability
2.2 Multi-target TT&C
2.3 Image and Voice Communication
3 Characteristics of TDRSS
3.1 High TT&C Coverage
3.2 High Data Transfer Rate
3.3 Diverse Link
3.4 Outstanding Economic Benefits
4 Applications of TDRSS
4.1 Multi-target TT&C
4.2 Key Event Support
4.3 Capability Improvement and Optimization
5 Conclusion
References
Modeling and Analysis of Satellite with a Large Inertia Rotating Load
1 Introduction
2 Model Establishment Using SimMechanics
3 Simulation Analysis
3.1 Simulation Setup
3.2 Results and Analysis
4 Conclusion
References
Research on Satellite-to-Ground Integration Joint Mission Planning System for Mega-constellation Multi-user Remote Sensing Information Resource Guarantee
1 Introduction
2 Present Situation and Deficiencies of Earth Remote Sensing Observation Task
2.1 Planning Status
2.2 Deficiencies
3 Design of Satellite-to-Ground Integration Joint Mission Planning System for Multi-user Remote Sensing Information Resource Guarantee
3.1 General Train of Thought
3.2 Construction Goal
3.3 System Composition and Function Requirements
4 Running Scenario
5 Conclusion
References
Analysis of the Influence of Satellite Drift Angle Correction Datum on Imaging Quality
1 Introduction
2 The Basis for Selecting the Drift Angle Correction Datum and Influence
3 Simulation
3.1 Orbit and Payload Parameters
3.2 Drift Angle Residuals for Different Correction Datum
3.3 Effect of Drift Angle Residuals on Payload Image Quality
4 Conclusion
References
Application and Process of Inclination Adjustment of Satellites in Sun Synchronous Orbit
1 Preface
2 Variation Principle and Influencing Factors of Local Time
3 The Principle of Orbit Inclination Adjustment
4 Choice of Inclination Adjustment Quantity
5 Inclination Adjustment Process
6 Effect Tracking
7 Concluding Remarks
References
Analysis Method for Isolation Index of Transceiver Link of Space Ground TT&C Equipment
1 Introduction
2 Operating Mechanism of Transceiver Link
3 Analysis of Transceiver Interference
3.1 Influence of Baseband Generated Signal on Receiving Link
3.2 Influence of High Power Transmitting Signal on Receiving Link
3.3 Influence of Receiving Resistance Filter and Duplexer on Receiving Link
3.4 Influence of High Power Signal Transmission on Receiving Link
3.5 Influence of Space Radiation on Receiving Link
4 Requirements and Design Method of Transceiver Isolation Index
4.1 The Standard Mode System
4.2 The Spread Spectrum Mode System
5 Test and Validation
6 Conclusions
References
Satellite Momentum Wheel Life Prediction Method Based on PSO-LSSVM
1 Introduction
2 LSSVM Algorithm
3 Pso-Svr Line Model
4 Numerical Sample
4.1 Data Source
4.2 Model Evaluation Index and Model Parameter
4.3 Fitness and Prediction Performance Evaluation
4.4 Reliability Function of Momentum Wheel
5 Conclusion
References
Design of Equivalent Simulation Test System for Universal Power Supply and Distribution of Space Station Multi-aircraft
1 Introduction
2 Equivalent Analog System Design
2.1 Modeling of General Power Supply and Distribution Equivalent Simulation System
2.2 Equivalent System Hardware Design
2.3 Software Design
3 System Application and Verification
4 Conclusion
References
Design and Verification of a Geographic Information Prediction Scheme for Autonomous Mission Planning of Satellites
1 Introduction
2 Geographic Information Prediction Algorithm
2.1 Orbit Prediction
2.2 Prediction of Sunshine Area/Shadow Area
2.3 Prediction of Domestic Area/Overseas Area and Land/Ocean
3 System Verification
4 Conclusion
References
Space Technology Session 2
Bit Synchronization Verification Strategy for High Orbit Navigation Receiver
1 Overview
2 High Orbit Bit Synchronization Method
2.1 Common Bit Synchronization Method
2.2 High-Orbit GPS Bit Synchronization Method
2.3 High-Orbit Beidou Bit Synchronization Method
3 Bit Synchronization Check Method
3.1 Beidou Bit Synchronization Verification Method Based on NH Code Information
3.2 GPS and BD Universal Bit Synchronization Detection Mechanism Based on Converted Time
4 Test Verification
5 Conclusion
References
Real Time Dynamic Adjustment Calculation Method of Camera Integration Time Based on Spaceborne Digital Elevation Model
1 Introduction
2 Dynamic Adjustment Method of Camera Integration Time
2.1 Dynamic Acquisition of Real-Time Point High-Precision Elevation Data
2.2 Projection of Angular Velocity in Field of View Coordinate System
2.3 Calculate the Ground Speed and Oblique Distance of the Three Fields of View of the Camera
2.4 Calculate the Integration Time of Three Cameras
3 On Orbit Test Results
4 Conclusion
References
A Time Sequence Design for Improving the Output Frequency and Accuracy of Star Sensor Under High Dynamic Conditions
1 Introduction
2 The Problems We Face
2.1 How to Accurate Prediction of the Exposure Chart?
2.2 How to Increase the Output Frequency of the Attitude Information?
3 Our Time Sequence Design to Solve the Problems
3.1 Accurate Prediction of the Exposure Star Map
3.2 Parallel Processing of Star Map Exposure and Calculations
4 Conclusion
References
Research on the Measurement Method of Inter-satellite Clock Difference and Distance of Formation Satellites
1 Introduction
2 Measurement Principle of Inter-satellite Clock Difference and Distance
3 Implementation of Measurement Method
3.1 Measurement System Scheme
3.2 Realization of System Measurement Function
3.3 Pairing of Measured Values in Two-Way Measurement
3.4 Mismatch Between Transmission Delay and Clock Difference
4 Method Validation
5 Concluding Remarks
References
Research on Asteroid Detection Radar Based on High Precision Signal Delay Estimation
1 Introduction
2 Introduction and Modeling Analysis of Dual Base Station Radar System
2.1 Dual Base Station Radar System
2.2 System Modeling
3 Time Delay–Spectrum Estimation
3.1 Mathematical Model of Multichannel Correlation Technology
3.2 Specific Implementation of Delay Map Estimation
4 Conclusion
References
Review of Micro-vibration Modeling, Suppression, and Measurement for Spacecraft with High-Precision
1 Introduction
2 Integrated Modeling Method of Micro-vibration
3 Suppression of Micro-vibration
4 On-orbit Measurement of Micro-vibration
4.1 Line Vibration Measurement
4.2 Angle Vibration Measurement
5 Conclusions
References
Satellite Gravity Gradient Measurement Technology Based on Superconducting Suspension
1 Introduction
2 Principle of Satellite Gravity Gradient Measurement
2.1 Satellite Gravity Gradient Measurement System
2.2 Gravity Field Model
2.3 Measurement Model of Gravity Gradiometer
3 Satellite Superconducting Gravity Gradient Measurement Technology
3.1 Basic Principle of Superconducting Gravity Gradiometer
3.2 Key Technology of Superconducting Gravity Gradient Load
3.3 Key Technologies of Superconducting Gravity Gradient Satellite Platform
4 Conclusions
References
The Mathematic Analytical Results of Self Excited Flyback Converter with Double Switches
1 Discussion Question
2 Operating Equation During Ton Period
3 Operating Equation During Toff Period
3.1 Operating Equation During the Period of T1
3.2 Operating Equation During the Period of T2
4 The Energy Transmission Coefficient η
5 The Expression of the Operating Period T
6 The Representation of the Maximum Magnetic Density B
7 The Representation of the Ratio of Duty Time D
8 Voltage Waves of Main Switches K1 and K2
9 Conclusions
References
Anomaly Interpretation Method Based on Data Mining for Remote Sensing Satellite Payload
1 Introduction
2 Comprehensive Time Series Analysis of Load Data
2.1 System Hardware Design and Implementation
2.2 Load Integrated Data Code
3 Research on Anomaly Detection Method Based on Adaptive Threshold
3.1 Load Characteristic Data Creation
3.2 Load Data Prediction and Residual Calculation
3.3 Load Residual Smoothing
3.4 Adaptive Threshold Design
4 Image Anomaly Detection Test Results
5 Conclusion
References
A Study on Determination Method of Safety Band in the Translational Closing Section of Rendezvous and Docking
1 Introduction
2 The Description of Dynamics Equation and Problems
3 Design of Control Method and Collision Avoidance Strategy Based on Safety Band
3.1 Design of Nominal Trajectory of Translational Converging Section
3.2 Design of Control Law and Collision Avoidance Algorithm
4 Simulation Analysis
4.1 Control Effect
4.2 Analysis of Collision Avoidance Examples
4.3 Simulation of Collision Avoidance Calculation Examples
5 Conclusion
References
Prospect Analysis of Satellite Application of Long-Distance Wireless Energy Transmission System
1 Summary
2 Wireless Energy Transmission System
2.1 Wireless Laser Energy Transmission
2.2 Wireless Transmission of Microwave Energy
2.3 Quasistatic Cavity Resonance Wireless Energy Transmission
2.4 Summary
3 Inspiration
References
Design of Multi-dimensional Control and Multi-subsystem Cooperative Operation Algorithm for Control Subsystem
1 Introduction
2 Autonomous Control Method
2.1 A Subsection Sampl Maneuver Attitude Control Method with Single-Gimbal Control Moment Gyros
2.2 Control Scheme of Magnetorquer
2.3 Control Scheme of Propulsion Subsystem
2.4 Solar Panels Control Scheme Combined with Energy Subsystem
3 Design of Multi-dimensional Control and Multi-subsystem Cooperative Operation Algorithm
3.1 Dimension 1 - Verification Test
3.2 Dimension 2 - Onboard
3.3 Dimension 3 - Failure of Actuator Occurs
3.4 Application
4 Conclusion
References
Research on Attitude Determination of Spacecraft Two-Stage Composite Control System
1 Reference
2 Attitude Determination Analysis
2.1 Attitude Matrix Calculation of Secondary Control Platform
2.2 Attitude Calculation of Star Sensor Measurement
2.3 Calculation of Gyro Inertial Angular Velocity
3 Conclusion
References
Design of Dual Robot Collaborative Assembly Scheme for Aerospace Products
1 Introduction
2 The Current Status of Robotics in the Aerospace Field
3 Overall Solution Design
3.1 System Composition
3.2 Workflow
4 The Main Parts of the Program Design
4.1 Flexible Automatic Gripping of Parts
4.2 Automatic Installation of Fasteners
4.3 Visual Positioning of Mounted Parts
4.4 Visual Positioning of Mounting Holes
5 Conclusion
References
Study on the Application of Robot-Assisted Assembly in Satellite Board Assembly
1 Introduction
2 Robot-Assisted Assembly System Solution Design
2.1 Design Ideas
2.2 Overall Solution Design
3 Key Technologies
3.1 Basic Motion Path Algorithm
3.2 Robot Motion Forward and Reverse Solutions
4 Conclusion
References
3D Model-Based Design Method for Complex Cable Network Three-Dimensional Marking of Spacecraft
1 Introduction
2 Communication Platform Cable Network General Assembly Status
3 Cable Network Modeling and Marking Methods
3.1 Cable Network Model Requirements
3.2 Cable Network Model Marking Requirements
3.3 Cable Network Logo Production Requirements
4 Conclusion
References
Study on the Optimization of Temperature Measurement Points for Small Satellite
1 Introduction
2 Temperature Measurement Point Setting Classification and Optimization Method Research
2.1 Classification of Small Satellite Temperature Measurement Point Settings
2.2 Small Satellite Temperature Measurement Point Optimization Method
3 Small Satellite Temperature Measurement Distribution Analysis and Optimization Design
3.1 Thermal Control Subsystem
3.2 Attitude Control Subsystem
3.3 Power Subsystem
3.4 Overall Circuit Subsystem
3.5 Star Service Subsystem
3.6 Measurement and Control Subsystem
3.7 Digital Transmission Subsystem
3.8 Load Subsystem
4 Optimization Results of Small Satellite Temperature Measurement Deployment
5 Conclusion
References
Design of Model-Based Spacecraft Flight Control Support System
1 Introduction
2 Task Analysis
2.1 Requirements Analysis of Flight Control Support System
2.2 Kirin Operating System
2.3 Extensible Markup Language (XML)
3 Flight Control Support System Design
3.1 System Architecture
3.2 Telemetry Data Processing
3.3 Telemetry Display Template
3.4 Application Process
4 Application
5 Conclusion
References
Big Data Workshop Session 1
Research and Application of 5G Massive MIMO Antenna Weight Intelligent Optimization Based on 4G/5G Coordination
1 Introduction
2 Weight Optimization Principle
2.1 Beamforming
2.2 MM Weight Optimization Process
3 4G/5G Coordination Algorithm
3.1 High Backflow Cell Pair and Area Identification
3.2 ACO Weight Iteration Algorithm
3.3 Objective Function Prediction
3.4 4G/5G Coordination Algorithm Process
4 Application Effect
5 Conclusion
References
A Method of 5G Core Network Service Fault Diagnosis
1 Introduction
2 5G Service Procedures
2.1 The Registration Procedures
2.2 System Interworking Procedures with EPC
3 5GC Data Service Fault Diagnosis Method
4 Experiment and Performance
5 Summary and Prospect
References
Research on User Behavior Credibility Evaluation Model in Trusted Network
1 Introduction
2 Trusted Network and Measurement of Network Trustworthiness
2.1 Trusted Network
2.2 Measurement of Network Trustworthiness
3 Evaluation Model of Decision Credibility Based on Multiple User Behaviors
3.1 Direct Credibility
3.2 Indirect Credibility
3.3 Behavior Activity Considering Behavior Penalty Factor
3.4 Comprehensive Reliability
3.5 Security Trustworthiness Level and Service Level Mapping Mechanism
4 Experimental Simulation and Performance Analysis
4.1 Experimental Simulation
4.2 Performance Analysis of Experimental Results
5 Conclusion
References
Research and Application of Key Technologies of Intelligent Manufacturing Based on 5G Vehicle
1 Introduction
2 Research and Application of Key Technologies of Intelligent Manufacturing
2.1 IIOT for Smart Car Manufacturing
2.2 Application of Artificial Intelligence in Automobile Manufacturing
2.3 Industrial Computing Network Platform Integrated with Big Data Cloud Technology
3 Classic Case Analysis of Automobile Intelligent Manufacturing
3.1 Background
3.2 Automated Factories Based on Machine Learning
3.3 Integrated Automation (TIA) Industrial Internet Portal
3.4 Intelligent Manufacturing Application Based on 5G
3.5 Experimental Process
4 Concluding Remarks
References
Research on Key Technologies of 5GC Network Intelligent Operation Under the Background of Digital Transformation
1 Introduction
2 Management and Orchestration (MANO) Technology
2.1 MANO System Architecture
2.2 MANO Key Technology
3 Enhanced Operation Systems
3.1 Alarm Association and Root Cause Analysis System
3.2 Cloudification Network Traffic Intelligent Monitoring System
4 Conclusion
References
Research on Location Algorithm of Mobile Network Based on Hidden Markov Model
1 Introduce
2 Problem Description
3 Fingerprint Location Algorithm
3.1 Observation Probability Model
3.2 Transition Probability Model
3.3 Viterbi Algorithm Trajectory Matching
4 Road User Fingerprint Matching Map Based on HMM
4.1 MR Number Backfill
4.2 MR Fingerprint Positioning
4.3 Electronic Map Acquisition and Analysis
4.4 Acquisition of Internet Travel Data
5 Implementation Effect
6 Conclusion
References
Research on the Development of 5G Messaging Service for Operators
1 Introduction
2 5G Messaging Overview
3 5G Messaging System Architecture
3.1 5G Messaging System
3.2 Industry Gateway
3.3 Interworking Gateway
3.4 Integrated SMS Center
4 Business Model of 5G Messaging
4.1 Characteristics of MaaP Business Model
4.2 Business Value of 5G Messaging
4.3 Application Scenarios of 5G Messaging
5 Conclusion
References
Investigation of 5G Terminal Users with Dual SIMs
1 Introduction
2 5G Dual SIM Terminal User Identification Method
3 The Other IMEI Identification Method of 5G Dual SIM Terminal User
4 Big Data Analyzation Results
4.1 5G Terminal User Single and Dual SIM Identification
4.2 The Other IMEI Identification of Current 5G Terminal User
4.3 Dual IMEIs in the Same 5G Terminal Identification
5 Conclusion
References
Research on Deep Packet Inspection for Driving Digital Operation
1 Background and Problem Description
2 Solution Description
2.1 Main Goals of the Paper
2.2 Research Methods
3 Results and Conclusions
3.1 Main Innovations
3.2 Main Achievements
4 Suggestions on Digital Transformation
5 Conclusion
References
Research on the Operation Model of Operator Computing Products
1 Economic Development and National Policies Accelerate the Construction of Computing Power Network
2 The Process and Challenges of Computing Power Network
2.1 Generation of Computing Power Network
2.2 Development and Evolution Route of Computing Power Network
3 Role Positioning and Strategic Layout of Telecom Operators in Computing Power Network
4 Typical Application Scenarios of Computing Power Network Empowerment
4.1 Personal Life
4.2 Enterprise Customers
4.3 Social and People’s Livelihood
5 Operational Suggestions of Computing Power Products to Operators
6 Conclusion
References
Big Data Processing Mode Based on Cross Data Source SQL Engine
1 System Architecture Design and Principle
2 Supplement to SQL Syntax
3 Critical Technology
4 Spread Value
5 Ending Words
References
Online Machine Learning-Based Quality Difference Identification and Prediction Prevention for Broadband Users
1 Introduction
2 Intelligent Analysis System Architecture
3 Key Techniques
4 Digital Programme Implementation Process
4.1 Analysis of the Quality of Business Applications in the Three Regions of the Cloud
4.2 Analysis of the Quality of the PON Network in the Pipe Section
4.3 Analysis of Users with Poor Service Quality on the Device Side
4.4 Potential Mining Analysis
5 Result
6 Conclusion
References
Achieving Privacy-Preserving Diagnosis with Federated Learning in LEO Satellite Constellation
1 Introduction
2 System Model, Security Requirements and Goals
2.1 System Model
2.2 Security Requirements
3 Proposed Privacy-Preserving Data-Driven Diagnosis Framework with Federated Learning
3.1 System Initialization
3.2 Secure Model Protection
3.3 Privacy-Preserving Model Aggregation
4 Security Analysis
5 Performance Evaluations
5.1 Experiment Setup
5.2 Computational Complexity
6 Conclusions
References
Cellular Network Coverage Hole Detection and Diagnosis Method Using WaveCluster
1 Introduction
2 Problem Formation
3 Research Theory and Method
3.1 WaveCluster Algorithm Introduction
3.2 Wavelet Transform Introduction
3.3 Model Construction
3.4 Model Evaluation Methods
4 Model Experiment and Result Analysis
4.1 Data Preparation
4.2 Process of Clustering Model
4.3 Analysis of Clustering Results
5 Conclusion
References
A Novel Method of Trace Message Decoding and the Performance Analysis
1 Introduction
2 Elastic Decoding System
2.1 Overview
2.2 Configuration Data
2.3 Configuration Data Management Module
2.4 Configuration Data Register Module
2.5 Original Data Input Module
2.6 Decoding Scheduling Module
2.7 Decoders Module
2.8 Decoding Output Module
3 Detailed Procedures
3.1 Configuration Management Procedure
3.2 Decoding Output Procedure
4 Performance Analysis
4.1 Preconditions
5 Performance Analysis
6 Conclusion
References
Study on Cost Difference Between Peak-Valley Pricing and Flat Pricing
1 Introduction
2 Analysis of Electrical Equipment in Base Station
2.1 Air Conditioner
2.2 Transmission Equipment Such as PTN
2.3 BBU
2.4 RRU and AAU
3 Economic Analysis
3.1 Calculation of Electricity Price
3.2 Electricity Price of the First Type
3.3 Electricity Price of the Second Type
3.4 Electricity Price of the Third Type
3.5 Electricity Price of All Types Together
4 Results Analysis and Verification
5 Conclusion
References
Three-Dimensional Integrated Base Station Rental Fee Benchmark Evaluation
1 Introduction
2 Analysis of Lease Fee Data
2.1 Area Data of Site
2.2 Annual Rent Fee of Site
2.3 House Rental Fee
2.4 Tower Site Fee
3 Design of 3D Integrated Benchmarking Algorithm
3.1 Traverse Reference Points
3.2 The Calculation Method of Benchmark Value of Each Dimension
3.3 The Three Dimensiona Integrated Benchmark and Calculation of Exceedance Rate
4 Results Analysis and Verification
4.1 Verification of Sites Where the Rental Fee Seriously Exceeds the Standard
4.2 Analysis of Sites
5 Conclusion
References
The Energy Saving Measurement System and Method of Main Base Station Communication Equipment
1 Overview
2 Introduction to the Energy Saving Calculation Models of Equipment
2.1 The Definition of Energy Saving Measurement
2.2 Introduction to the Model Usage Algorithm
2.3 New Energy Saving Formula
3 Application of the Energy Saving Calculation Model of Equipment
3.1 Model Index Selection
3.2 Model Validation
4 Calculation Results of Energy Saving of Equipment
5 Conclusion
References
Energy Saving Technology and Parameter Research of 5G Terminal
1 Introduction
2 Terminal Power Consumption Related Technologies
2.1 DRX
2.2 BWP
2.3 Uplink Intelligent Pre-scheduling
3 Parameter Configuration Suggestions
3.1 DRX Parameters
3.2 BWP Parameters
3.3 Uplink Intelligent Pre-scheduling Parameters
4 Energy-Saving Technology Verification
5 Conclusion
References
Research on Energy Consumption Modelling of 5G Wireless Communication Main Equipment Based on GBRT Algorithm
1 Introduction
2 5G Base Station Energy Consumption Analysis
3 RRU/AAU Energy Consumption Modeling Data Collection and Analysis
3.1 The Equipment Model of RRU/AAU Selection Analysis
3.2 Data Selection and Analysis of Energy Consumption Modeling Indicators
4 Analysis of RRU/AAU Energy Consumption Modeling Scheme
5 Analysis of RRU/AAU Energy Consumption Modeling Results
5.1 Analysis of RRU Energy Consumption Modeling Results
5.2 Analysis of AAU Energy Consumption Modeling Results
6 Conclusion
References
Research of Voice Speech Quality Evaluation Based on Trace Data
1 Introduction
2 Existing Voice Quality Evaluation Methods
2.1 Mainstream Evaluation Methods
2.2 Operator's Practice
3 Collection and Application of Trace Data
3.1 Subscription of Trace Data
3.2 Collection of Trace Data
3.3 Construction of Intermediate Tables
3.4 Voice Quality Evaluation Based on Trace Data
4 Practical Application Comparison Results
5 Conclusion
References
Analysis of the Impact of COVID-19 on the Development of National ICT Industry
1 Introduction
2 Theoretical Reference
2.1 Pearson Correlation Coefficient Model
2.2 Simple Linear Regression Model
3 COVID-19 and Telecommunication Business
4 COVID-19 and Mobile Internet Business
5 COVID-19 and Internet Business, Software Business
6 Conclusion
References
Research on Low Latency and High Reliability Wireless Technologies and Solutions
1 Introduction
2 Research on URLLC Wireless Key Technology Research
2.1 Low Latency Technology
2.2 High Reliability Technology
3 Research on URLLC Scenario Grading
3.1 Introduction to the URLLC Scenario in the 3GPP Protocol
3.2 SLA Requirements for URLLC Scenarios
3.3 URLLC Scenario Grading
4 Wireless Network Solutions Under Different URLLC Grading
4.1 Solutions for Grading 1
4.2 Solutions for Grading 2
4.3 Solutions for Grading 3
4.4 Solutions for Grading 4
5 Conclusions
References
Resource Multiplexing Schemes of URLLC and eMBB Under Multi-service Coexistence Scenario Based on Management Aspects
1 Introduction
2 Concepts and Features Defined for URLLC
2.1 Concepts Defined for URLLC
2.2 Features Defined for URLLC
2.3 Difference in Requirements for eMBB and URLLC
3 Relation of eMBB and URLLC in Network Deployment
4 Resource Multiplexing Schemes and Technologies
4.1 Parameter Configuration for QoS Flow
4.2 PRB Resource Reservation: Semi-persistent Resource Multiplexing
4.3 Preemption Mechanism: Dynamic Resource Multiplexing
5 Conclusions
References
Big Data Workshop Session 2
5G Construction Efficiency Enhancement Based on LSTM Algorithm
1 Introduction
2 Introduction of 5G Construction Efficiency Evaluation
2.1 5G Resident Ratio Evaluation Method
2.2 5G Coverage Efficiency Evaluation Method
2.3 5G Capability Efficiency Evaluation Method Based on 5G High Backflow
2.4 5G Traffic Sharing Efficiency Evaluation Method Based on 5G Neighbor Cell Load Sharing
3 Implementation of 5G Construction Efficiency Prediction Based on LSTM Algorithm
3.1 Scheme of Intelligent 5G Construction Efficiency Enhancement
3.2 Prediction Model Based on LSTM Algorithm
4 Verification and Application Effect Assessment
5 Conclusions
References
Business Circle Attraction Based on DPI
1 Introduction
2 The Process of Business Circle Attraction
2.1 Merchant Feature Extraction
2.2 User Network Behavior and Moving Track Capture
2.3 User Profile Construction
2.4 Merchant Recommendation
2.5 Implementation of Attraction
2.6 Attraction Effect Evaluation
3 Conclusion
References
A Novel Method of Obtaining Location Information
1 Introduction
2 Location Service Based on Location Information Prediction
2.1 Overview
2.2 Overall Procedure to Positioning the Target Terminal
2.3 Analysis of the Solution
3 Current Location Prediction for One Surrounding Device
3.1 Trajectory Fitting of Peripheral Devices
3.2 Eigenvectors Updating for Each Surrounding Device
4 Detailed Procedure for Location Request
5 Conclusion
References
Research on Network Quality Intelligent Monitoring Technology Based on Prophet Model
1 Introduction
2 Design Ideas
2.1 The Principle of Prophet Model
2.2 Prediction Method of Poor Quality Cells Based on Prophet Model
3 Application Implementation
3.1 Implementation of Key Algorithm
3.2 Application Examples
4 Conclusions
References
Uplink Tx Switching Application Optimization Based on XGBoost Algorithm
1 Introduction
2 Principles of Uplink Tx Switching Technology
2.1 Principles and Implementation of Uplink Tx Switching Technology
2.2 Uplink Tx Switching Technology Based on SUL
2.3 Uplink Tx Switching Technology Based on UL CA
2.4 Comparison of Two Technical Routes of Uplink Tx Switching
3 Adaptive Turn-On of Uplink Tx Switching Based on XGBoost Algorithm
3.1 An Adaptive Scheme for Uplink Tx Switching Based on Uplink Load
3.2 Introduction of XGBoost Algorithm
3.3 Implementation of XGBoost Algorithm
3.4 Effect Evaluation
4 Conclusions
References
Research and Application of Key Technologies in 5G Mobile Network E2E Slicing
1 Introduction
2 5G Slicing Service Development and Key Technologies
2.1 Evolution of Slicing Protocol Specification
2.2 Research on Key Slicing Network Technology
2.3 Network Slicing Management Technology
3 Beijing Unicom 5G Slice Management and Operation System
3.1 Overall Architecture
3.2 Overall Capacity of 5G Slice Operation Platform
4 Slicing Technology Enables Scenario Application of Innovative Product
4.1 Design of Slicing Innovative Product
4.2 Scenario-Based Application
5 Conclusion
References
Research on 5GtoB Service Monitoring and Prediction Scheme in the Full Scenario of ‘Smart Winter Olympics’
1 Introduction
2 Related Work
2.1 Service Connection SLA Indicator Monitoring Function
2.2 SLA Monitoring Indicators for Video Service Performance
2.3 Network Performance SLA Monitoring
3 Methodology
3.1 Radio Access Network Model
3.2 Fixed Network Model
3.3 Critical Device Model
3.4 End to End Service Model
4 Experimental Results
5 Conclusions
References
Research on Handling User Complaints in 5G Key Scenarios Based on Reinforcement Learning
1 Introduction
2 Design of Intelligent Complaint Handling Scheme for 5G Users
2.1 The Prototype of User Complaint Handling
2.2 Design of Intelligent Handling of User Complaints
3 Intelligent Learning
3.1 Markov Attribute of Complaint Handling
3.2 Constructing Markov Decision Process
3.3 State Space
3.4 Action Space
3.5 Reward Strategy
3.6 Data Sources
4 Application in Actual Production
5 Conclusion
References
Research on 5G Networking and Massive MIMO Intelligent Optimization Method Based on Big Data and AI for Winter Olympics Venues
1 Introduction
2 5G Antenna Technology
2.1 Massive MIMO Technology
2.2 Distributed Massive MIMO Technology
3 Distributed Massive MIMO Networking Scheme for Winter Olympics Venues
4 Massive MIMO Intelligent Optimization Scheme Based on Big Data and AI for Winter Olympics Venues
4.1 Network Evaluation
4.2 Iterative Optimization
5 Application of Scheme
5.1 Application of 5G Networking Scheme for Winter Olympic Venues
5.2 Application of Massive MIMO Intelligent Optimization Scheme
6 Conclusion
References
Users' Perception Monitoring Operation System Based on Big Data Algorithm for Highway Scenarios
1 Introduce
2 Problem Description
3 City Freeway’s Mobile Network Monitoring Scheme According to Big Data Analysis
3.1 User Perception Monitoring Architecture
3.2 User Perception Monitoring
4 Implementation of Expressway Optimization
4.1 Elevated, Freeway, High-Speed Railway Network Evaluation
4.2 Elevated, Freeway, High-Speed Railway Problem Handling Management
4.3 Determination of Elevated, Freeway, High-Speed Railway Network Problems
5 Implementation Effect
6 Conclusion
References
Weak Coverage Analysis Method for Mobile Networks Based on Machine Learning
1 Introduction
2 Mobile Network Coverage Analysis and Optimization
2.1 Coverage Analysis Methods
2.2 Coverage Optimization Method
3 Weak Coverage Analysis Method
3.1 Weak Coverage Localization Based on Density Clustering
3.2 Optimization Algorithm Based on Clique Algorithm
4 Modeling Analysis
4.1 Data Set
4.2 Result
5 Conclusion
References
Indoor Positioning Based on Enhanced 5G Fingerprint Positioning Algorithm
1 Research Background
2 5G Fingerprint Positioning Algorithm
2.1 Fingerprint Positioning Algorithm
2.2 5G Fingerprint Database Building Stage
2.3 Online Matching Stage
3 Performance of 5G Fingerprint Positioning Algorithm
4 Conclusion
References
Root Cause Analysis Based on Trace for Mobile Network Problem
1 Introduction
2 LTE Trace and Event
2.1 The Reason for Choosing TRACE
2.2 Trace and Event
3 Root Cause Analysis for Mobile Network
3.1 Perceptual Entry to Find the Problem
3.2 Cell and Grid Indicators
3.3 Cause Analysis
3.4 Root Cause Analysis Process
4 Conclusion
References
A Solution of 5G Multi-beam Optimization Based on Big-Data
1 Research Background
2 5G Massive MIMO - Multi-beam Optimization
2.1 Massive MIMO Beam
2.2 Location Capability
2.3 Optimization Objective Setting
2.4 Application Scenario
2.5 Influencing Factors and Limitations
3 Online Deployment
4 Automatic Multi-beam Adjustment
5 Conclusions
References
A Microservice-Based Visual Orchestration Platform for 5G IoT Applications
1 Introduction
2 Agile and Lightweight IoT Application Architecture
2.1 The Business Process Run on the AMP
3 Implementation
4 Case Study
5 Evaluation
6 Conclusion
7 Related Works
References
Comprehensive Analysis Scheme of Video Service Based on XDR
1 Introduction
2 Development Status of Mobile Video Service and Operator Strategy
2.1 Improve Network Operations and Provide High-Quality Network Experience for Mobile Video Services
2.2 Continuous Traffic Operation, Exploring New Video Service Modes and Products
2.3 Layout Content Operations, and Widely Aggregate Content Through the “INtroduction + Self-made” Approach
2.4 Explore Technology Empowerment to Bring Greater Imagination to the Video Business
2.5 Explore Data Operations and Reconstruct the Video Business Value Chain
3 Big Data Operation Analysis System of Video Service
3.1 Comprehensive Analysis of Mobile Video Service
3.2 Classified Video Service Analysis
3.3 Competitive Product Analysis of Video Apps
3.4 Multidimensional Analysis of Single Video APP
4 Big Data Operation Monitoring Platform of Video Service
5 Conclusion
References
5G Close Loop Proactive Optimization Using Network Data Analysis Function
1 Introduction
1.1 Background
1.2 Contribution and Organization
2 System Architecture
2.1 R&D Environment
2.2 NWDAF Implementation
2.3 Qos Prediction Based Proactive Optimization
3 System Operation Result
4 Conclusion
References
Evolution of Intelligent Telecom Networks in 5G Era
1 Introduction
2 Smart Network
3 Autonomous Network
4 Intent Network
5 Compute First Networking
6 Conclusion
References
Research and Application of 5G Super Uplink Technology in Plateau
1 Introduction
2 Technical Principles of Super Uplink
3 Deployment Method of Super Uplink
3.1 Independent SUL Deployment Mode
3.2 SUL and NR FDD Co-deployment Mode
3.3 Pilot Deployment Mode
4 Pilot Effect of Super Uplink
4.1 Pilot Scenario
4.2 Verification Effect of Fixed-Point Test
4.3 Verification Effect of KPIs
4.4 Verification Effect of LTE KPIs
5 Conclusion
References
Author Index

Citation preview

Lecture Notes in Electrical Engineering 996

Yue Wang Yuyang Liu Jiaqi Zou Mengyao Huo   Editors

Signal and Information Processing, Networking and Computers Proceedings of the 10th International Conference on Signal and Information Processing, Networking and Computers (ICSINC)

Lecture Notes in Electrical Engineering

996

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering and Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •

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Yue Wang · Yuyang Liu · Jiaqi Zou · Mengyao Huo Editors

Signal and Information Processing, Networking and Computers Proceedings of the 10th International Conference on Signal and Information Processing, Networking and Computers (ICSINC)

Editors Yue Wang China Academy of Space Technology Beijing, China Jiaqi Zou Beijing University of Posts and Telecommunications Beijing, China

Yuyang Liu Beijing University of Posts and Telecommunications Beijing, China Mengyao Huo Beijing University of Posts and Telecommunications Beijing, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-19-9967-3 ISBN 978-981-19-9968-0 (eBook) https://doi.org/10.1007/978-981-19-9968-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

It is our great honor to welcome you to the 10th International Conference on Signal and Information Processing, Network and Computers (ICSINC 2022). The ICSINC 2022 Committee has been monitoring the evolving COVID-19 pandemic. We have decided to delay the 2022 edition of this conference from July to September. ICSINC 2022 provides a forum for researchers, engineers, and industry experts to discuss recent development, new ideas and breakthrough in signal and information processing schemes, computer theory, space technologies, big data, and so on. ICSINC 2022 received 314 papers submitted by authors, in which 150 papers were accepted and included in the final conference proceedings. The accepted papers were presented and discussed in 27 regular technical sessions and 2 workshops. On behalf of the ICSINC 2022 committee, we would like to express our sincere appreciation to the TPC members and reviewers for their tremendous efforts. Especially, we appreciate all the sponsors for their generous support and advice, including Springer, China Unicom, HuaCeXinTong company. Finally, we would also like to thank all the authors for their excellent work and cooperation. Songlin Sun Yue Wang Changbo Zhu ICSINC 2022 General Chairs

Committee Members

International Steering Committee Songlin Sun Ju Liu Chenwei Wang Jiaxun Zhang Takeo Fujii Hongwu Li Xiaoyu Ye Changbo Zhu Fang Wang

Beijing University of Posts and Telecommunications, China Shandong University, China DOCOMO Innovations (DoCoMo USA Labs), USA China Academy of Space Technology, China The University of Electro-Communications, Japan China Unicom Research Institute, China China Unicom Research Institute, China China Unicom QingHai Branch, China China Unicom QingHai Branch, China

General Co-chairs Songlin Sun Yue Wang Changbo Zhu

Beijing University of Posts and Telecommunications, China China Academy of Space Technology, China China Unicom QingHai Branch, China

Technical Program Committee Chairs Xinzhou Cheng Qiang Zhang

China Unicom Research Institute, China China Unicom QingHai Branch, China

Publication Chair Yuyang Liu

Beijing University of Posts and Telecommunications, China

viii

Committee Members

Exhibits Chair Lianxin Xin

China Unicom QingHai Branch, China

Local Service Committee Zhijuan Tao Wei Shang

Sponsors Springer

China Unicom

HuaCeXinTong

China Unicom QingHai Branch, China China Unicom QingHai Branch, China

Contents

Information Technology Session 1 A New Multi-instance Learning Algorithm Integrated with TF-IDF for Entity Relation Extraction in Electronic Medical Records . . . . . . . . . . . . . . . Yiyang Xiong, Yajuan Qiao, Shilei Dong, Xuezhi Zhang, and Hua Tan

3

A Deep Learning Algorithm Using Feature Engineering to Adjust Attention Mechanisms and Neural Network for Cloud Security Detection . . . . . Yiyang Xiong, Yajuan Qiao, Shilei Dong, Xuezhi Zhang, and Hua Tan

11

The Design and Implementation of a Ranging System Based on the DSSS . . . . Yingpeng Cao, Fu Li, Shuyi Qin, Wei Zhang, and Zihong Yuan Application of Grey Approximation Ideal Solution Ranking Methods in Optimal Selection of Satellite Initial Orbits . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Lei, Wang Fan, and Bo Zhenyong

19

27

Filter and Closed Loop Control Technology of Piezoelectric Drivers . . . . . . . . . Qian Lu, Bin Ren, and Le Zhang

39

A Novel Back-Feed Double-Layer Microstrip Antenna . . . . . . . . . . . . . . . . . . . . Pengfei Zhao, Dabao Wang, Mingxuan Wu, Xiangwei Ning, Xiaojuan Li, and Jinyuan Ma

48

A Full-Digital Test Method for Satellite-Borne Software . . . . . . . . . . . . . . . . . . . Pan Xin, Jiao Rong-Hui, and He Jing

55

3D Warehousing: Enabling Intelligent Warehousing Visualization Based on Three.js . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenqian Shan, Wenbo Wan, Anyi Chen, Leichao Ren, Jiande Sun, Meng Fang, and Yantong Zhan

63

Deep Learning Neural Networks with Auto-adjustable Attention Mechanism for Server Fault Diagnosis Under Log Data . . . . . . . . . . . . . . . . . . . . Yiyang Xiong, Yajuan Qiao, Shilei Dong, Xuezhi Zhang, and Hua Tan

72

A Hierarchical Classification Algorithm of Spacecraft Telemetry Data Based on Time Series Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Wan, Tang Li, Da Tang, and Qianqian Zhang

80

x

Contents

A Saliency-Transformer Combined Knowledge Distillation Guided Network for Infrared Small Target Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Zhang, Wenquan Feng, Menghao Li, Shuchang Lyu, and Ting-Bing Xu

88

Research on Cloud-Edge Collaboration Based on Object Storage for Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiuhong Zheng, Dan Liu, Yun Shen, and Peng Ding

96

Industrial Defect Detection System Based on Edge-Cloud Collaboration and Task Scheduling Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuying Xue, Yun Shen, and Huibin Duan

104

Actuator Grouping Optimization Method of Antenna Reflector Based-on Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Du, Songjing Ma, Tao Ma, Xiangshuai Song, and Jiayou Zhang

113

A Method to Measure the Quality of a Cloud Network Across Multiple Resource Pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Su Yue, Chen Ruihao, Li Wei, Zhao Weibo, and Ma Fei

122

A New Cloud Computing Deployment Model: Proprietary Cloud . . . . . . . . . . . . Weibo Zhao, Su Yue, Ma Fei, Ruihao Chen, and Li Wei

130

Overview of the Development of Secure Access Service Edge . . . . . . . . . . . . . . Ruihao Chen, Su Yue, Weibo Zhao, Ma Fei, and Li Wei

138

Optimization and Numerical Investigation of Micro-pin-Fin Structure on Heat Sink with Checkerboard Nozzles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huajie Lv, Kehan He, and Xing Ju A Method of Design and Evaluation of X-ray Communication in Black-Out Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fangyuan Xia, Bo Wang, Guoting Zhang, Yabo Yuan, Jian Wu, Yingjun Zhang, Yao Li, Furui Zhang, Zhenkun Tan, Yun Du, and Tong Su

146

155

A Robust DOA Estimation Method Based on Auxiliary Sensors and Power Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yihao Song, Puming Huang, Lede Qiu, Shuai Li, and Ming Li

161

Diffusion Convolution Graph Attention Network for Spatial-Temporal Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Yin, Lei Wu, Yanqiang Zhang, Yanni Han, and Kun Zhai

170

Contents

Fast Estimation Technology of Orbit Information for Non-cooperative Space Targets Based on AREKF Filtering Theory . . . . . . . . . . . . . . . . . . . . . . . . . Zhuo Zhang, Gang Liu, Henghai Fan, Dongmei Kuang, Anliang Li, and Ruixi Gaoya

xi

179

Information Technology Session 2 Design of Intelligent Power Supply and Distribution Test System for Small Satellite Based on Distributed Structure . . . . . . . . . . . . . . . . . . . . . . . . . Yuexin Hu, Jing Zhang, Guiying Zhang, Guangjie Ren, and Hui Yao

189

Design of Small Satellite Power Supply Interface Test System Based on PXI Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianfeng Dai, Zhi Yang, Haichao Wu, and Yiming Cheng

198

Research on Method of On-Orbit Evolution for Aerospace Control Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XiaoFeng Li, XiaoGang Dong, Bin Gu, Ming Zhao, RuiMing Zhong, Yi Li, and Jing Wang Research on Automatic Assembly Technology for Spacecraft System . . . . . . . . Zejian Chen, Jizhi Yang, Jianping Xu, Yangcheng Zhang, Yongliang Li, and Yi Yue

207

219

Design and Research of Multi-user Distributed Configuration Management Based on Zookeeper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming Zhang, Zhaojian Shen, Bin Yin, Li Cui, and Fan Xu

228

A Complete Ensemble Local Mean Decomposition and Its Application in Doppler Radar Vital Signs Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . Meng Zhang, Zhibin Yu, Pang Rong, and Gao Yuan

236

The Key Research of High Speed Camera Based on Multiple Core CMOS Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanzhang Li, Hong Zeng, Guangsen Liu, Jun Zhu, and Xiaoling Che

245

Research on Cargo Level Optimization Based on Multi-objective Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Shi, Yanan Tan, Hongling Chen, Wenbo Wan, Yunhua Wang, and Leichao Ren The Application of Single Pulse Output Module Integrated by FPGA in On-Board Electronic Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sun Xiaofeng, Sun Hao, Xing Weiwei, and Wangbo

253

260

xii

Contents

Design of Steady Speed Control System for Space Borne Scanning Loads . . . . Yong Zhou, Jie Pang, Fuqiang Liu, Chao Ma, and Chao Dong

267

Research on Slotting Optimization Based on MOEA/D . . . . . . . . . . . . . . . . . . . . Hongling Chen, Yanyan Tan, Lin Shi, Wenbo Wan, Leichao Ren, and Yunhua Wang

276

An Improved Density Peak Clustering Algorithm Based on Gravity Peak . . . . . Hui Han and Rui Zhang

284

A DPC Based Recommendation Algorithm for Internship Positions . . . . . . . . . . Rui Zhang, Lingyun Bi, and Tao Du

292

Optimal Method of Air Cargo Loading Under Multi-constraint Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuheng Lu, Chunyun Dong, Meng Nan, Xiaolong Chen, and Yufan Wei

300

Research on Software Synthesis Method for Spacecraft Control System . . . . . . Yi Li, Xiaogang Dong, Xiaofeng Li, Bin Gu, Xingsong Zhao, Yanxia Qi, and Bo Yu

309

Prediction and Analysis of Infectious Disease Visit Data Based on DPC . . . . . . Rui Zhang, Yuhui Song, and Tao Du

319

Research on the Method of Improving the Accuracy of MRP Calculation Through Big Data in Aerospace Enterprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wen Liu, Ruihua Li, and Yuzhu Li

328

Design and Application of Bad Block Management Strategy for On-Board Solid-State Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongjun Ma, Jianhong Xiao, Dayang Zhao, and Yifeng He

334

An Automated Scoring System for Photoshop Course in Secondary Vocational Colleges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Liu, Zhiyan Wang, Xiufang Liu, and Wenbo Wan

341

Discussion on the Application of Contributing Student Pedagogy in Vocational Education “Computer Network Technology” . . . . . . . . . . . . . . . . . Fanjie Lv and Jiande Sun

349

Research on NURBS Interpolation Algorithm Based on Newton Iteration and Adams Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunsen Wang, Lin Li, Chengzhi Ma, SiPei Shao, and Yangchuang Cao

355

Contents

Fake AP Detection Technology Based on Improved DBSCAN Algorithm . . . . . Zhaoyuan Mei, Chenwei Wang, Lvxin Xu, and Songlin Sun

xiii

362

Vehicles Location Estimation and Prediction in Integrated Sensing and Communications: Sensing Vehicles as Extended Targets . . . . . . . . . . . . . . . . Guanyu Zhang, Zhilei Ling, and Songlin Sun

371

Research on Vehicle and Cargo Matching of Electric Materials Based on Weed Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao Yu, Dedi Huang, Yanlong Gao, Jun Zhu, and Bohan Ren

380

Improved MSFLA-Based Scheduling Method of Electric Power Emergency Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tiezheng Wang, Xiao Yu, Ming An, and Liang Shi

389

A Survey of Class Activation Mapping for the Interpretability of Convolution Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingwei He, Bohan Li, and Songlin Sun

399

Robust Multi-person Reconstruction in Right Spatial Arrangement from In-the-Wild Scenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Wang, Ronghui Zhang, Hai Huang, and XiaoJun Jing

408

Research on the Curriculum System of Electronic Information Engineering for the Certification of Engineering Education . . . . . . . . . . . . . . . . . Songlin Sun, Jiaqi Zou, Zhilei Ling, Hai Huang, Tiankui Zhang, Caili Guo, and Changchuan Yin

417

Multimedia and Communication Networks An S Band Antenna Using Meta-surface for Satellite TT & C Test . . . . . . . . . . . Xiangwei Ning, Fanhui Zhou, Liang An, Shuo Feng, Pengfei Zhao, and Aixin Chen Design and Implementation of a Test and Verification System for Spaceborne Synthetic Aperture Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Liang, Liang Jian, Yang Li, Hao Zhiya, and Zhongjiang Yu An Optimized Scheme for Telecommand Transmission . . . . . . . . . . . . . . . . . . . . Liu Bo, Weining Hao, Hongguang Wang, and Weisong Jia Transmission Efficiency Improvement of Ka-Band VCM Satellite-to-Ground Data Transmission System of LEO Remote Sensing Satellite Based on DVB-S2X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhongguo Wang and Dabao Wang

425

430

437

443

xiv

Contents

Anti-interference Control for On-Orbit Servicing Spacecraft Formation Under Communication Resource Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Wang, Yingchun Zhang, Heli Gao, Jie liu, and Hongchen Jiao

452

FPGA-Based High-Speed Remote Sensing Satellite Image Data Transmission System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yifeng He, Peng Sun, Qian Liu, Hao Zhang, and Dayang Zhao

460

Application of Frequency Domain Filtering in Edge Detection . . . . . . . . . . . . . . Bo Li and Hongyan He

471

Research on the Anti-interference of a Satellite Telecommand Interface . . . . . . Shifeng Gao, Jingang Wang, Luyuan Wang, Zhenyu Wu, and Duanguo Si

478

Intelligent Propagation Prediction Model for Wireless Radio Channel Based on CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yajuan Qiao, Yiyang Xiong, Shilei Dong, Xuezhi Zhang, and Hua Tan Research on Application Efficiency Analysis Method of Remote Sensing Satellite for Emergency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gao Han, Bai Zhaoguang, Che Xiaoling, Zhao Liang, Wu Bin, Wang Chao, Zhu Jun, and Bai Yuchen

486

494

Research on Blind Restoration Algorithm of Motion Blurred Remote Sensing Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanwen Yang, Yanran Liu, Li Luan, Yunsen Wang, and Haoxuan Wang

505

A Fast Algorithm of CU Block Division Based on Frequency Domain on VVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zihao Liu, Lei Chen, and Chenggang Xu

513

Optimization of AVS3 Intra Derived-Tree Mode Algorithm Based on DCT Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chenggang Xu, Fuyun Kang, Yi Wu, and Lei Chen

520

Integrated Sensing and Communications with OTFS: Application in V2I Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhilei Ling, Yuxin Wu, Guanyu Zhang, and Songlin Sun

528

Space Technology Session 1 Fault Analysis and Evaluation of Control Moment Gyroscope on Orbit . . . . . . . Jinfeng Zhou, Xi Chen, and Boneng Tan

541

Contents

Precision Orbit Determination for LEO Based on BDS Navigation Data . . . . . . Chao Li, Xiusong Ye, Hong Ma, Shouming Sun, and Yang Yang Research and Practice of Electrostatic Grounding Technology of Aerospace Electronic Products Based on Intelligent Active Protection Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Zhu, Dong Wang, Ke Li, Wei Qiao, Yijian Zheng, and Wenze Qi

xv

551

560

Investigation on Feasibility of Covert TT&C Scheme Using Satellite Payload Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Chen, Haiming Qi, and Fengchun Wang

567

A Satellite Imaging Stability Monitoring Method Based on Lunar Calibration Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong-chang Li, Qiang Cong, and Shun Yao

574

Design and Verification of mK Temperature Fluctuation Control for Gravity Gradiometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Liu, Yupeng Zhou, and Tinghao Li

581

A Perigee Orbit Change Method for Geostationary Satellite . . . . . . . . . . . . . . . . ShuHua Zhang, Heng Yao, JianPing Li, and ZhongMou Lei

590

Optimal Design of Power Drive Unit Layout Based on Improved Discrete Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufan Wei, Chunyun Dong, Meng Nan, Xiaolong Chen, and Yuheng Lu

598

Preliminary Discussion on the Application of Digital Thread in the Model-Based Spacecraft Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingfeng Zhao, Ziwei Wu, Lingyun Cheng, and Guoxiong Zhan

606

Study on the Construction Method of Spacecraft System Model Based on Meta-model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiangdong Ruan, C. G. Li, Yuxuan Li, and Xinwu Chen

614

The Design and Implementation of Magnetic Control of Ultra Quiet Control System in Gravity Satellite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bin Guan, Yiwu Liu, Qirui Liu, Yan Li, and Jiakun Fan

622

Intercomparison of On-Orbit Consistency of Radiometric Calibration Between Two Sensor Calibration Spectrometers of HY Satellite . . . . . . . . . . . . . Huiting Gao, Yue Ma, and Qingjun Song

630

xvi

Contents

Loose Formation Keeping Control for Low-Earth Orbit Satellite Cluster Considering the Interaction Between Cluster Satellites . . . . . . . . . . . . . . . . . . . . . Huang He, Yang Xu, Zhang Qi, and Jing Baohong Research on Data Fusion Architecture of GNC Subsystem of China Space Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingsong Li, Jing Wang, Haixin Yu, Xiaofeng Li, Ruiming Zhong, Xiaogang Dong, Zhaohui Chen, and Junchun Yang

638

647

Application in Emergency Tasks Schedule with the GFDM-1 Satellite . . . . . . . . Mingliang Liu, Hu Qiu, and Ziwei Li

656

Emergency Scheduling Process Design for ZY303 Satellite . . . . . . . . . . . . . . . . . Ziwei Li, Mingliang Liu, and Hu Qiu

663

Research on the Application of TDRSS for Manned Spacecraft . . . . . . . . . . . . . Da Tang, Tang Li, Peng Wan, Zhisheng Wang, and Yushu Feng

673

Modeling and Analysis of Satellite with a Large Inertia Rotating Load . . . . . . . Guiming Li, Shixuan Liu, Zhihui Li, Xinyan Wu, Kui Zou, and Rui Liu

681

Research on Satellite-to-Ground Integration Joint Mission Planning System for Mega-constellation Multi-user Remote Sensing Information Resource Guarantee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shaoyu Zhang, Xu Yang, Ruolan Zhang, Keqiang Xia, Xiaobo Hui, Haipeng Liu, and Xing Wang Analysis of the Influence of Satellite Drift Angle Correction Datum on Imaging Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao Wang, Yuze Cao, Han Gao, Cheng Yan, Hongzhi Zhao, Keli Zhang, Jun Zhu, and Lili Wang

689

696

Application and Process of Inclination Adjustment of Satellites in Sun Synchronous Orbit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shenggang Wang and Yuan Jun

705

Analysis Method for Isolation Index of Transceiver Link of Space Ground TT&C Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingyu Zhao, Jianxiao Zou, Shuo Wang, Yalong Yan, and Ben Su

713

Satellite Momentum Wheel Life Prediction Method Based on PSO-LSSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Chao, Wenjie Ma, and Jiexuan Song

723

Contents

xvii

Design of Equivalent Simulation Test System for Universal Power Supply and Distribution of Space Station Multi-aircraft . . . . . . . . . . . . . . . . . . . . Xu-zhen Jing and Peng Ying

731

Design and Verification of a Geographic Information Prediction Scheme for Autonomous Mission Planning of Satellites . . . . . . . . . . . . . . . . . . . . . . . . . . . Rina Wu, Jie Liu, Tao Zhang, Jianmin Zhou, and Chao Chen

740

Space Technology Session 2 Bit Synchronization Verification Strategy for High Orbit Navigation Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mengdan Cao, Yu Chen, Yukui Zhou, Zhaoqiang Cheng, Yu Peng, and Dongsheng Shi

749

Real Time Dynamic Adjustment Calculation Method of Camera Integration Time Based on Spaceborne Digital Elevation Model . . . . . . . . . . . . . Linfeng Xing, Chao Xue, Xin Guan, and Gaojian Lv

759

A Time Sequence Design for Improving the Output Frequency and Accuracy of Star Sensor Under High Dynamic Conditions . . . . . . . . . . . . . . Rui Liu, GuiMing Li, ZhiHui Li, and JianFu Zhang

766

Research on the Measurement Method of Inter-satellite Clock Difference and Distance of Formation Satellites . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Liu, Yujie Liu, and Sufang Chen

772

Research on Asteroid Detection Radar Based on High Precision Signal Delay Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sufang Chen, Mengna Jia, Yang Liu, and Dong Liu

780

Review of Micro-vibration Modeling, Suppression, and Measurement for Spacecraft with High-Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meng Ge, Kuai Yu, Yongsheng Wu, Dong Wang, Guangyuan Wang, and Chongzhou Yu

787

Satellite Gravity Gradient Measurement Technology Based on Superconducting Suspension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming Li, Yue-xin Hu, and Ming-xue Shao

797

The Mathematic Analytical Results of Self Excited Flyback Converter with Double Switches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shan Li, Dinghao Sun, Liwei Wang, Xuhui Liu, and Xudong Wang

804

xviii

Contents

Anomaly Interpretation Method Based on Data Mining for Remote Sensing Satellite Payload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia You and Chengzhi Lu

814

A Study on Determination Method of Safety Band in the Translational Closing Section of Rendezvous and Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Honghui He and Changqing Chen

823

Prospect Analysis of Satellite Application of Long-Distance Wireless Energy Transmission System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sen Wang, Xiaofei Li, Xiaochen Zhang, Yudan Liu, and Bingxin Zhao

829

Design of Multi-dimensional Control and Multi-subsystem Cooperative Operation Algorithm for Control Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hong Guan, Jian Cai, HeLong Liu, Yong Li, and Zefeng Ma

838

Research on Attitude Determination of Spacecraft Two-Stage Composite Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaojian Lv, Chao Xue, Xin Guan, and Linfeng Xing

844

Design of Dual Robot Collaborative Assembly Scheme for Aerospace Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lijian Zhang, Dingwen Pan, and Ruiqin Hu

850

Study on the Application of Robot-Assisted Assembly in Satellite Board Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tiecheng Qiu, Qian Zhang, and Liang Guo

858

3D Model-Based Design Method for Complex Cable Network Three-Dimensional Marking of Spacecraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qian Zhang, Tiecheng Qiu, and Yu Fu

866

Study on the Optimization of Temperature Measurement Points for Small Satellite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhijia Li, Dawei Li, and Zhiming Xu

873

Design of Model-Based Spacecraft Flight Control Support System . . . . . . . . . . Wang Xiaoming, Gao Hongtao, Fan Daliang, Qu Xiaoyu, and Zhang Xiaogong

881

Contents

xix

Big Data Workshop Session 1 Research and Application of 5G Massive MIMO Antenna Weight Intelligent Optimization Based on 4G/5G Coordination . . . . . . . . . . . . . . . . . . . . Zixiang Di, Xinzhou Cheng, Jiajia Zhu, Yi Li, Lexi Xu, Jinjian Qiao, Lu Zhi, and Wenzhe Wang A Method of 5G Core Network Service Fault Diagnosis . . . . . . . . . . . . . . . . . . . . Shiyu Zhou, Yong Wang, Xiqing Liu, Xinzhou Cheng, Zhenqiao Zhao, and Tian Xiao Research on User Behavior Credibility Evaluation Model in Trusted Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anshun Zhou, Suimin Wang, Mingde Huo, Jianzhi Wang, Yuwen Huo, Shihan Fu, and Lexi Xu Research and Application of Key Technologies of Intelligent Manufacturing Based on 5G Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shangyu Tang, Mingde Huo, Yan Zhang, Xin Zhao, Yuwen Hou, Ying Ji, and Guoyu Zhou Research on Key Technologies of 5GC Network Intelligent Operation Under the Background of Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . Zhenqiao Zhao, Xiqing Liu, Yong Wang, Jie Miao, Shiyu Zhou, Xinzhou Cheng, Lexi Xu, Xin He, and Icy Yan Research on Location Algorithm of Mobile Network Based on Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bei Li, Wei Zhao, Kai Zhou, Lexi Xu, Guanghai Liu, Tian Xiao, Chen Cheng, Lu Zhi, Wei Zhang, and Ziwei Zhu

893

902

911

920

929

938

Research on the Development of 5G Messaging Service for Operators . . . . . . . Mengni Chen, Ziyuan Zhu, MuYun, Jianying Guo, Yimeng Song, and Sainan Hou

947

Investigation of 5G Terminal Users with Dual SIMs . . . . . . . . . . . . . . . . . . . . . . . Jiajun Li, Bowei Pei, Yukun Liu, and Fengwei Chen

954

Research on Deep Packet Inspection for Driving Digital Operation . . . . . . . . . . Miao Sun, Qiang Zhang, and Gan Han

961

Research on the Operation Model of Operator Computing Products . . . . . . . . . . Yimeng Song, Fan Shi, Mengni Chen, Yun Mu, and Jianying Guo

968

xx

Contents

Big Data Processing Mode Based on Cross Data Source SQL Engine . . . . . . . . Yilong Li, Qiang Zhang, Bin Han, and Jianru Wang Online Machine Learning-Based Quality Difference Identification and Prediction Prevention for Broadband Users . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Deng, Lin Yuan, Ling Zhen, Xina Zhang, Jingzhu Chen, and Xiuyuan He Achieving Privacy-Preserving Diagnosis with Federated Learning in LEO Satellite Constellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qinglei Kong, Zhidi Lin, Feng Yin, Lexi Xu, Xinzhou Cheng, Shuguang Cui, and Xiaoyu Ye Cellular Network Coverage Hole Detection and Diagnosis Method Using WaveCluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zijing Yang, Lexi Xu, Feibi Lyu, Lixia Liu, Jiajia Zhu, Kun Chao, and Xinzhou Cheng

975

981

990

999

A Novel Method of Trace Message Decoding and the Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1007 Shengli Guo, Jinghui Li, Xiaodong Cao, Chao Wang, Chuntao Song, Xinzhou Cheng, Runsha Dong, Tianyi Wang, and Xijuan Liu Study on Cost Difference Between Peak-Valley Pricing and Flat Pricing . . . . . . 1014 Zeyi Yang, Yang Liu, Feng Luo, Xiaoxuan Du, Zetao Xu, Pengcheng Liu, Ao Shen, and Yuan Fang Three-Dimensional Integrated Base Station Rental Fee Benchmark Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1022 Zetao Xu, Pengcheng Liu, Yang Zhang, Shuaizhong Pan, Zeyi Yang, Yang Liu, Feng Luo, and Xiaoxuan Du The Energy Saving Measurement System and Method of Main Base Station Communication Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1030 Yang Liu, Zeyi Yang, Feng Luo, Xiaoxuan Du, Xidian Wang, Zetao Xu, Ao Shen, and Yuan Fang Energy Saving Technology and Parameter Research of 5G Terminal . . . . . . . . . 1038 Yuan Fang, Jiandi Luo, Yun Lu, Jian Hu, Ao Shen, Pengcheng Liu, Yao Cen, Zetao Xu, Jimin Ling, and Qintian Wang Research on Energy Consumption Modelling of 5G Wireless Communication Main Equipment Based on GBRT Algorithm . . . . . . . . . . . . . . . 1046 Yao Cen, Yuan Fang, Zeyi Yang, Yunlong Liu, Sheng Zhang, Zetao Xu, Ao Shen, and Yang Liu

Contents

xxi

Research of Voice Speech Quality Evaluation Based on Trace Data . . . . . . . . . . 1054 Lu Zhi, Xinzhou Cheng, Jiajia Zhu, Lexi Xu, Bei Li, Zixiang Di, Liang Liu, Jinjian Qiao, and Zhaoning Wang Analysis of the Impact of COVID-19 on the Development of National ICT Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062 Yang He, Cheng Feng, Quan Yu, Rui Zhou, and Ziyuan Zhao Research on Low Latency and High Reliability Wireless Technologies and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1070 Yi Li, QingLiang Long, Zixiang Di, Yuchao Jin, Yuting Zheng, Lexi Xu, Xinzhou Cheng, and Bei Li Resource Multiplexing Schemes of URLLC and eMBB Under Multi-service Coexistence Scenario Based on Management Aspects . . . . . . . . . 1078 Yuchao Jin, Yi Li, Yuting Zheng, Deyi Li, Xinzhou Cheng, Qingliang Long, Lexi Xu, and Tian Xiao Big Data Workshop Session 2 5G Construction Efficiency Enhancement Based on LSTM Algorithm . . . . . . . . 1089 Tian Xiao, Qingliang Long, Lexi Xu, Guanghai Liu, Zixiang Di, Bei Li, Zhaoning Wang, Shiyu Zhou, and Fei Xue Business Circle Attraction Based on DPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1097 Wei Zhang, Yuhui Han, Qingqing Zhang, Tianyi Wang, Chen Cheng, Lexi Xu, Xinzhou Cheng, and Bei Li A Novel Method of Obtaining Location Information . . . . . . . . . . . . . . . . . . . . . . . 1105 Shengli Guo, Lexi Xu, Xiaodong Cao, Chao Wang, Chuntao Song, Xinzhou Cheng, Runsha Dong, Tianyi Wang, Jinghui Li, and Xijuan Liu Research on Network Quality Intelligent Monitoring Technology Based on Prophet Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1112 Hongbo Shen, Baoyou Wang, Saibin Yao, Jiucheng Huang, Zhanqiang Liu, Lei Chen, and Jie Tian Uplink Tx Switching Application Optimization Based on XGBoost Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Tao Huang, Tian Xiao, Yao Wei, Lexi Xu, Ji Lan, Pengxiang Li, Jiansheng Liang, and Xinyan Wang

xxii

Contents

Research and Application of Key Technologies in 5G Mobile Network E2E Slicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1130 Rui Xiao, Jihua Li, Sirui Zhong, Xinzhou Cheng, Xiaoyu Ye, and Xinrui Yang Research on 5GtoB Service Monitoring and Prediction Scheme in the Full Scenario of ‘Smart Winter Olympics’ . . . . . . . . . . . . . . . . . . . . . . . . . . 1138 Rui Xiao, Wei Zeng, Lei Wang, and Xueting Zhang Research on Handling User Complaints in 5G Key Scenarios Based on Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Wei Zeng, Jihua Li, Weiyi Xia, Xueting Zhang, and Ulrich Kleber Research on 5G Networking and Massive MIMO Intelligent Optimization Method Based on Big Data and AI for Winter Olympics Venues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155 Wei Zeng, Jun Fan, Haotian Wu, Andrey Krendzel, and Rui Xiao Users’ Perception Monitoring Operation System Based on Big Data Algorithm for Highway Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163 Chao Liu, Bei Li, Hui Pan, Li Xu, Xufeng Hang, Zhenwei Jiang, Baoyou Wang, and Ziwei Zhu Weak Coverage Analysis Method for Mobile Networks Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1171 Jinjian Qiao, Jiajia Zhu, Xinzhou Cheng, Lexi Xu, Ning Meng, Lijun Cheng, Jinyu Zhai, Zixiang Di, and Fred Dong Indoor Positioning Based on Enhanced 5G Fingerprint Positioning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179 Li Xu, Saibin Yao, Sibing Rao, Qiuyue Hu, Chao Liu, and Haiyun Zhu Root Cause Analysis Based on Trace for Mobile Network Problem . . . . . . . . . . 1185 Liang Liu, Xinzhou Cheng, Jiajia Zhu, Lexi Xu, Songbai Liang, Lijun Cheng, Jinyu Zhai, and Fred Dong A Solution of 5G Multi-beam Optimization Based on Big-Data . . . . . . . . . . . . . 1193 Xufeng Hang, Yi Zeng, Baoyou Wang, Chao Liu, and Fengli Dai A Microservice-Based Visual Orchestration Platform for 5G IoT Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1201 Zhaoning Wang, Jiajia Zhu, Xinzhou Cheng, Jinjian Qiao, Feibi Lyu, Tian Xiao, Xu Han, and Tao Lyu

Contents

xxiii

Comprehensive Analysis Scheme of Video Service Based on XDR . . . . . . . . . . 1209 Lijuan Cao, Xin He, Yuwei Jia, Kun Chao, Yunyun Wang, Lexi Xu, Chen Cheng, Heng Zhang, Yuchao Jin, and Yi Li 5G Close Loop Proactive Optimization Using Network Data Analysis Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 Feibi Lyu, Jiajia Zhu, Zhaoning Wang, Jinjian Qiao, Liang Liu, Zixiang Di, Chen Cheng, Tian Xiao, and Yuan Zhang Evolution of Intelligent Telecom Networks in 5G Era . . . . . . . . . . . . . . . . . . . . . . 1225 Runsha Dong, Xiaodong Cao, Han Liu, Lexi Xu, Xin He, Chen Cheng, Shengli Guo, and Xinzhou Cheng Research and Application of 5G Super Uplink Technology in Plateau . . . . . . . . 1234 Lixia Liu, Weijun Chen, Xun Zhu, and Xinjie Hou Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1241

Information Technology Session 1

A New Multi-instance Learning Algorithm Integrated with TF-IDF for Entity Relation Extraction in Electronic Medical Records Yiyang Xiong(B) , Yajuan Qiao, Shilei Dong, Xuezhi Zhang, and Hua Tan China Telecom Corporation Limited Research Institute, Beijing, China [email protected]

Abstract. Electronic medical record (EMR) is the core data which generated in the process of clinical treatment. By extracting the record text’s entity relationship, we can catch a large amount of information closely related to the patient’s condition. Deep learning is widely used in the field of entity relationship extraction in EMR. Among them, seq-to-seq is the most popular network structure for text sequence, but it can’t distinguish the influence between different words. Therefore, multi-instance learning which are improved by attention mechanism can adjust the weight of words is proposed and achieved outstanding results. However, the reason for the creation of the attention mechanism parameter is always puzzling: it lacks interpretability and it is difficult for us to understand the origin of the numbers. Term frequency–inverse document frequency (TF-IDF) is an algorithm to calculate the importance of words according to their occurrence frequency, so we think integrating it into the construction of neural network may increase model’s interpretability. In this paper, we propose a new multi-instance learning algorithm, which integrates the TF-IDF into the construction of deep learning model. Compared with the network without TF-IDF, our model achieves an improvement of nearly 2% and the convergence rate is reduced by 5% on average. Keywords: Multi-instance learning · TF-IDF · Relation extraction

1 Introduction 1.1 Background In daily medical activities, doctors, nurses and other medical personnel usually use the medical information system to generate multiple types of medical records which is called electronic medical records (EMR). EMR records the diagnosis and treatment process and medication suggestions in detail, which has high research value [1]. With gradual improvement of medical informatization, EMR is becoming more and more popular, and gradually plays a more important role in clinical diagnosis and treatment. In EMR natural language processing, entity relation extraction is the core tasks. It refers to obtain the relationship between known entities. For example, here is a text: doctors treat patients. This text has a pair of entities of doctors and patients, and the entity relationship is ‘treat’ (the relationship of ‘treat’ needs to be defined in advance). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 3–10, 2023. https://doi.org/10.1007/978-981-19-9968-0_1

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Deep learning not only has strong ability, but also can solve problems end-to-end, so it become mainstream method of EMR entity relation extraction. Deep learning applied to EMR can be roughly divided into supervised learning algorithm and unsupervised learning algorithm. Supervised learning algorithm usually adopts convolutional neural network which represented by textCNN [2] and PCNN [3], it can obtain excellent performance [4], but these methods need a large number of positive samples, so the cost of large-scale learning is high. Semi supervised learning can make up for this problem because it can quickly obtain a large number of annotation data. The commonly used semi supervised learning include remote supervision learning [5] and multi-instance learning [6]. Among them, multi-instance learning introduces attention mechanism, so the weight of context can be adjusted, and it achieves better results and become the most popular algorithm. 1.2 Our Innovation The introduction of attention mechanism greatly increases the processing ability of neural network for long text [7]. Transformer, which abandons neural network and adopts attention mechanism completely [8], even leads the research direction of natural language processing. Because the text length of EMR is generally long, it is particularly effective. However, the initial parameters of attention mechanism are often set randomly, which brings two problems: 1. The algorithm has great randomness in convergence. 2. Insufficient interpretability, so it is difficult to realize improvement in the future. In addition, the embedding layer of neural network lacks meaningful annotation of data: it usually only has location information. So, if we can set the initial parameters of neural network and attention mechanism through the understanding of the text, can we create a better model? TF-IDF is a very mature technology in the field of text search [9]. It can judge the importance of a word in the text through word frequency. In this paper, we introduce its calculation results into the initial parameters of model, and propose a new multi-instance learning algorithm for EMR. By doing this, we can deliver neural network the importance of each word before training starts. Compared with the network without integration, the convergence speed of our proposed algorithm has been improved by more than 5%, and the accuracy has also been improved.

2 Model Architecture 2.1 Overview When encountering deep learning problems, the ideal state is that the data is either black or white, such as whether a picture is a face. However, in practice, the samples we have are difficult to meet the situation above, especially for the problem of entity relationship extraction: when any two entities appear at the same time, the relationship is usually not unique. For example, there are two sentences: doctors treat nurses and nurses cooperate with doctors. Nurses and doctors have different relationships in these two sentences: treatment and cooperation. Remote supervision method and the multiinstance learning belong to semi supervised learning which can solve the problem above.

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Since multi-instance learning can significantly reduce the impact of false tagging in Distant Supervision method [10], we adopt its framework. The multi-instance learning algorithm makes a hypothesis: if an entity pair has a certain relationship in the knowledge base. Then, in the data containing the entity pair, at least one sentence expresses this relationship. Unlike ordinary supervised learning, which aims at a single sample, multi-instance learning is bag oriented, and a bag contains multiple samples. It has two definitions: 1. If one sample in a bag is a positive sample, the whole bag is marked as a positive bag. 2. If a bag is all negative samples, then the bag is negative bag. Let me give you an example to understand this. For example, we can judge whether there is gold ore from the excavated ore. If there is gold in the trial excavated ore, it is considered that there is gold ore here. If none of the trial excavated ore contains gold, it is considered that there is no gold ore here. Therefore, for the problem of electronic medical record, we adopt multi-instance learning algorithm. Our multi-instance learning algorithm consists of two parts: neural network part and attention mechanism part. Our innovation integrates the results of TF-IDF calculation into the construction of two modules. Through our innovation, our model has the ability to transmit the importance of each word to the neural network from the perspective of word frequency, which quickly improves the convergence speed and slightly improves the accuracy. Next, we will introduce the construction of the model in detail. 2.2 Neural Network In terms of neural network, we adopt Piecewise Convolutional Neural Network (PCNN) as basic framework. The TF-IDF PCNN model we proposed has following framework. Different colors in the figure represent the results of different convolution kernels. In addition, we use blue to represent the starting position of the entity. convolution

ӽ kernel is 3

Word vector matrix

convolution kernel is 4

ӽ

Embedding

Electronic medical record

Max Pooling Multiply with TFIDF weight matrix 1

Max Pooling Multiply with TFIDF weight matrix 2

ӽ convolution kernel is 5 Word

Position

Max Pooling Multiply with TFIDF weight matrix 3

Fig. 1. The figure shows the structure of the neural network. It consists of five parts: word embedding, convolution, TF-IDF weight adjustment, Max pooling and splicing.

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As shown in Fig. 1, We use a common sentence in the electronic medical record to describe how to deal with this example and convert it into sentence vector. Next, we will elaborate the processing process of each step through the example. Word embedding. We first need to convert the text into a word vector that can be understood by computer and neural network. We take a common Chinese text in electronic medical record as an example. Here is a Chinese sentence: “医生和护士相互协作”. Since it consists of 9 rows (each row represents a word), and the number of columns is set to 6. This text is transformed into a 9 * 6 matrix. There are two kinds of word embedding methods. One is that word vector can be obtained in other corpora in advance, and the other is obtained by network training as unknown parameters [11]. These two methods have their own advantages. The first method can use other corpora to get more a priori knowledge. The second method can grasp the features associated with the current task better. In order to make good use of the advantages of both, our model adopts a dual channel method: one of them is embedded in pre trained words and will not change; The other is initialized in the same way, but will be changed with the training process. Please note that since we use the method of piecewise convolution, we need to mark the position of the entity and record it in the word vector (the position part of the vector). The starting position of doctors and nurses is marked here and highlighted in blue in Fig. 1. It can be described as the following formula. S = Rr∗c

(1)

R is the length of the sentence (number of words), C is the width of the convolution kernel, and the size of C is determined by position coding and word embedding. Convolution. The biggest feature of convolution in natural language processing is the width of convolution kernel is the same as that of sentence matrix, and it can only move in the height direction, which reflects words are the smallest constituent unit of sentences. Therefore, in the example given in this paper, the width of convolution kernel is 6. In terms of length, since convolution kernels of different sizes can learn complementary features from each other [12], we set up three convolution kernels with lengths of 3, 4 and 5, and obtained convolution results respectively. The contents can be described by the following formula. C ∈ Rr+w−1 , Cj = wqj−w+1:j s + w − 1 ≥ j ≥ 1,

(2)

W is the size of convolution. Since we use three convolution kernels, so j is equal to three and the convolution output is: C = {C1 , C2 , C3 }, Ci3 = wi q3−w+1:3

(3)

TF-IDF adjustment. Here is an important embodiment of our TF-IDF in innovation. In the construction of word vector, each word is embedded according to the way of pre training or neural network convergence, and the neural network treats them equally. However, we should understand that the information behind each entity is actually different. If a word appears infrequently in the corpus and suddenly appears in our sentences,

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it may bring more information and deserve our more attention. TF-IDF reflects this fact. Its formula consists of three parts: ni,j TFi,j =  k nk,j

(4)

TF (Term Frequency) indicates the frequency of the entry in the text, where ni,j indicates the number of times the entry ti appears in the document dj , and TFi,j indicates the frequency of the entry ti in the document dj . IDFi = log

|D|    1 + j : ti ∈ dj 

(5)

IDF (inverse document frequency) indicates the prevalence of keywords, where |D| indicates the number of all documents and |j: ti ∈ dj | indicates the number of documents containing the term ti . TF − IDF = TF ∗ IDF

(6)

TF-IDF is obtained by multiplying formulas (1) and formulas (2). We can infer that TF-IDF tends to filter out common words and pay attention to the words that appear frequently in the text and are rare in the corpus. We calculate TF-IDF value of each word according to word frequency and combine them to form TF-IDF vector, which can reflect the importance of each word in the sentence. We multiply it with the result of convolution: Ct = TF ∗ IDF ∗ C

(7)

Now, we can tell the neural network which words need more attention. Max pooling and splicing. Finally, we use pooling layer to reduce the complexity of the system, and splice the results of different convolution kernel calculations. A complete sentence vector is now complete.

2.3 Attention Mechanism Multi-Instance Learning usually produces a lot of false data, we need to reduce the impact of noise. In addition, sentences deserve different attention due to its word’s frequency. Based on the problems above, we propose a new attention mechanism improved by TFIDF. The specific framework is shown in Fig. 2. Our attention mechanism is sentencelevel. We creatively calculate the contribution of each sentence to the text through TF-IDF, which means that the model has an initial parameter with physical meaning. In addition, the data oriented by multi-instance learning has many wrong labels, so it is also necessary to reduce the impact of these wrong labels through the attention mechanism. The formula of the attention mechanism is as follows: exp(ei ) β= k exp(ek )

(8)

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Attention Mechanism

softMax

bag features

Align weights: the initial value is TF-IDF value for the sentence

…………

…………

TF-IDF value for the sentence

…………

………… …………

…………

Sentence1 Sentence2 vector vector

Sentence3 vector

Sentence1 Sentence2 Sentence3

Fig. 2. The figure describes our sentence-level attention mechanism. Its initial parameters are calculated by TF-IDF. Its calculation will get the final classification result through SoftMax.

Among them, ei is called a query function, which scores the matching degree between the input sentence xi and the prediction relationship r and its specific expression is as follows, TF-IDF determines its initial value, where A is a weighted diagonal matrix: ei = xi r ∗ TF − IDF ∗ A

(9)

Finally, we define the output of SoftMax according to [13]: exp(or ) p(r|S) = nr k=1 exp(ok )

(10)

Among them, nr is the total number of relations in the corpus and o is the output of neural network.

3 Experiment and Analysis We are committed to the informatization of the medical industry. Therefore, the experiment of this paper is oriented to electronic medical records. Related work is usually based on the English data set. The difference of this paper is that the experiment is carried out for the Chinese text. The data set we use is from Ruijin Hospital in Shanghai. The content of the dataset is diabetes clinic information and related academic papers provided by Ruijin hospital. It has the entity information that has been tagged, so we can extract entity relationship directly. There are ten kinds of entity relations defined in the dataset. Five of them are related to diseases: Test Disease, Symptom_Disease, Treatment_Disease, Drug_Disease, Anatomy_Disease. The remaining categories are related to drugs: Frequency_Drug, Duration_Drug, Amount_Drug, Method_Drug, SideEff_Drug. Our experiment evaluates the model ability by using Precision, Recall and F1, we compare our proposed algorithm with multi-instance learning algorithm (PCNN + ATT),

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machine learning and distant supervision and so on. After that, we analyze the effect of adding TF-IDF weight in neural network and attention mechanism layer respectively. Our experiment adopts the pytorch3.0 framework and calculate by RTX 3080. Table 1 shows our experimental results. Table 1. Performance comparison of different models. Model type

Model

Precision

Recall

F1 score

Training time/h

Non-deep learning algorithm

Template based

0.556

0.529

0.542

0.03

dependency sentence algorithm

0.550

0.526

0.538

0.12

Deep learning algorithm

bootstrap

0.696

0.667

0.681

0.17

LightGBM

0.698

0.648

0.672

0.15

Distant Supervision

0.710

0.684

0.697

0.42

PCNN + ATT without TF-IDF

0.758

0.723

0.740

0.52

PCNN + ATT with TF-IDF in PCNN

0.760

0.731

0.746

0.50

PCNN + ATT with TF-IDF in ATT

0.773

0.733

0.753

0.49

PCNN + ATT with TF-IDF in all

0.774

0.737

0.755

0.49

As you see, adding TF-IDF to the attention mechanism significantly accelerate the convergence speed of the model (about 5%), and adding it to the piecewise convolution network can also improve. We speculate that through TF-IDF, the neural network obtains the initial value closer to the optimal solution, so the step size required by the gradient descent method is shortened. In addition, the model with TF-IDF improves the performance by about 2% compared with the multi example learning method without TF-IDF on F1. The value of adding TF-IDF in the PCNN and attention mechanism layer at the same time does not increase significantly compared with adding in the attention mechanism only. We speculate that it may provide some redundant information to the network, so the convergence speed of the model is not significantly accelerated.

4 Conclusion As your see, TF-IDF help the model achieve better results whether it is added to the PCNN or the sentence-level attention mechanism, and the effect of adding to the attention mechanism is more significant. However, we also note that adding it to the neural network and attention mechanism at the same time it is not as good as adding it alone. In my opinion, because the information of TF-IDF has been transmitted by adding part of the model, the additional modifications may increase the network complexity.

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On the whole, we have proved that better results can be obtained by integrating TF-IDF into convolution network and attention mechanism. Therefore, the traditional text or web algorithm may provide ideas on neural network work, which may be a good research direction in the future.

References 1. Bates, D.W., Ebell, M., Gotlieb, E., Zapp, J., Mullins, H.C.: A proposal for electronic medical records in U.S. primary care. J. Am. Med. Inform. Assoc. 10(1), 1–10 (2003) 2. Chen, Y.: Convolutional neural network for sentence classification. University of Waterloo (2015) 3. Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical methods in Natural Language Processing, pp. 1753–1762 (2015) 4. Kavuluru, R., Rios, A., Lu, Y.: An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artif. Intell. Med. 65(2), 155–166 (2015) 5. Cameron, M., Ray, R., Sabesan, S.: Remote supervision of medical training via videoconference in northern Australia: a qualitative study of the perspectives of supervisors and trainees. BMJ Open 5(3), e006444 (2015) 6. Carbonneau, M.-A., Cheplygina, V., Granger, E., Gagnon, G.: Multiple instance learning: a survey of problem characteristics and applications. Pattern Recogn. 77, 329–353 (2018) 7. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014) 8. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017) 9. Ramos, J.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, issue 1, pp. 29–48 (2003) 10. Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018) 11. Wang, B., Wang, A., Chen, F., Wang, Y., Jay Kuo, C.-C.: Evaluating word embedding models: methods and experimental results. APSIPA Trans. Signal Inform. Process. 8, E19 (2019) 12. Kim, Y.: Convolutional neural networks for sentence classification. arXiv 2014. arXiv preprint arXiv:1408.5882 (2019) 13. Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2124–2133 (2016)

A Deep Learning Algorithm Using Feature Engineering to Adjust Attention Mechanisms and Neural Network for Cloud Security Detection Yiyang Xiong(B) , Yajuan Qiao, Shilei Dong, Xuezhi Zhang, and Hua Tan China Telecom Corporation Limited Research Institute, Beijing, China [email protected]

Abstract. Cloud computing realizes the intensive management of resources and improves the production efficiency, but it also inevitably brings security problems. Cloud security detection technology can be understood as immune cells in the human body, which can protect against all kinds of viruses invading the server. In this article, we transform the virus activity information into time series data, and study how to classify the virus through deep learning algorithm. In our system, we roughly divide the virus activity information into six categories, and calculate the importance of these six types of behavior to virus recognition through feature engineering. Then, we integrate the calculated parameters into the construction of attention mechanism layer and neural network embedding layer to propose a new algorithm based on deep learning architecture. Finally, we verify the accuracy of the new algorithm in virus classification through open source dataset. We compare the performance of this model with logistic regression, support vector machine, random forest model and convolutional neural network. The experimental results show that our model has certain advantages in F1, and improves the performance index by nearly 4%. Keywords: Deep learning · Feature engineering · Attention mechanisms · Cloud security detection

1 Introduction As a new type of information technology infrastructure, cloud computing is gradually becoming the main driving force for the intelligent transformation of large enterprises [1]. It can help users realize intensive management and improve the intelligent level of enterprises [2]. However, although cloud computing can bring efficiency improvement and cost reduction to enterprise customers, the migration of sensitive information such as enterprise data to the cloud will bring new security risks [3]. Therefore, whether it is safe enough has become the core issue for enterprises to choose cloud services. Due to the rapid development of cloud computing, the potential attack threat of cloud platform with more and more important personal and enterprise information is gradually © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 11–18, 2023. https://doi.org/10.1007/978-981-19-9968-0_2

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increasing [4]. On the other hand, the cloud uses a lot of virtualization technology, so the coverage of an attack will be particularly wide. Common attacks include DDoS attacks, backdoor programs, Trojan horses, worms and mining programs that have gradually increased with the development of blockchain in recent years. These viruses will steal computer computing resources or user information and bring irreparable damage. Whether traditional viruses or new viruses, machine learning can achieve good results in identifying their attacks [5]. It can learn its behavior pattern from the activity information of the virus and distinguish it. However, machine learning method belongs to shallow learning, which is very dependent on Feature Engineering, such as feature construction, feature selection and so on. The large-scale virus intrusion scenario in the real network environment is a multi-classification problem with dynamic growth of data sets, so its accuracy is difficult to be guaranteed. Compared with it, deep learning can extract deeper features of the virus through data, so it performs better [6]. In addition, because the virus activity information can be converted into an API call sequence, it is very suitable for the deep learning model that can extract timing information. On the other hand, after the initial brilliance of deep learning, new algorithms are often proposed around the improvement of attention mechanism [7]. The core idea of attention mechanism is to give different weights to different data, which can improve the performance of the model. Therefore, we believe that there may be some combination between the feature engineering of virus and the attention mechanism of neural network. Based on this idea, our algorithm framework first analyzes the types of API called, and roughly divides these APIs into six categories: service, registration, operation process, system information acquisition, network communication and file operation. Then, we calculate the importance of different kinds of behavior to virus discrimination through feature engineering, and integrate it into the model construction through the attention mechanism and embedding of neural network. Our model has achieved better results on these three types of data than other models including deep learning algorithms.

2 Model Architecture 2.1 Overview The overview of the algorithm we proposed is as follows: first, convert the API call information of the virus into text information, and then conduct word embedding coding to convert it into a vector that can be understood by the computer. In the process of word embedding coding, we innovatively give different weights according to the types of API calls and encode them into the word embedding module. Second, after obtaining the word vector, we carry out piecewise convolution, pooling and splicing of the word vector. Then, we extract the features through the random forest model, and obtain the importance of each API call information to virus classification, so as to creatively construct the parameters of attention mechanism. Finally, we send the information adjusted by the attention mechanism to the full connection layer and get the output through SoftMax. After training and adjusting parameters, the model is obtained. Our model can be simply divided into neural network module and attention mechanism module.

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2.2 Neural Network Our neural network consists of three parts: word embedding, convolution, pooling and splicing (see Fig. 1).

Word vector matrix OpenSCManager

convolution kernel is 3

Max Pooling

ӽ

RegSetValue

NetUserEnum

Embedding

GetCurrentProcess

convolution kernel is 4

ӽ

Socket

Max Pooling

Sentence vector

SetFileAttributes Word

Feature importance

convolution kernel is 5

ӽ

Max Pooling

Fig. 1. The structure of the neural network consists of three parts: word embedding, convolution, pooling and splicing. Among them, the blue part of the word vector matrix is the coding of virus importance, which is one of the core innovations of this paper.

Word Embedding. The input of neural network is the API call information of virus. It exists in the form of characters, and the computer cannot understand the connotation of characters. Therefore, we must first convert it into word vector through word embedding. Each word vector can be trained in other corpora in advance, or it can be trained by the network as unknown parameters. Because our text information comes from virus activity information, there is almost no corpus for this kind of problem in academic circles, and there is no available pre training model. Therefore, instead of using the pre training model, we set the random initial value and change it completely through network training. Figure 1 illustrates this process with an example. Each API call of the virus will become a row of the word embedding matrix. Due to the lack of corresponding corpus, the convergence speed of neural network in the early stage will be very slow. Therefore, we explore how to improve the word embedding matrix in the experiment, and try to add some pre training information to make the model understand the current task faster. We innovatively added the importance information of virus into the word vector through feature engineering. Firstly, we divide the virus API into six categories according to expert experience. Then, we and calculate the importance of virus type through random forest model encode it into the word vector. Convolution. In order to obtain the information of word embedding matrix from different angles, we use three kinds of convolution kernels to convolute the matrix respectively. Since each row of the word embedding matrix represents the single active information

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of the virus, the width of our convolution kernel is consistent with that of the matrix, which ensures that the neural network learns in the unit of virus call in a physical sense. Pooling and Splicing. Finally, we pool different convolution results, and then splice them into lower dimensional vectors.

2.3 Attention Mechanism The attention mechanism of deep learning can continuously calculate the semantic code according to the current new vector, which can input different semantic codes at each time. Attention mechanism not only helps neural network solve the problem of word information loss, but also pays more attention to the “key information” in word vector. Our innovation is based on this “key information”. From the point of view of our concerns, although many APIs can be called, we should be able to understand that the calls of some APIs are normal, while the frequent calls of some APIs are very suspicious (such as registration information modification). These suspicious behaviors should have greater attention in neural networks. Figure 2 shows our attention mechanism module framework.

softMax Full connection layer …………

API importance Attention mechanism …………

…………

API classification

Calculate importance through Feature Engineering

…………

…………

API called API called vector1 vector2

API called vector3

random forest

Fig. 2. Our attention frame is sentence level (about 100 activity information performs one attention). Its parameters are calculated by characteristic engineering.

Feature Engineering. Firstly, we divide APIs into six categories: service, registration, operation process, system information acquisition, network communication and file operation. In this way, each API information has a label. Then, we convert all API virus called into six types of labels by setting key value pairs. Finally, we use the random

A Deep Learning Algorithm Using Feature Engineering

15

forest algorithm LightGBM to calculate the importance of these six types of tags for virus classification according to the existing data, and get the value of the importance parameter. Attention Calculation. The calculation of our attention mechanism ci is obtained by the weighted sum of hidden vectors hi :  Tx aij hj (1) ci = j=1

Among them, the weight corresponding to hi can be calculated by the following formula:   exp eij (2) aij = T x k=1 exp(eik ) The eij of formula 2 is obtained by multiplying our API importance matrix W and hj : eij = hi−1 Wi−1 hj

(3)

By multiplying the importance matrix calculated by feature engineering, our attention mechanism can give bigger weight to the API with bigger impact. Our new algorithm reduces information loss and focuses on more important information for sequence prediction. Full Connection and SoftMax. Finally, we send the sentence vector adjusted by attention mechanism into the full connection layer for training, and use SoftMax for normalized output to facilitate the convergence of the model during training.

3 Experiment 3.1 Data Preprocessing We used Alibaba Tianchi’s open source cloud security detection data set in the experiment. The data set is composed of windows executable programs. Its sample data are all from the Internet. The types of malicious files are traditional infectious viruses, DDoS and Trojan viruses. In addition, it also includes mining viruses and extortion viruses that have emerged in recent years. There are 295 kinds of APIs called by viruses in the data. Ideally, we give different weights to all APIs, but this will lead to too complex calculation. To simplify the problem, we first roughly divide the 295 APIs into six categories. As shown in Table 1. We give 2–3 examples for each classification. After dividing the APIs into six categories, in order to make the neural network better understand the differences between different APIs, we use LightGBM random forest algorithm to calculate the importance of these six categories of APIs in virus classification through python, and finally get the results shown in Fig. 3.We can see that registration, operation process and file operation are the categories that have a better impact on virus classification. We record the calculated importance as the label of each API, which lays a foundation for the adjustment of neural network attention mechanism.

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Y. Xiong et al. Table 1. Six different categories and their representative APIs.

Behavior type

API called

Purpose of API

Service behavior

OpenService

Open service

Registration behavior

RegCreateKey

Create or open registry

RegSetValue

Modify registry

Operation process behavior System information acquisition behavior

Network communication behavior

File operation behavior

QueryServiceConfig

Query system service information

CreateProcess

Create process

LoadLibrary

Load dynamic database

GetUserName

Get the name of the current user

GetSystemInfo

Get information about hardware platform

GetHostByName

Obtain IP address through domain name

Send

Send data

WNetAddConnection

Create permanent connection

Socket

Create network communication socket

CreatFile

Creat a file

SetFileAttributes

Modify file properties

Fig. 3. Normalized importance of API for virus classification.

3.2 Test and Result We interpret the API call information labeled by each virus as text and convert it into a word vector. Then we add different coding values to each API according to the divided categories. Then, we perform piecewise convolution and splicing, and construct sentence level attention mechanism in units of 100. The parameters of attention mechanism are

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completely obtained from the API importance obtained in the preprocessing stage. In this way, the importance features obtained by random forest processing affect the processing of neural network from two angles: 1 Word embedding of piecewise convolution neural network. 2. Construct attention mechanism from sentence dimension. We have divided Alibaba’s data set by five-fold cross Validation. It means that we use four parts of them as training each time, and the next part as testing, and then, we average the precision and recall obtained from the four experiments to obtain the experimental results. We then used the same method to compare the innovative model with neural network without adjusting attention mechanism, ordinary piecewise convolution network, traditional random forest model, the result is shown in Fig. 4. The results show that our model achieves better performance than others, and we guess that because deep learning algorithm can capture the timing information of API activities, it performs significantly better than random forest.

Fig. 4. Performance comparison of four different models on F1. It shows the comparison of our model with PCNN (Piecewise convolutional neural network), ATT (attention mechanism) and light BGM (Light Gradient Boosting Machine)

4 Conclusion This paper creatively calculates the importance of virus API activity information to virus classification through feature engineering, and integrates this importance into the construction of neural network attention mechanism and convolution. The method proposed in this paper achieves about 4% improvement over the algorithm without considering

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feature engineering. This method brings a lot of inspiration to similar problems. For example, can we use feature engineering to artificially set the importance of different texts, and then improve the neural network through this importance? In addition, this paper transforms the virus activity information in cloud resources into text information, which also provides a good idea for the expansion of depth learning applications. With the advent of 6G, the society will move towards deeper intelligence, and the problems related to cloud security will become more and more important. This paper does not have a deep understanding of virus activities in cloud servers, and the main feature extraction is realized through the model itself. In the future, we should make better creation according to the knowledge in the field of security.

References 1. Alam, T.: Cloud computing and its role in the information technology. IAIC Trans. Sustain. Dig. Innov. (ITSDI) 1(2), 108–115 (2020) 2. Bello, S.A., et al.: Cloud computing in construction industry: use cases, benefits and challenges. Autom. Constr. 122, 103441 (2021) 3. Kumar, R., Goyal, R.: On cloud security requirements, threats, vulnerabilities and countermeasures: a survey. Comput. Sci. Rev. 33, 1–48 (2019) 4. Ahsan, M.M., Gupta, K.D., Nag, A.K., Poudyal, S., Kouzani, A.Z., Mahmud, M.A.P.: Applications and evaluations of bio-inspired approaches in cloud security: a review. IEEE Access 8, 180799 (2020) 5. Nassif, A.B., Talib, M.A., Nasir, Q., Albadani, H., Dakalbab, F.M.: Machine learning for cloud security: a systematic review. IEEE Access 9, 20717–20735 (2021) 6. Dipendra, R.: A Deep Learning Approach for Intrusion Detection using Recurrent Neural Network. Department of computer Science & information Technology (2018) 7. Niu, Z., Zhong, G., Yu, H.: A review on the attention mechanism of deep learning. Neurocomputing 452, 48–62 (2021)

The Design and Implementation of a Ranging System Based on the DSSS Yingpeng Cao(B) , Fu Li, Shuyi Qin, Wei Zhang, and Zihong Yuan Academy of Space Information Systems, Xian 710100, Shannxi, China [email protected]

Abstract. Along with the high-speed development of navigation technique and the intersatellite networking technology, the improvement of ranging tracking of satellite become more and more important. In the paper, the design and implementation of a ranging system based on the Direct Spread Spectrum System (DSSS)ranging principle was presented. It can achieve stable tracking of satellite under low sensitivity, most Doppler frequency and high dynamic application scenarios. The system has the characteristics of strong extension, and can flexibly configure the symbol rate, and PN code length to achieve different systems. Keywords: DSSS · Ranging · Low sensitivity · High dynamic application · Doppler frequency

1 Introduction Along with the increasingly development of China’s aerospace technology, the Beidou satellite navigation system has basically completed the global networking, and the No. 12 satellite the successfully entered into orbit [1, 2]. These achievements are inseparable from a stable and efficient satellite-to-ground data transmission system [3, 4]. The satellite measurement and control is irreplaceable in the system, because it is the “bridge” which connects the communication between the satellite and the measurement station [5, 6]. Due to the continuous development of industry and information industry, we live in an era of big data, and various advanced systems have higher requirements on the accuracy and security of location information [7–10]. Therefore, it is an urgent problem to improve the accuracy, stability and confidentiality of the satellite measurement and control system. In the measurement and control system, in order to determine the specific position of the satellite at a certain time, it is generally necessary to select a measurement station on the ground to measure the distance between the measurement station and the satellite. If the location of the measurement station is known accurately, the space location of the satellite at this moment can be further calculated under the condition of multi-station co-viewing satellite [11], which can achieve the purpose of positioning and tracking the satellite. So far, there are three main ranging methods: multi-sidetone ranging, tonecode hybrid ranging [12] and pseudo-random code ranging. The pseudo-code ranging system has many advantages, such as strong anti-interference ability, high measurement © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 19–26, 2023. https://doi.org/10.1007/978-981-19-9968-0_3

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accuracy, code division multiple access, etc. And it has been widely used in satellite measurement and control, radar, navigation and other fields [13]. In this paper, the design and implementation of a ranging system based on the Direct Spread Spectrum System (DSSS) ranging principle was presented. From the aspects of ranging principle, PN code selection, implementation of the ranging and despreading and demodulation of spread spectrum ranging signal, the best combination of ranging algorithm performance and engineering realization is achieved. Finally, the navigation, positioning and tracking of satellite in low sensitivity, doppler frequency shift, and high dynamic application scenarios is realized.

2 The Principle of Incoherent Spread Spectrum Ranging In the measurement and control system, the incoherent spread spectrum ranging is realized by two-way pseudo-ranging. After the measurement frame of the ground measurement and control station is coded and spread, the ranging frame is sent to the satellite using the uplink. When the downlink measuring frame is received, the ground station performs despreading, demodulation, frame synchronization, and uses the trailing edge of the downlink ranging frame synchronization to sample the uplink ranging frame formed by itself, and extracts frame count, bit count, PN code period count, Ranging information such as PN code phase and chip phase. The ground station comprehensively calculated the uplink pseudo-range value which were transmitted by the satellite and the downlink pseudo-range value which were measured by the ground station. Then the measurement of the radial distance between the satellite and the ground measurement and control station were completed. Figure 1 is a timing diagram of non-coherent pseudo-code ranging. After the system completed the ranging frame capture and stable tracking, the ground station sent the uplink ranging frame to the satellite at time T1, and the satellite received the frame header of the uplink ranging frame at time T2. T2-T1 is the propagation delay of signal between the ground station and the satellite. The satellite sent the downlink ranging frame to the ground station at T3. T3-T2 was the delay between the satellite receiving the uplink ranging frame and sending the downlink ranging frame. The ground station received the downlink ranging frame at T4. T4-T3 was the signal transmission delay between the satellite and the ground station, and T4-T1 was the delay between the ground station sending the uplink ranging frame and receiving the downlink ranging frame. From the above analysis, it can be seen that the round-trip delay of the signal between the ground station and the satellite is τup + τdown , the transmission speed of the radio frequency signal in space is the speed of light c, which value is 2.99792458 × 108 m/s, from which it can be calculated that the satellite and the ground station. The radial distance R between is 1 (τup + τdown ) × c 2 1 = (τg − τs ) × c 2 1 = (Rg − Rs ) 2

R=

(1)

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The ground transmitter

T3

T2

T1

T4

The instant for transmitting uplink station frame symbols

t The instant for receiving frame symbols

The satellite receiver

up

The instant for sampling on the

satellite

t

s The instant for transmitting downlink frame symbols

The satellite transmitter

21

t down The instant for sampling on the ground station

The ground station receiver

t

g

Fig. 1. The timing diagram of non-coherent pseudo-code ranging

where Rg and Rs are the pseudo-range values measured by the ground station and the satellite respectively.

3 PN Code Selection In the spread spectrum communication ranging system, anti-interference, antiinterception, information and data concealment and confidentiality, anti-multipath interference and anti-fading, multiple access communication, synchronization and acquisition of ranging signals, time measurement, etc. are all closely related to the spread spectrum coding design. Therefore, the characteristic of the PN code is directly related to the performance and realization of the spread spectrum communication ranging system. The PN code sequence has similar correlation characteristics to white noise, which can be copied and stored. And it can be an ideal signal for ranging. PN codes have sequences and various code types. The gold sequences and m sequences are commonly used in spread spectrum ranging systems. They all have good autocorrelation characteristics, cross-correlation characteristics and orthogonality, and the Pseudo – random sequence can be detected under low level conditions. However, the m-sequence has great limitations as address code for code division multiple access due to the small number of m-sequences of the same length. Gold code is a combination code of m-sequence. It returns to two proper m-sequences mod two, the sequences have the same length and rate, but different initial value. The Gold code has good autocorrelation and cross-correlation between the sequences while the number of address code is much larger than the m sequence. Therefore, the PN code sequence ranging code selected in this paper is a Gold sequence, and Fig. 2 shows an example of how Gold code to generate.

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1

M sequence 1

2

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4

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10 Gold code

1

M sequence 2

2

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10

Fig. 2. Illustration of gold code generator

4 Implementation of the Ranging According to the principle of spread spectrum ranging in the first section, the transmission rate of electromagnetic waves is c. To measure the distance from the satellite to the ground station, it is only necessary to measure the delay between the satellite receiving the uplink ranging frame and sending the downlink ranging frame, and the delay between the ground station sending the uplink ranging frame and receiving the downlink ranging frame. According to formula (1), the distance R between the satellite and the ground can be calculated. The delay τs and delay τg are obtained by despreading and demodulating the ranging frame by the satellite and ground receiving equipment respectively. As shown in Fig. 3, the satellite receives the uplink ranging frame header “1ACFD” at time T2, and the satellite sends the downlink ranging frame to the ground station at time T3. At this time, the bit count in the corresponding uplink frame is Bitcnt, and the spread spectrum pseudo code period count is Pncnt, the pseudo code phase is PnPhase, the chip phase is NcoPhase, and the spreading code rate is PnRate. The receiving and transmitting delays at the satellite side can be expressed as:   (2) τs = (Bitcnt ∗ 2 + Pncnt) ∗ 1023 + PnPhase + NcoPhase/216 /PnRate

T3

T2

τs Uplink receiving

The satellite

1ACFD 1ACFD

Downlink transmitting

Fig. 3. Illustration of timing measurement on the satellite

Similarly, as shown in Fig. 4, the ground station sends an uplink ranging frame to the satellite at T1, and the ground station receives the downlink ranging frame header “1ACFD” at T4. At this time, the frame count of the corresponding uplink ranging frame is Frmcntg. The bit count is Bitcntg, the spread spectrum pseudo code period count is

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Pncntg, the pseudo code phase is PnPhaseg, the chip phase is NcoPhaseg, and the spread spectrum code rate is PnRate, then the transmit and receive delays at the ground station can be expressed as:   τg = (Frmcntg ∗ 200 + Bitcntg) ∗ 2 + Pncntg) ∗ 1023] + PnPhaseg + NcoPhaseg/216 /PnRate

(3)

Downlink receiving

1ACFD

The ground station

1ACFD

τg

Uplink transmitting

T4

T1

Fig. 4. Illustration of timing measurement on the ground station

5 Despreading and Demodulation Principle As mentioned above, the spread-spectrum pseudo-code ranging is realized after the system completes the despreading and demodulation of the ranging signal. The model of the direct spread spectrum system is shown in Fig. 5.

m(t) s(t) ModulaModulation tion

Signal Signal source source

c(t) PN-code PN-code generator generator

fc Carrier Carrier generator generator

r(t)

rs0(t)

r0(t) LNA LNA

Mixer Mixer

ar(t) Demodula-tion Demodula-tion

cr(t) fL Receiving Receiving socillation socillation

PN-code PN-code generator generator

Synchroni-zer Synchroni-zer

Fig. 5. The direct spread spectrum system notation

The left side is the transmitting end of the spread spectrum transmission. The source output information code stream is m(t). The spread spectrum code generator generates the PN code sequence c(t). The spread spectrum sequence is modulated by the carrier, and the obtained modulated signal s (t) is: s(t) = m(t) ∗ c(t) ∗ cos(2π fc t + ϕ);

(4)

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The process of despreading and demodulation at the receiving end is to extract the information stream m(t) from s(t) in Eq. (4), and the modulation s(t) in space will generate Doppler frequency shift fd and code offset, then the signal r(t) obtained at the receiving end is: r(t) = m(t) ∗ c(t − τ ) ∗ cos[2π(fc + fd ) + φ]

(5)

The final despreading demodulation output ar(t) is: ar(t) = m(t) ∗ c(t − τ ) ∗ cr (t) ∗ cos[2π(fc + fd )t + φ] ∗ cos(2π fL t + φ  )

(6)

The receiving end adjusts the timing of the synchronizer, so that: c(t − τ ) = cr (t)

(7)

fL = fc + fd

(8)

φ = φ

(9)

Substitute Eq. (7), (8) and (9) into Eq. (6), then: ar(t) =

1 1 m(t) + m(t) cos[4π(fc + fd )t + 2φ] 2 2

(10)

ar(t) is then filtered by a low-pass filter to eliminate high-frequency components to obtain the original information code stream m(t), which completes the despreading and demodulation of the spread spectrum modulation signal s(t). In the whole process of despreading and demodulation, the performance of the synchronization algorithm is directly related to the acquisition and tracking performance of the satellite and ground receivers. This design uses the PMF-FFT two-dimensional search and acquisition algorithm combined with the carrier tracking method of the Frequency Locked Loop (FLL) -assisted Phase Locked Loop (PLL). Reasonabe order and coefficient of the loop filter are designed, and the appropriate loop bandwidth to achieve high-precision tracking performance with a large dynamic range is selected in this model.

6 System Performance Test Verification The performance of the satellite receiver is tested and verified by the ground station spread spectrum ranging simulation equipment. The ground station spread spectrum ranging simulation equipment simulates the ground station to send Doppler frequency offset signals, high Doppler rate of change signals, and extremely low spectral density signals. After testing the performance of the satellite receiver, the satellite receiver can normally capture and track the ranging signal and correctly demodulate the measurement and control data. The detailed performance test data are shown in Table 1.

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Table 1. The performance of Satellite Receiver Input signal power

Frequency offset

Doppler changing rate

Acquisition time

Ranging accuracy

−114 dBm

±800 kHz

21 kHz/s

T_frame_DDS. This method ensures that the CMOS sensor control sequence first enters the SI waiting state. As shown below in the Fig. 3.

Fig. 3. The Operation status of GSENSE2020 CMOS sensor SI waiting stage

The fourth step is each GSENSE2020 CMOS sensor stops the counter in the SI phase and keeps all operation sequences fixed, as shown below in the Fig. 4. When the GSENSE2020 CMOS sensor operation sequence enters the SI state [5], the control software will judge when the TX2 signal is pulled high. When the control software detect the TX2 signal is pulled high, and then it will set stop CNT counter and save current value, the counter will enter the stop state and all operation values will remain constant. The TX2 signal is the exposure time of start signal, the charge in the light sensitive pixel area of the CMOS sensor is cleared when it is pulled up, and the exposure starts when the falling edge of TX2 starts to accumulate charge in the photosensitive area.

Fig. 4. The sequential of TX2 signal in the normal operation and synchronization status operation

The fifth step is each GSENSE2020 CMOS sensor waits for the arrival of the hard trigger pulse in the SI stage, starts the counter and releases the timing control [6]. When a hard trigger pulse is detected When CNT counter will be cleared, the CNT counter will enter the working state, all operation sequences will proceed normally, the TX2 signal will be pulled down to start the exposure, and the exposure synchronization of multiple

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CMOS sensors is realized at this time And at this time, we achieve High speed camera based on multiple GSENSE2020 CMOS sensors. On this basis, the working timing of the GSENSE2020 CMOS sensors is simulated and verified. The simulation program provides reset signal (i_rst_n), clock signal (i_clk) and configuration completion signal (i_config_finish), and generates internal register start signal (o_config_start) and detector reset signal (o_sensor_rst). After the detector is reset for 6 ms, pull it up and wait for 2 ms to start the configuration. The simulation module provides pixel clock (i_pixel_clk), reset signal (i_rst_n), frame request (i_frame_req) signal and exposure time (i_exp_time) signal. The row control module generates o_frame_req and o_texp. As shown below in the Fig. 5 and Fig. 6.

Fig. 5. GSENSE2020 CMOS sensor request exposure simulation result 1

Fig. 6. GSENSE2020 CMOS sensor request exposure simulation result 2

GSENSE2020 CMOS sensor output image data serially through multiple LVDS channels. Here define the function of the clock channels, and what customers should use clock channel for. In case the sensor is not designed with clock channels, then an off-chip generated clock should be used to sample the LVDS data [7]. The phase relationship among different LVDS data channels and clock channel from the sensor can be affected by PCB routing and FPGA synthesis, as shown in Fig. 7.

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Fig. 7. LVDS data and clock channel connection

The purpose of training is to adjust the phase relationship between data channel and sampling clock to ensure correct data acquisition. So we make the simulation program to verify the LVDS data training [8]. During simulation, the test program provides reset signal (i_rst_n), LVDS clock signal (i_clk_ser) of GSENSE2020 CMOS sensor, clock (i_pixel_clk) of output graphics data, and simulated LVDS control signal and data signal of GSENSE2020 CMOS sensor. Observe the output waveform, check the output frame validity, line validity and data timing relationship, and judge the consistency of written and read data. As shown in Fig. 8 below is the simulation diagram of LVDS data receiving module. After receiving LVDS serial data, the output frame is valid, and the line is valid and the data is buffered after data training.

Fig. 8. Simulation results of LVDS data training and receiving.

As shown in Fig. 9 below, the simulation results of parallel data converted from LVDS data after training and caching are written into RAM. The simulation results show that the program runs well [9].

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Fig. 9. The simulation result of LVDS parallel data writing RAM simulation

4 Conclusion For designing high speed camera, The first problem to be solved is the accurate control of the initial exposure of each CMOS sensor. After receiving the external exposure synchronization signal, it can accurately synchronize to each CMOS sensor for starting exposure. Therefore, the method proposed by the invention is to make the CMOS sensor enter the SI state [10] in advance and wait for the synchronization pulse in the SI state. In the SI state, the TX2 signal is the exposure time of the start signal, the charge in the lightsensitive pixel area of CMOS sensors is cleared when it is pulled up, and the exposure starts when the falling edge of TX2 starts to accumulate charge in the photosensitive area. Therefore, the continuous increase of TX2 during the waiting period will not lead to early or delayed exposure. After receiving the synchronization trigger pulse, the exposure will be started. At this time, the synchronization error of N CMOS sensors is within one clock cycle, so the high-precision exposure synchronization is realized.

References 1. Zhao, Q., Li, J., Li, Y., Zhang, X., Zhu, Y., Tan, S.: Study on velocity measurement of light gas projectile based on high speed photographic images. Comput. Measur. Control 29(11), 132–136 (2021) 2. Mihnea, R.-H., et al.: High speed CMOS acquisition system based on FPGA embedded image processing for electro optical measurements. The Rev. Sci. Instrum. 89(6), 065103 (2018) 3. Huang, H.-J.: Method for measuring the attitude angle of object based on two high-speed cameras. Equipment Env. Eng. 18(5), 062–067 (2021) 4. Ni, J.-P., Song, Y.-G., Feng, B.: The principle for measuring motion parameters of projectiles using two sky screens in vertical intersection manner. Opt. Tech. 34(3), 388–390 (2008) 5. Su, Z.L., et al.: Research on the measurement of initial trajectory parameters by using high speed photo-electronic camera. J. Spacecraft TT&C Technol. 22(4), 80–83 (2003) 6. Yang, Z., Wang, L., Zhang, X., Wang, Y.: Position and attitude estimation algorithm and error analysis of high-spin projectile based on dual high-speed cameras intersected. J. Natl. Univ. Defense Technol. 44(2), 71–79 (2022) 7. Ning, Y.H., et al.: High-frame frequency imaging system of large area CMOS image sensor. Opt. Precis. Eng. 27(5), 1167–1177 (2019)

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8. Guo, C., et al.: Test of contact loads during propelle rice milling process. J. Harbin Eng. Univ. 39(7), 1172–1178 (2018) 9. Xu, T., et al.: Method for measuring three dimensional coordinate of near-ground explosion point based on high speed cameras. J. Ordnance Eng. 41(05), 203–206 (2020) 10. Liu, Z., et al.: Research on the method of extracting fragments from velocity measurement image of static explosion testing

Research on Cargo Level Optimization Based on Multi-objective Optimization Algorithm Lin Shi1(B) , Yanan Tan2 , Hongling Chen1 , Wenbo Wan2 , Yunhua Wang3 , and Leichao Ren3 1 Shandong Normal University, Jinan 250358, China

[email protected]

2 Institute of Data Science and Technology, Shandong Normal University, Jinan 250358, China 3 Shandong Port Land-Sea International Logistics Group Co., Ltd., Qingdao, China

Abstract. The accumulation of goods in logistics companies, unreasonable storage, and the tedious process of finding cargo space for storing goods by manual means have caused a waste of space and resources. We propose an algorithm of cargo space optimization for the cargo storage problem in logistics companies. With the most popular multi-objective optimization algorithm as the model, a dual-objective cargo level optimization algorithm is proposed for the objectives of operational efficiency and utilization of cargo level at storage. The experimental results show that the algorithm can effectively recommend a reasonable cargo level. Keywords: Goods storage · Cargo space optimization · Multi-objective optimization algorithm

1 Introduction In today’s highly developed artificial intelligence, it turns out that algorithms and models can solve all kinds of tedious matters to help people, and advanced theories can only be achieved better if the knowledge is applied in practice. There are many optimization problems in the real world, whether, in finance [1], architecture [2], logistics [3], etc., and the application of optimization is becoming more and more widespread. The issues that need to be solved in practical applications are usually multifaceted. All problems cannot be solved at once, requiring multi-objective optimization to coordinate all problems simultaneously. Therefore, multi-optimization algorithms are very suitable and common to solve the warehouse space planning problem [3]. On the one hand, it is often impossible to choose a good storage space for goods storage because of the accumulation of goods in the warehouse, and it is impossible to give an optimal combination when multiple spaces are needed for storage because of the scattered distribution of free spaces, in addition, the time consumed by manual screening is also a significant problem. On the other hand, the multi-objective optimization algorithm has been further developed and paid more and more attention by researchers, and its advantages in solving practical application problems are becoming more and more obvious. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 253–259, 2023. https://doi.org/10.1007/978-981-19-9968-0_30

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In this paper, we propose a bi-objective optimization algorithm for solving the problem of planning the cargo space in a warehouse. After a site visit to the company’s warehouse and operations, we determined two objectives for planning, the efficiency of the operations and the utilization of the warehouse. In addition, the efficiency of the algorithm is further optimized by considering the requirements of the algorithm running time in practical applications. The effectiveness of the algorithm was demonstrated through experiments and practical applications.

2 Literature Review 2.1 Multi-objective Optimization A multi-objective optimization problem can be described as [4], maximize F(x) = (f1 (x), . . . , fm (x))T x ∈ 

(1)

Without loss of generality, we take the maximization problem as an example. The multi-objective optimization problem of maximization can be abstracted into the mathematical model expressed in Formula (1). F(x) in the formula is the objective function value matrix, and f i (x) in the matrix is the function value in the i-th objective direction at x  Ω. x is an n-dimensional candidate solution, and Ω is the space of feasible domains of the independent variables. F(x) is the function mapping relation from the space of independent variables Ω to the objective space. 2.2 Crossover Method The crossover method used in this project is a single-point crossover [5] based on [0,1] encoding. The number N of eligible combinations of cargo spaces planned by the order is randomly generated as an initial population of population size N. Since the number of cargo spaces corresponding to one inter-library in each warehouse should be less than 100, we use an 8-bit binary code to represent a cargo space, and different individuals x to represent different combinations of recommended cargo spaces. For example, we randomly take two individuals x 0 = (0,000,110,000,001,101) and x 1 = (0,010,100,100,110,001) from the population, randomly select the 10th position as the crossover point when crossing over, and swap the chromosomes after the 10th position to get x 0 = (0,000,110,000,110,001) and x 1 = (0,010,100,100,001,101). 2.3 Mutation Mode This project uses random variation [6], for example, the current individual is x = (0,000,000,000,000,000) and changes the binary code of the selected position when the probability factor Q = rand(0,1) > 0.9. Assuming that the 10th position is selected for mutation, when Q = 0.91, the mutated individual x = (0,000,000,001,000,000). After getting the new individual according to every 8-bit binary code as a cargo level to read to get the new cargo level, and then the new cargo level to judge, if the new generated cargo level exceeds the number of cargo levels in the warehouse re-cross variation until the generation of not out of bounds cargo level.

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3 Cargo Space Planning Problem Model 3.1 Problem Description The current logistics company business is developing rapidly, the existing warehouse has a large number of goods, while the amount of goods in and out every day is increasing, so how to find one or several discontinuous cargo spaces in a warehouse to reduce operating time, improve efficiency, and make full use of the existing warehouse space resources has caused a problem. We wanted to improve efficiency and reserve more and larger spaces as backup, while at the same time reducing the time and effort spent on manual searching for spaces, thus greatly improving company efficiency. 3.2 Adaptation Function Design The fitness value [7] is used to measure the merits and demerits of the current offspring. We designed the fitness function as shown in Formula (2) [8]. Under this function, the greater the fitness value is, the better the offspring is. fitness = λ1

i + λ2 U distant

(2)

where fitness [9] denotes the fitness value, λ1 and λ2 denote the weights and λ1 + λ2 = 1, i denotes the number of cargo spaces needed for the current order, and U denotes the utilization rate of the cargo space. We abstract the distance of each cargo space to the door according to the warehouse space planning diagram and use the distance from the cargo space to the door to indicate the time needed to travel to the current space so that taking his inverse is equivalent to describing the efficiency of the operation to this space. As shown in the diagram, the distance from door#1 to the 01 cargo space is 1, the distance to the 02 cargo space is 2, and so on (Fig. 1).

Fig. 1. Warehouse site plan

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3.3 Cargo Space Capacity Formula Design Cargo space capacity refers to the number of goods that can be stored in the designated cargo space. We calculate the number of goods that can be stored according to different cargo Spaces and different sizes of goods, as shown in Formula (3). Count =

maxFloor warehouseLength warehouseWidth ∗ ∗ startFloor + Countk k=startFloor+1 goodsLength goodsWidth (3)

where, warehouseLength indicates the length of the cargo space, warehouseWidth indicates the width of the cargo space, goodsLength indicates the length of the goods, goodsWidth indicates the width of the goods, startFloor indicates the number of palletizing layers, maxFloor indicates the maximum number of layers, Count k indicates the number that can be accommodated in the kth layer. The formula calculates the capacity of a cargo space according to different commodities, which further calculates the utilization rate of the shipping space. 3.4 Algorithm Flow Step 1: Obtain order information, including order quantity, shipment information, etc. Step 2: Get the warehouse information to get all the eligible warehouses Step 3: Calculate the remaining capacity of each warehouse separately by formula (3) Step 4: Crossover and variation Step 5: Calculate the fitness value by formula (2) Step 6: Determine if the iteration termination condition is satisfied, if so, execute the next step, otherwise return to Step 4 Step 7: Output results Our entire algorithm is for new orders, and the algorithm is called only when a new order arrives to plan the cargo space, which includes all the attributes of the cargo (e.g., size of the cargo, palletizing rules for the cargo, quantity of the cargo, etc.). After getting the order we need to plan all the warehouses that can store the goods and calculate the capacity of all the warehouses for the goods in the order. Crossover and variation are performed in the same way as in Sects. 2.2 and 2.3, and then fitness values are calculated to decide whether to update the population or eliminate the generated individuals. Finally, after the iteration termination condition is satisfied, the recommended results are output.

4 Experiment and Analysis To verify the effectiveness of the algorithm, we compare the recommended spaces for the same order before and after optimization. Before optimization, the recommended cargo space is recommended manually, and it is impossible to calculate the utilization rate manually, and only the cargo space with high operational efficiency and empty can be recommended, which is equivalent to the single objective problem of ignoring the utilization rate and considering only the operational efficiency in our algorithm, so we

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Table 1. Recommended space numbers before and after optimization for different quantities of items in the same order. Number of products

Optimize the front cargo position number

100

83

200

83

300

83

400

5, 28, 42, 59, 72

500

5, 28, 42, 59, 72

Operating distance before optimization

Utilization before optimization

Optimized cargo number recommendation

Optimized operating distance

Optimized utilization

3

26%

95

2

27%

3

52%

95

2

54%

3

78%

95

2

81%

67

61%

94

3

99%

67

76%

5, 7, 23, 24, 25, 28

8

40%

600

42, 46, 54, 22 55, 56, 63

43%

5, 7, 13, 25, 28, 30 18

91%

700

18, 23, 33, 47 42, 54, 57, 59

39%

5, 7, 13, 15, 25, 28, 30

22

97%

800

7, 15, 28, 69 33, 34, 46, 57, 59

67%

5, 7, 23, 24, 25, 28, 30

10

61%

900

5, 7, 23, 47 28, 42, 53, 56, 57, 59

42%

5, 7, 23, 24, 25, 28, 30

10

67%

1000

5, 15, 18, 83 23, 28, 34, 53, 55, 57, 59, 63

43%

5, 7, 23, 24, 25, 28, 30

10

74%

1100

5, 15, 18, 83 23, 28, 34, 53, 55, 57, 59, 63

47%

5, 7, 13, 15, 24, 25, 28, 30

24

98%

simulate the manual recommended cargo space with the single objective of operational efficiency. In preparation for the experiment, we obtained a recent order and the loading of the goods at that time. Since a certain number of goods consume a certain amount of time and cost when palletizing, the different distances of handling goods and the distances of different bays reflect the cost to a certain extent. Table 1 shows the recommended bay numbers, operating distances, and utilization rates of all bays before and after optimization for different quantities of goods in the same order. It is obvious from the table that when the number of goods in the order is small, one space can store all the goods, and the manual way will only check the empty spaces and find a space with high operational efficiency, while our optimization algorithm calculates all the empty spaces and unfilled spaces, and fully considers the impact of the two goals

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of space utilization and operational efficiency to recommend a more reasonable space. For example, when the number of goods is 100–300, the utilization rate of recommended spaces is not much different, but the distance of recommended spaces is smaller after optimization. When the number of goods in the order is large, the capacity of some non-empty spaces is larger than the capacity of empty spaces, resulting in the number of spaces recommended by the optimization algorithm being less than the manual recommendation, indicating that it is necessary to calculate the utilization rate of the spaces in the algorithm, which further verifies the effectiveness of the algorithm. In addition, we use all the recommended shipping positions to the warehouse door distance to respond to the cost and efficiency of the operation, the greater the distance, the higher the cost, the lower the efficiency, and vice versa, we need a low-cost and efficient cargo position. As can be seen from the table, the distance of the recommended cargo space after optimization is significantly smaller than the distance of the recommended cargo space before optimization. At the same time, the utilization rate of the cargo space after optimization is significantly higher, reducing the use of the cargo space, thus allowing more goods to be stored and increasing the capacity of the warehouse. We have fully verified the advantages of our optimization algorithm from the above aspects, and at the same time, the algorithm is also recognized by the company’s warehouse managers in practical applications, which further proves the effectiveness of the algorithm.

5 Conclusion This paper focuses on the use of a multi-objective optimization algorithm to solve the problem of storing goods in a warehouse, specifying the coding and the crossover and variation for the cargo spaces in the warehouse. We have used the two most commonly considered and inconvenient trade-off elements in practice as the objectives and designed the fitness function based on these two objectives. In addition, we also designed the goods storage calculation formula based on the actual stacking form of goods in the warehouse and wrote the corresponding algorithms for calculating the storage quantity and the remaining quantity. We have combined the multi-objective optimization algorithm practically with the warehouse cargo storage, which has strengthened the understanding of the algorithm and also improved the application of the algorithm, and we will understand the algorithm more deeply and apply the algorithm to practical use in the future.

References 1. Farag, A., Al-Baiyat, S., Cheng, T.C.: Economic load dispatch multiobjective optimization procedures using linear programming techniques. IEEE Trans. Power Syst. 10(2), 731–738 (1995) 2. Chen, Q., Sun, J., Palade, V., Wu, X., Shi, X.: An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems. Eng. Optim. 54(5), 743–769 (2021). https://doi.org/10.1080/0305215X.2021.1900154 3. Pisacane, O., Potena, D., Antomarioni, S., Bevilacqua, M., Diamantini, C.: Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem. Eng. Optim. 6 (2020)

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4. Qin, C.: Study of distribution center location based on improved particle swarm optimization. In: International Conference on Computer Science and Education. IEEE (2009) 5. Zhang, Q., Hui, L.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008) 6. Li, M., et al.: Optimization and application of single-point crossover and multi-offspring genetic algorithm. Int. J. Hybrid Inf. Technol. 9(1), 1–8 (2016) 7. Calafiore, G.C., Corrado, P.: A variation on a random coordinate minimization method for constrained polynomial optimization. IEEE Control Syst. Lett. PP.3, 1–1 (2018) 8. Xu, H., Xue, B., Zhang, M.: A duplication analysis based evolutionary algorithm for bi-objective feature selection. IEEE Trans. Evol. Comput. PP99, 1–1 (2020) 9. Jin, Y., Olhofer, M., Sendhoff, B.: On evolutionary optimization with approximate fitness functions. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, USA, July 8–12, 2000 Morgan Kaufmann (2000).

The Application of Single Pulse Output Module Integrated by FPGA in On-Board Electronic Products Sun Xiaofeng(B) , Sun Hao, Xing Weiwei, and Wangbo Beijing Institute of Control Engineering, Beijing 100090, China [email protected]

Abstract. This paper introduces the application of single pulse output function integrated by FPGA in on-board electronic products, studies the potential risks caused by its common improper use, and the factors that need to be comprehensively considered in the hardware and software co-design. This paper also puts forward the design and test safeguard measures in engineering application. Keywords: Single pulse output module · FPGA · On-board electronic product

1 Introduction FPGA integrated single pulse output function module is a kind of function module integrated in FPGA chip [1]. It can output a single pulse signal through the specific pin of the chip under the control of application software to meet some functional requirements of electronic products. These functional modules are widely used in various electronic products, including on-board electronic products [2]. However, different application scenarios require different pulse parameters, and the main parameter is the positive pulse width. The positive pulse width is determined by application software settings for two registers in FPGA [3], which are ‘pulse equivalent register’ and ‘positive pulse width register’. Define ‘pulse equivalent register’ as letter U, ‘positive pulse width register’ as letter N, and the theoretical width of positive pulse output as P, there is a formula as follows: P=U×N Assuming that the pulse equivalent is set to 10 us and the positive pulse width is set to 10, the output theoretical width is 10 us × 10 = 100 us.

2 Common Problems in Practical Applications In practical applications, many designers will adopt a simpler setting method, positive pulse width register is often set to 1 and pulse equivalents register is set directly to the required theoretical output width [4]. The advantage of this setting method is that © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 260–266, 2023. https://doi.org/10.1007/978-981-19-9968-0_31

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only one register parameter needs to be simply calculated. However, the simpler setting method has certain risks, because in this method, the actual width of the positive pulse output is not exactly the same as the theoretical value, there will be some deviation [5]. The specific deviation is determined by the time of the software’s writing operation, as shown in Fig. 1.

100us TICK 100us

s

START_TICK1 of software writing operation positive pulse of START_TICK1

100us

START_TICK2 of software writing operation

s

positive pulse of START_TICK2

4us

START_TICK3 of software writing operation positive pulse of START_TICK3

s

50ns

Fig. 1. Diagram of pulse signal equivalent deviation

The phase of the positive pulse width register relative to the reference clock directly affects the actual width of the output pulse. The value written to the register at a certain time is 1, then the initial value of the FPGA chip counter is 1, and the pulse equivalent is 100 us. After the counter is enabled, the pulse begins to output. When the rising edge of the next 100 us pulse equivalent comes, the counter is reduced by 1. Once it is reduced to 0, the pulse stops output [6]. In the above figure, time 1 is at the beginning of the cycle of 100 us pulse equivalent, and the time from this time to the rising edge of the next pulse equivalent is 100 us, so the generated pulse width is about 100 us; similarly, the pulse width generated by time 2 is about 4 us, while the pulse width generated by time 3 is about 50ns. In summary, the deviation between the actual output pulse width and the theoretical pulse width is 0 ~ 1 equivalent clock [7]. When the deviation is too large and the actual output width is too narrow, the signal generally cannot meet the requirements of subsequent circuit, which will lead to product’s function failure.

3 The Concealment of the Problem According to the principle of pulse signal output deviation, the actual output pulse width should be a random value within an equivalent, and the failure of product function should

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also occur randomly. But this is not the case in the actual engineering tests, most products are normal and stable. Further research shows that the phase relationship between the writing time of the FPGA register and the equivalent reference clock is basically stable. This is because the control period signal of the application software often comes from the same FPGA time module, which has a common basic clock [8], as shown in Fig. 2 below.

1us tick module 32ms tick module 100us tick module

Fig. 2. Relationship between software control period and 100 us Tick equivalent pulse

The control period of the application software is usually in ‘ms’, and there is the following two-level synchronization relationship between control period and the pulse width equivalent in FPGA: 1. The two signals come from a common base clock ( 1 us Tick in the figure) and share integer multiples relationship; 2. Time slice assignment and task partition of application software in each control period are fixed, and the write operation of the pulse register is executed at a fixed offset time of each control period. Taking a 32 ms control cycle as an example, the diagram is shown in Fig. 3 below.

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0

ΔT

32

263

t(ms)

32ms control period Fig. 3. Software task scheduling process in each control period

Based on the above synchronization relationship, and since the control period T(32 ms) is an integer multiple of the equivalent pulse U(100 us), the starting time of each control period corresponds to the starting time of the equivalent pulse. Under the normal operation of the application software, the phase relationship between the writing time of the FPGA pulse width register and the 100 us equivalent pulse is fixed, and the actual pulse width output by FPGA is also fixed and stable [9]. Define the offset of START_TICK’ writing time as T, as shown in Fig. 4, and suppose its value is 5.66 ms, the positive pulse width P triggered by this time is: P = U − (T mod U) Import data calculated as: P = 100 us-(5.66 ms mod 100 us) = 40 us. So, the actual positive pulse width will be 40 us stably. Since most products have a derating design on the signal width requirement, the signal width may fail only if the deviation is large (as shown in time 3 in Fig. 1). Therefore, the functions of most products in this case are not abnormal (as shown in

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T(Control Period) T

T

U Fig. 4. Corresponding relationship of control period and the equivalent pulse

time 1 and time 2 in Fig. 1). This kind of problem has great concealment in practical application, and it will also bring difficulties to fault location and investigation.

4 Design Optimization Scheme and Verification In response to the problem, the best solution is to take design measures. First, the ‘positive pulse width register’ is set to no less than 2, Second, more appropriate pulse equivalent parameters need to be reselected to ensure that the minimum output width of the positive pulse still meets the application requirements. In addition, the above settings need to consider the following factors: 1. FPGA chips usually need to output multiple single pulse signals simultaneously for different application objects. Although the width requirements of each signal are different, they usually need to share the same pulse equivalent, so the equivalent must meet the requirements of all signals; 2. The actual output width of each signal should not be too large, otherwise the offset between the signal trailing edge (descent edge) and the writing operation time of the application software will be too large, unknown risk may be introduced to the circuit using the signal trailing edge. Taking the case in Fig. 1 as an example, the ‘positive pulse width register’ is changed to 10, and the ‘pulse equivalent register’ remains the original set to 100 (us). Retest and grasp the measured waveform, the actual pulse width is between 900.2 us and 1000 us, as shown in Fig. 5 and Fig. 6, channel1 waveform. This change resulted in a delay of about 900 us in the trailing edge of the pulse compared to that before. After analysis, it still meets the requirements.

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Fig. 5. Maximum pulse width after Reset(1000 us)

Fig. 6. Minimum pulse width after Reset(900 us)

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The research experience has been widely used in on-board electronic products engineering, and is implemented in the design specifications and detection specifications of application software. For product testing positions, it is also recommended to pay attention to the deviation between the actual measurement pulse width and the set value of such signals. The single pulse signals whose measured deviation is greater or less than one reference pulse width should be judged as unqualified, contrarily, product’s function correctness cannot be simply used as the criterion.

5 Conclusion Based on the application scenarios of on-board electronic products, this paper has carried out a useful exploration on the specific application method of single pulse output function integrated by FPGA. The optimization scheme and safeguard measures are put forward in the aspects of software design and test detection combined with engineering practice, and have been implemented in the design and test specifications of on-board electronic products, and good benefits have been achieved [10]. The research experience can be applied to many kinds of electronic product lines, including aerospace electronics industry [11], it is especially helpful for the high quality development of various batch-produced products.

References 1. Liu, G., Han, H.: Design and implement on monopulse radar signal preprocessing system base on FPGA. Transactions of Beijing Institute of Technology 38(7) (2018) 2. Xing, Z., Shi, M.: Elimination buffeting of keystroke and single pulse generator circuit in FPGA development process. Modern Electronics Technique (21) (2009) 3. Jiang, J.: Design and realization of a radar target tracking algorithm based on FPGA+ DSP architecture. Shipboard Electronic Countermeasure 43(3) (2020) 4. He, H., Chao, Y., Wang, Z.: Optimization of the transmitting circuit of acoustic remote detecting tool. Applied Acoustics 37(2) (2018) 5. Zhang, S., Sun, J., Jiang, T.: Research and implementation of SHEPWM based on DSP-FPGA architecture. Electric Drive 48(11) (2018) 6. Wang, M., Kang, Y., Ye, Z.: Multichannel thickness measurement system of electromagnetic ultrasonic. Instrument Technique and Sensor (6) (2015) 7. Long, H.: Optimized design and implementation of real-time DOA estimation technique for pulse. Telecommunication Engineering 52(9) (2012) 8. Xing, B., Wang, F., Fan, J.: Design of waveform acquisition for the pulsed X-ray and dose analysis system. Nuclear Electronics & Detection Technology 36(3) (2016) 9. Cui, Y., Zhao, S., Zhou, W.: Design of satellite-based integrated electronic system with high performance. Modern Electronics Technique 43(12) (2020) 10. Li, L., Qiao, D., He, S.: Design and implementation of universal software test platform for on-board computer. Microelectronics & Computer 36(3) (2019) 11. Zhang, X., Zhou, X., Jiang, G.: The analysis and discussion on electromagnetic interference of satellite-borne electronic equipment. Aerospace Control 32(4) (2014)

Design of Steady Speed Control System for Space Borne Scanning Loads Yong Zhou1(B) , Jie Pang1 , Fuqiang Liu2 , Chao Ma1 , and Chao Dong1 1 China Academy of Space Technology (Xi’an), Xi’an 710100, China

[email protected] 2 Institute of Remote Sensing Satellite, Beijing 100081, China

Abstract. To avoid affecting the satellite attitude stability, the speed ripple of rotating payloads such as microwave radiometer and microwave scattermeter will be limited. The speed ripple shall be ±0.05°/s (3σ) or less. The causes for the development of speed ripple are presented and analyzed, such as cogging torque, angle sensor accuracy, quantization error of speed and load disturbance torque. The capital speed ripple suppression methods are analyzed. Firstly, permanent magnet synchronous motor (PMSM) using vector control technique is used to drive directly rotating payloads, which can drive load smoothly. Secondly, cogging torque of motor is restrained with machine design to reduce torque ripple. Thirdly, quantization error of speed is eliminated with tracing filter. Fourthly, the load disturbance torque coming from slip ring and bearing friction is compensated with active disturbance rejection controller (ADRC). The control system has been simulated to verify disturbance rejection capability. After long term test on orbit, the speed ripple is reduced to 0.05°/s. This scheme is proved to be feasible. Keywords: Speed ripple · Torque ripple · Tracing filter · ADRC

1 Introduction In recent years, the sensors with big rotary inertia on remote sensing satellite are applied increasingly. In some application, the rotary inertia of sensor is bigger than the rotary inertia of satellite. It brings the influence of speed ripple to satellite attitude. For example, the microwave radiometer and microwave scatterometer are important remote sensing instrument on satellite. Both instruments terminal detect continuously all weather and all time sea surface wind field, sea surface height and sea surface temperature. Generally, the attitude control system of satellite acquires attitude speeds by inertial attitude sensors. The disturbance torque produced by sensors while rotating is eliminated by momentum wheels. If the speed ripple caused by the sensors is worse, the momentum wheels are incapable of action. In this moment, satellite engine is working to adjust attitude. It will consume fuel to decrease satellite life [1–4]. Thus, it is demand that speed ripple of the two sensors is ±0.05°/s or less while scanning at the rated speed. We determined the factors influencing speed ripple. The PMSM is designed for low torque ripple. The tracing filter is used to eliminate quantization error of speed. The ADRC is used to compensate the torque fluctuation of slip ring and bearing. The model of system and the test on orbit verify the validity of project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 267–275, 2023. https://doi.org/10.1007/978-981-19-9968-0_32

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2 Design of System In order to achieve rigid coupling between motor and load, the direct driver is used. Thus, Response ability is enhanced. The driver unit of scanning equipment (shown in Fig. 1) includes the PMSM with external rotor, two-speed resolver, slip ring and bear, all mounted to the central shaft of the scanning equipment. The resolver is used to get the position of the scanning equipment. The ring is used to transfer signal from extravehicular to intra-satellite. During scanning, the central shaft of the scanning equipment is fixed, and motor drives the housing to rotary. Thus, the antenna and high frequency equipment case mounted on the housing are droved to rotary. During the assembly of slip ring, contact pressure of brush probe is adjusted to the correct contact resistance. If the brush contact resistance is large, then friction becomes greater, which life cycle of slip ring is shortened. In addition, friction of slip ring will cause speed ripple. To achieve high precise, the fieldoriented control of PMSM is required. The rotor position is measured by resolver with 32 polar, which error is 30 .

vector control

Structured Model of PMSM

VDC

Id*=0 Speed

PI ADR C

Vd

Va ab/dq

Iq*

PI

Vq

Vb

A

dq/ab

Fig. 1. Driver unit

A/D B

Angle

speed

B

Resolver

C

Vc

Id Iq

A Invert

SVPWM

Current Sensor

RDC

coarse fine

Fig. 2. Control system

3 Factors of Influencing Speed Ripple 3.1 Design of Motor with Minimized Torque Ripple In order to achieve minimized speed ripple, it is chief to design the motor with minimized torque ripple. The motor produces torque ripple at low speed, it can worse speed ripple. The cogging torque and processing technology determine the torque ripple of motor. General, the torque ripple of PMSM for high accuracy application is required less than 3%. The torque ripple due to electromagnetic design is less than 1%, the other torque ripple is caused by processing technology. In PMSM design, Low-order cogging torque can be eliminated via fractional-slot concentrated winding. While the dimension of motor is fixed, it can eliminate cogging torque by increasing the number of stator slot (Z), but it can not to be increased without limit. Accordingly, it is important to design the ratio of slots and poles (Z/2p) reasonably. The number of stator slot is 60, the number of pole pairs is 64. The lowest order of cogging

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torque is 16, the number of harmonics is 960. With the number of harmonics increasing, cogging torque will be eliminated. Furthermore, it can eliminate cogging torque with designing the shape of magnets, pole-arc coefficient and slot opening reasonably. The amplitude of cogging torque can be eliminated greatly via to design the structure of motor. Whereas torque ripple caused by manufacture process and assembly technology is greater than torque ripple caused by cogging torque. For instance, dimensional and geometric tolerances of the core plate of motor affect greatly the performance of motor. The coaxial error of the motor can affect the equality of the air gap between stator and rotor, which can result in air-gap reluctance varying. Thus, it is essential to eliminate torque ripple that manufacture process and assembly requirement are controlled strictly [5]. 3.2 Analysis of Speed Quantization Error Position sensor gives limited quantization discreet value, controller gets position with equal-interval sampling, speed can be get by means of difference calculation which can cause resolution noise. The resolution noise produces limit cycle possibly. Generally, low pass filter is used to eliminate resolution noise. Low pass filter eliminates bad result which is caused by resolution. However, low pass filter brings phase lag to control system, which affects dynamic performance and decreases stability margin [6]. The resolution of rotary encoders is 0.005 493 164°, rated speed is 100°/s. Firstly, error which fixed time sampling brings is analyzed. Sampling period is 1 ms, quantization error brings 6% to 7% error. Tracing filter is constant gain filter. It is used to eliminate quantization error of encoders, with occupy few memories and having no use for measurement noise and state noise. The tracing filter enhances the resolution of speed, it eliminates the speed ripple caused by quantization error, and it brings less phase lag. The tracing filter is  ˆ θˆ (k + 1) = θ(k) + α[θm (k + 1) − θ (k + 1)] (1) ˆθ˙ (k + 1) = θˆ˙ (k) + β [θ (k + 1) − θ (k + 1)] m T

T is sampling time. θm (k +1) is coming from angle sensor, it contains noise. At Tk+1 , ˆ +1) and θˆ˙ (k +1) are correct by prediction error θm (k +1)−θ (k +1). estimated value θ(k α and β are parameters of filter. α = 0.3 ∼ 0.5, β =

α2 1−α

(2)

4 Design of Algorithm 4.1 Modeling of PMSM Current Loop The idea of current loop is to follow the input value quickly. The resistance and inductance of motor can be obtained accurately. The power of motor is coming from DC/DC, so the voltage fluctuation is ignored. Thus, PI controller is applied to the current loop.

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1. Model of Motor The control trajectory of PMSM is id = 0. It eliminates the couple between ω • iq and ω • id , then decouples q axis voltage uq and d axis voltage ud . Thus PMSM servo can be simplified to DC servo to be controlled. The voltage equation of PMSM is modeled as follows. diq + ω · p · ψf uq = R · iq + L (3) dt where p is the number of pole pairs. ψf is permanent magnet flux. iq is torque current. id is field current. R is resistance of motor winding. L is inductance of motor winding. Generally, back electromotive force (EMF) interfere current loop in forward channel. Because electromagnetic time is less than mechanical time, feedback can overcome the interference. In sample time, the variation of back EMF is constant interference, the value is less than DC voltage. In transient process of motor current, back EMF can be ignored. So, ω · p · ψf ≈ 0. 2. Design of Current Loop The plant of current loop is cascade structure of two first-order inertial elements, which are PWM invert and motor winding. The current loop structure is shown in Fig. 3. Controller is PI. The zero of PI is used to cancel the pole of motor winding. The open loop transfer of current loop is corrected to typical I model system. The open loop transfer is (

Kp s + Ki β · KPWM 1 K )( )( )= s T + 1 Ls + R s(Ts + 1)

(4)

Based on second order optimization principle, K * T = 0.5, Kp and Ki are Kp =

L R Ki = 2β · T · KPWM 2β · T · KPWM

v*

K p s Ki

iqref -

s

K PWM Ts 1

TD

v1

NLSEF

u0

(5)

iq*

1/b

PMSM Modle

v

1 Ls R

a(t)

b z2 z1

Fig. 3. The block diagram of current loop

ESO

Fig. 4. One order ADRC block diagram

where iqref is output of speed loop. Kp is proportion coefficient of PI. Ki is integral coefficient of PI. β is gain of current filter. KPWM is gain of PWM invert. T is the sum of PWM invert delay time and current filter delay time. 4.2 Modeling of PMSM Speed Loop The idea of speed loop is to suppress disturbance coming from load inertia and slip ring friction torque. The model of slip ring friction torque is difficult to obtain. So, we select ADRC in speed loop.

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1. Analyzing Disturbance The motion equation of PMSM is modeled as follows. dω B · ω Ke · iqref TL =− + − (6) dt J J J where ω is speed of motor. J is inertia of load. B is friction coefficient. Ke is torque coefficient. TL is load torque. ω˙ = w(t) + B · iqref

(7)

Ke B · ω TL − , b= (8) J J J w(t) is the disturbance coming from TL , J , B. 2. ADRC Model One order ADRC is applied to speed loop. ADRC consists of tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback control law (NLSEF). The controller structure is shown in Fig. 4. One-order ADRC system can be expressed as follows [7–9]. TD:  e0 = v1 − v∗ (9) v˙ 1 = −r · fal(e0 , a0 , δ0 ) w(t) = −

ESO:

NLSEF:

⎧ ⎨ e1 = z1 − v z˙ = z2 − β01 · fal(e1 , a1 , δ1 ) + b · u ⎩ 1 z˙2 = −β02 · fal(e1 , a1 , δ1 ) 

e2 = v1 − z1 u0 = −β1 · fal(e2 , a2 , δ2 )

(10)

(11)

Output after disturbance compensation: iq∗ = u0 −

z2 b

(12)

where v∗ is input speed, v1 is tracking signal of v∗ . v is feedback speed. r is parameter of TD. β01 and β02 are feedback gain of state error in ESO, both amount to integration of PID. z2 is estimated value of system disturbance. b is disturbance compensation, it is parameter related to controlled plant. β1 is control gain of NLSEF, it amounts to differentiation of PID. Nonlinear function fal(e, a, δ) is expressed as follows.  a |e| sgn(e), |e| > δ fal(e, a, δ) = (13) e/δ 1−a , |e| ≤ δ where, a, δ are no needed to adjust after selected. Nonlinear function fal(e, a, δ) is selection experience of control engineering. The function has mathematical characteristics with big gain and small error, small gain and big error.

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5 Simulation and Test Results 1. Simulation Results The section presents the simulation results with simulink. The model is built based on Fig. 2. Simulation parameters of servo are presented in Table 1. Table 1. Simulation parameters Parameters

Value

Motor Pole

32

Supply Voltage

+28 V

Inertia of Load

170 kg · m2

Output Torque

2 Nm

Resistance of Motor Winding

4

Inductance of Motor Winding

10 mH

Torque Coefficient

2.1 N · m/A

Sampling Frequency of Current Loop

50 μs

Sampling Frequency of Speed Loop

1 ms

Resolution of Angle

16 bit

Rated Speed

100º/s

Because the speed ripple of the load is the main factor affecting the attitude stability of the satellite, the step disturb torque is applied to the load, the speed response with different disturb torque shows as Fig. 5. When the disturb torque is less than the output torque of motor which is 2 N · m, the current speed can be adjust to the rated speed with small speed drop. The speed drop has little impact on the satellite attitude. 2. Results on orbit The control system has been successfully applied to the radar payload of a remote sensing satellite. The inertia of load is 170 kg · m2 , the inertia of satellite is 200 kg · m2 . The load works continuously for 24 h. So, the influence of the speed ripple of the load on the satellite attitude is quite critical. The satellite has been in orbit for more than 3 years. Figure 6 shows the q axis current on orbit in three years. The friction resistance moment of scanning equipment consist of grease lubricated bearing and dry friction of slip ring. The q axis current represents the change of friction resistance moment.

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The preload and grease viscosity of bearing increase at low temperature to increase friction resistance moment [10–12]. It results q axis current to increase. Figure 7 shows speed ripple on orbit in three years.

Fig. 5. Speed response with different disturb torque

Fig. 6. q Axis current curve on orbit

The q axis current represents load torque. As Fig. 6 and Fig. 7 shown, although the load torque ripple is great, the speed ripple still is less than 0.05%.

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Fig. 7. Speed ripple on orbit

6 Conclusion The steady speed control system for space borne scanning loads was designed, analyzed, and its performance was verified through simulations and test on orbit. This technique has been used on a number of satellites. These payloads are working goodly on orbit. Due to the limitation of position sensor accuracy, the speed stability has reached limit. If higher steady speed is need, higher accuracy must be used.

References 1. Lu, L.Q., Weng, Y.H., Shi, Y.Q.: Analysis on satellite attitude affected by microwave radiometer imager scanning and it’s control realization. Aerosp. Shanghai 28(5), 59–62 (2011) 2. Wang, X.M., Chu, Y.H., Zhang, J.L.: On the influence of rotatable payload and control for satellite attitude. Aerosp. Control Appl. 42(5), 14–18 (2016) 3. Liu, F.C., Zhu, D.F., Song, T.: High precision attitude compound control for a satellite with large inertial moving parts. Aerosp. Shanghai 33(6), 53–60 (2016) 4. Lu, D.N., Guo, C.Y., Wang, S.Y.: A disturbance mitigation method for moving appendages on spacecraft. Chin. Space Sci. Technol. 40(5), 26–33 (2022) 5. Zhang, C., Gao, Q., Ren, H.C.: Overview of the influence of cogging torque in permanent magnet motors. Electr. Eng. Mater. 2, 23–30 (2022) 6. Ellis, G.: Control System Design Guide, 4th edn. Elsevier Inc., USA (2012) 7. Guo, B.L., Bacha, S., Alamir, M.: A review on ADRC based PMSM control designs. In: 43rd Annual Conference of the IEEE Industrial Electronics Society, America, pp. 1747–1753. IEEE (2017) 8. Huang, Y.S., Zou, J.M., Wang, M.H.: Active disturbances rejection controller for position servo control of PMSM. In: 22nd International Conference on Electrical Machines and Systems, China (2019) 9. Liao, Z.L., Zhao, Q.J., Zhang, X.X.: Improved permanent magnet synchronous motor control system based on position sensorless technology. In: 2nd IEEE Advanced in Formation Management, Communicates, Electronic and Automation Control Conference, America, pp. 2124–2128. IEEE (2018) 10. Li, C., Wangx, Q., Dai, F.: The debris characteristics and reliability design of aerospace slip ring. Electron. Prod. Reliab. Environ. Test. 36(2), 1–5 (2018)

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11. Jia, Q.J., Yaop: Friction torque analysis of space conductive slip ring under vacuum and high and low temperature environment. Lubr. Eng. 45(7), 123–140 (2020) 12. Du, F.J., Li, P.H.: Effect of material expansion coefficient and lubrication on telescope load torque under low temperature. Opt. Precis. Eng. 26(3), 616–623 (2018)

Research on Slotting Optimization Based on MOEA/D Hongling Chen1(B) , Yanyan Tan1,2 , Lin Shi1 , Wenbo Wan1 , Leichao Ren3 , and Yunhua Wang3 1 School of Information Science and Engineering, Shandong Normal University, Jinan, China

[email protected]

2 Institute of Data Science and Technology, Shandong Normal University, Jinan, China 3 Shandong Port Land-Sea International Logistics Group Co., Ltd., Qingdao, China

Abstract. Decomposition-based multi-objective optimization algorithms decompose a complex multi-objective optimization problem into several simple singleobjective optimization problems using a set of different decomposition method and solve these single-objective sub-problems simultaneously. It has outstanding performance in solving multi-objective optimization problems, so this paper propose a method to optimize the slotting optimization problem using MOEA/D. However, in evolutionary algorithms, the selection of operators directly determines the performance of the algorithm, and since the population has different characteristics at different evolutionary stages, the evolutionary process is divided into five stages to verify the effects of different operators on the performance of the algorithm, so as to select a suitable operator. The improvement of the evolutionary operator selection strategy further accelerates the convergence speed without affecting the convergence accuracy. We have verified the performance of the algorithm on the slotting optimization problem, and through many comparative experiments we can find that the proposed algorithm has better performance in searching the global optimal solution and running time. Keywords: Local search · Slotting optimization · Operator selection

1 Introduction The fundamental purpose of logistics management in enterprises is to ensure the normal operation of the capital flow chain, reduce production and operation costs, and thus maximize benefits [1], so logistics management occupies an important position in enterprise management. The adoption of modern management concepts not only realizes the automated management of enterprise logistics, but also effectively increases economic profits. Therefore, in modern logistics activities, it is crucial to update the concept and methods of logistics management. With the expansion of the enterprise scale, the goods in the warehouse will be more and more, and the proportion of the cost of warehouse management will be larger and larger. The ensuing problem is how to assign a reasonable location to these goods and reduce the cost of warehouse management [2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 276–283, 2023. https://doi.org/10.1007/978-981-19-9968-0_33

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No effective optimization of warehouse space is impossible to improve the effectiveness of warehouse management, the reasonable use of warehouse space can reduce unnecessary losses due to confusion in the layout of the warehouse [3]. For example, reduce the space wasted by the goods, save warehouse operation time, etc. [1]. For some small-scale problems, satisfactory solutions can be found with experience and simple rules, but for some large-scale problems, it is difficult to obtain satisfactory solutions, so many scholars have proposed some improved algorithms to solve the cargo space optimization problem [4]. For example, Lee et al. [5] used a heuristic algorithm to solve the outbound and inbound optimal picking path problem of a square stereo warehouse. Kim et al. [6, 7] used a clustering algorithm combined with a sorting algorithm applied to the job picking problem of a stereo warehouse to optimize the shortest path picking path. MOEA/D [8] has been widely studied since it was proposed by Zhang and Li. In MOEA/D, a decomposition method is required to transform the multi-objective optimization problem is transformed into multiple scalar optimization problems [9]. It has significant advantages in the performance of diversity and convergence of equilibrium solutions, so we propose to use a new approache to optimize the storage location problem. MOEA/D simplifies the complex problem and decomposes the problem with multiple objectives into multiple single-objective problems. According to the importance of each objective, different weights are assigned to them, and solve these single-objective subproblems simultaneously [10]. Then a new generation of individuals is generated by replacing the solutions in the neighborhood of that subproblem based on the aggregation function. MOEA/D has significant advantages in balancing solution diversity and convergence, and therefore we propose to use MOEA/D to solve the reservoir optimization problem.

2 Overview of Multi-objective Slotting Optimization Problem Although warehousing is an important part of logistics, but the cost of warehousing and other factors in logistics, let’s say there is a contradictory relationship between transportation and distribution costs, so in order to reduce the cost of warehousing must not decline the overall quality of corporate services as a condition, the most common warehousing cost control measures are the following: • Reducing warehouse operation time According to the storage time of the goods, they will be discharged in the order of the time out of the warehouse, especially for goods with short storage time, thus reducing transportation costs. • Improve the warehouse space occupancy rate Improve the space occupancy rate of warehouse that is to improve the storage density, by improving the utilization rate of the unit storage area to reduce storage costs, such as the use of heavy-duty shelves, you can appropriately increase the storage height, within a reasonable range, reduce the library channel or use three column shelves and other methods.

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• Improve the degree of standardization of storage operations The use of advanced forms of processing such as zero inventory for warehousing processing to ensure the accuracy and minimization of inventory quantities. • Optimize warehouse layout Inappropriate planning will slow down the flow of goods and information, thus increasing operating costs. In the logistics costs, warehousing and transportation accounted for the largest proportion, so effective planning within the warehouse is very important. The basic properties of warehouses and commodities determine the adjustment of slotting for example, because of the short storage time of some goods, we will place them in the interim warehouse. For ordinary goods, according to the load-bearing level of goods, the goods should be stacked high discharge, so as to ensure the stability of the goods. For certain commodities that need shelves, if there is a serious imbalance such as top-heavy and bottom-light shelves, it may lead to deformation or even collapse of the shelves. Therefore, follow the principle of placing heavy goods at the bottom to ensure the stability of the shelves. The problem of multi-objective slotting optimization is to dynamically allocate the storage space for goods in the warehouse based on the basic properties of the warehouse and goods and unanticipated change factors. In order to ensure that the warehouse is in an effective operation state, ensure that the location distribution in a more reasonable state, so as to reduce the warehouse operation time, improve the storage density. When the objectives that want to be optimized reach more than one, the multi-objective warehouse space optimization problem is defined in the following form: maxmizeF(x) = (f1 (x), f2 (x), . . . , fm (x)) ∈ Rm

(1)

Subjecttox ∈  fi (x) is the sub-objective function, these sub-objectives can be the most efficient use of storage capacity, the most efficient use of warehouse equipment, the most reasonable allocation of available manpower, the stability of goods, the minimum warehouse operating time.  is the decision space (the set of available storage spaces), F(x) is the mapping of the decision space to the objective spaces. When a new order is put into the available warehouse space, it is placed on all possible warehouse spaces, each with different possible capacities, different stability values, different warehouse operation times, etc. The goal of optimization is to maximize each sub-goal.

3 Algorithm Description The multi-objective optimization algorithm based on decomposition(MOEA/D) simplifies the complex problem and decomposes the problem with multiple objectives into multiple single-objective problems. According to the importance of each objective, different weights are assigned to them, and solve these single-objective subproblems simultaneously. In the evolution process, we take the initial population as the parent population,

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and generate the offspring individuals through the evolutionary method. In the process of optimization, the individuals of each generation in the population are the current optimal solutions, and the population is the set of optimal solutions of each sub-problem selected from several generations of evolution. For two neighboring subproblems, the optimal solutions should be very similar. So, for each subproblem, it is simply optimized with the information from its neighboring subproblems. The algorithm can directly adopt the fitness allocation and diversity preservation strategies used in evolutionary algorithms for solving single-objective optimization problems. When solving multi-objective optimization problems, the decomposition method we most often use are: Weighted sum approach [4], Tchebycheff approach [4] and Boundary intersection approach [4]. In this paper, the slotting optimization problem is break up into multiple single-objective problems mainly using the weighted sum method. Let λ1 , λ2 , . . . , λN be a uniformly distributed set of weight vectors, and the decomposition of the slotting optimization problem using this method is described in the following form: m    g ws x|λi = λij fi (x)

(2)

j=1

T  where λi = λi1 , λi2 , . . . , λim , let λ = (λ1 , λ2 , . . . , λm ) be a weight vector and for m  λj = 1. MOEA/D optimizes these N subproblems all j = 1, 2, . . . , m, λj ≥ 0, and j=1

simultaneously in one evolutionary process. This approach allows users to specify their desired area by a reference point. Since the reference point allows a more focused search. Therefore, the advantages of this method encourage researchers to use the method based on user preferences in the field of location optimization. In MOEA/D, the neighbors of the weight vector λi are taken to correspond to the few weight vectors in λ1 , . . . , λN that are closest to λi . Therefore For two neighboring subproblems, the optimal solutions should be very similar. 3.1 The Algorithmic Framework of MOEA/D In MOEA/D, each generation of population is a set consisting of the current optimal solutions of each subproblem, and only neighboring subproblems can be used to optimize each other, and the algorithm flowchart is shown in Fig. 1: The specific algorithm of MOEA/D is described as follows. At each generation g, the information to be saved by MOEA/D is: • the n points that make up the population, x1 , x2 , . . . , xN ∈ {0, 1}N ; • FV 1 , FV 2 , . . . , FV N , where FV i = F (xi ), i = 1, 2, . . . , N ; • External population (EP), External population (EP), which is used to temporarily store the individuals with better performance found during the evolution of the population. Input: N: number of subproblems decomposed by MOEA/D (size of the population).λ1 , λ2 , . . . , λN uniformly distributed weight vectors constructed by means

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Fig. 1. The flow chart of MOEA/D

of uniform design method. T: number of neighbors of the weight vector; maxev: maximum number of evaluations. Output: EP. Step1 Initial population: Set EP = ϕ; Compute the Euclidean distance of any two weight vectors and select the nearest T vectors for every weight vector as its neighbors. Let B(i) = {i1 , . . . , iT }, i = 1, 2, . . . , N , where λi1 , λi2 , . . . , λiT is the T weight vectors closest to λi . Generate an initial population uniformly at random. Step2 Updated: For i = 1, 2, 3, ..., N, do A randomly selected neighbor xi from B(i) to Generate a new solution using the evolutionary operator. if g ws (y|λi ) ≥ g ws (xj |λi ), j ∈ B(i), then xj = y, FV j = F(y). Update EP: Remove EP by putting all solutions dominated by F(y) in EP; if F(y) is not dominated by any solution in EP, then remove EP by F(y). Step3 Stop judgment: If the termination criterion is satisfied, the algorithm stops and outputs EP, otherwise, g = g + 1, it returns to Step2. where we find the neighborhood of the power vector corresponding to the i − th individual based on the Euclidean distance, and the closest vector to the vector λi is itself, hence i ∈ B(i). 3.2 Evolutionary Operator Selection In multi-objective optimization, researchers have invested a lot of effort to improve the algorithm. Among them, the operator selection in the evolution process is the most concerned, and the appropriate operator can improve the performance of the algorithm and improve the quality of the offspring. In MOEA/D, the most commonly used DE evolution operators are as follows. In MOEA/D, the most commonly used DE evolution

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operator is shown below: DE/rand/1 : xr1 + F(xr2 − xr3 )

(3)

DE/rand/2 : xr1 + F(xr2 − xr3 ) + F(xr4 − xr5 )

(4)

DE/rand/1 − either − or : v =

xr1 + F(xr2 − xr3 ) Pm < P xr1 + 0.5 ∗ (F + 1) ∗ (xr2 + xr3 − 2 ∗ xr1 ) Pm ≥ P (5)

In the above equation, a different individual is randomly selected from the neighbors of the current individual. According to the formula, we can find that because the DE operator needs more parent individuals, it is more able to generate some children different from the parent individuals. Differential evolution can be used to deal with complex optimization problems and effectively avoid local optima. Different differential evolution operators are suitable for different problems. In selecting the next generation population, the greedy algorithm is used in the text to select the next generation population individuals:

y (g + 1) f (yi (g + 1)) < f (xi (g)) (6) xi (g + 1) = i xi (g) otherwise

4 Instance Testing Slotting optimization seeks to match different goods and warehouse types, discharge methods, shelf types, and other factors. The warehouse system can achieve the optimal layout of the storage space, effectively control the change of goods, reduce the working time and improve the density of goods storage. This paper is based on an actual warehouse example, we set the following objective function expression in the algorithm to improve the warehouse cargo density and reduce the operation time as the optimization objective: g = w1 f1 (x) + w2 f2 (x) 1 Lj vi f2 (x) = Vj f1 (x) =

(7) (8) (9)

where Lj indicates that the distance of the current individual from the warehouse door, Vj refers to the available volume of the current slotting, and vi refers to the volume of the current order to be warehoused. A weight λi is assigned to each objective function fi (x), and λi is the importance of the objective function. Where w1 + w2 = 1. The larger the value of g, then the better the quality of the solution.

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4.1 Parameter Setting The parameter settings involved in this experiment are as follows: a. The large number of experiments run by the program: 1000; b. The probability of crossover and mutation: Pc = 0.8, Pm = 0.2; 4.2 Operator Selection The changes of different fitness are obtained more intuitively of different operators at different evolutionary stages, we set the whole evolutionary process as 1000 generations and divided it into 5 parts, 200, 400, 600, 800, 1000 generations, respectively, and the performance of each operator at different periods is shown in Fig. 2:

Fig. 2. Fitness of different operators

The time spent in the evolution process also varies from operator to operator, as shown in the Table 1. Table 1. The runtime of different operators Mutation operator

DE/rand/1

DE/rand/2

DE/rand/1 − eithe − or

Runtime

4.6∗108 ns

4.3∗108 ns

2.5∗108 ns

According to the table we can find that the operator DE/rand/1-either-or has better results than the other two operators according to the mean value of the function adaptation. Through Table 1, we can see DE/rand/1-either-or that has the shortest running time. Therefore, DE/rand/1-either-or performs better in both time complexity and fitness mean, promoting the global convergence of the location optimization algorithm and accelerating the convergence speed. Therefore, we choose the DE/rand/1-either-or as the evolutionary operator during the experiment.

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5 Conclusion In order to save logistics resources, we need to strengthen logistics management and improve warehouse storage efficiency. In the choice of storage optimization algorithm, because MOEA/D has a wide search interval and facilitates global merit selection, it has obvious advantages in terms of convergence and diversity of solutions, which can further improve the global and reliability of solutions. Therefore, this paper innovatively proposes an optimization algorithm using MOEA/D and improved operators, so as to obtain the best combination of storage spaces, which is beneficial for slotting managers to dynamically search for the optimal solution.

References 1. Rupasighe, T., Dissanayake, S.: An integrated warehouse design and optimization modelling approach to enhance supply chain performance. International Conference on Production and Operations Management Society 2. Geraldes, C., Carvalho, M.S., Pereira, G.: An integrated approach for warehouse design and planning. Eurosis Eti (2011) 3. Solodovnikova, D., Niedrite, L., Kozmina, N.: Handling evolving data warehouse requirements. Springer International Publishing (2015) 4. Yang, L., et al.: Research on location assignment model of intelligent warehouse with RFID and improved particle swarm optimization algorithm. In: 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC) (2018) 5. Wen, L.G., Jin, H.: Analysis and research of the automated warehouse management and control system. Appl. Mech. Mater. 511–512, 1095–1098 (2014) 6. Zheng, Y.Q., et al.: Dynamic simulation analysis of stacker for automated warehouse. Advanced Materials Research 411, 383–387 (2012) 7. Scardua, L.A.: Multiobjective Evolutionary Algorithm Based on Decomposition (2021) 8. Zhang, Q., Hui, L.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008) 9. Deb, K.: Multi-objective optimization using evolutionary. ed: John Wiley & Sons (2001) 10. Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer (2012)

An Improved Density Peak Clustering Algorithm Based on Gravity Peak Hui Han1 and Rui Zhang2(B) 1 State Key Laboratory of Astronautic Dynamics, Xi’an 710043, Shaanxi, China 2 School of Information Engineering, Shandong Management University, No. 3500, Dingxiang

Road, Changqing District, Jinan 250357, Shandong, China [email protected]

Abstract. Density peak clustering (DPC) algorithm has been cited and further improved by many researchers since it was put forward. In the aspect of cluster centers discovery, the locations of these points need manually judged by classical decision graph or improved decision graph. However, this way of manual participation in decision-making significantly reduces the efficiency of the algorithm and trades the cost of efficiency for accuracy. Although, relevant scholars have made some improvements on the decision graph to automatically determine the truncation distance or cluster center. To solve this problem, an improved density peak algorithm based on gravitational peak named IDPC-GP is proposed. In this approach, the gravitational dimension is introduced into the data space in order to better grasp the data distribution, and KNN similarity is used for extended clustering. Then, the cluster centers can be quickly found and clustered. Experiments verifies the superiority of the algorithm in comprehensive performance of the IDPC-GP algorithm. Keywords: Density peak clustering · Gravity center · KNN

1 Introduction As a new generation of high-performance density clustering algorithm, density peak clustering algorithm provides a new starting point and research direction for unsupervised clustering analysis [1, 2]. DPC algorithm has superior performance in discovering high-density centers [3], special-shaped clusters and processing unbalanced data. However, the high-density center of DPC is determined manually based on the decision graph. And whether the high-density center can be clearly distinguished depends on the setting of appropriate truncation radius parameters [4]. To solve the above problems, relevant scholars have made relevant improvements to improve the adaptability and parameter tolerance of DPC algorithm. Li Tao et al. user γ ranking graph determines the inflection point and potential cluster center, and then automatically determines the actual cluster center from the potential cluster center [5]. The effect of self-determination center has been realized. Jiang P et al. used the improved adaptive method to select the representative points of the core grid as the clustering center to solve the problem that the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 284–291, 2023. https://doi.org/10.1007/978-981-19-9968-0_34

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center cannot be determined adaptively [6]. Chen Jinyin et al. solved the problem that the density center is difficult to determine by fitting the density distance product density distribution with the normal distribution curve [7]. Ruhui Liu et al. proposed a constraint based fast density peak search clustering algorithm (CCFDP). Automatically generate multiple potential clustering centers, and make the information best of the structure in the constraints to determine the high-density centers [8]. In this paper, aiming to the problems of parameter adaptation and automatic determination of cluster center in DPC, an improved density peak clustering algorithm based on gravitational peak (IDPC-GP) is proposed.

2 Related Works In this paper, the two problems to be solved: 1. The automatic determination of the center of DPC algorithm; 2. Reduce the definition of manual parameters and the tolerance rate of necessary parameters. To solve the first problem, two types of decision graphs are designed in DPC. The one is classical decision graph based on relative density distance δ and local density ρ [9]; The other is the improved decision diagram obtained by multiplying and sorting the two parameter values, which is obtained by Eqs. (1), (2), (3) and (4). N   (1) χ Dij − Dc i, j ∈ [1, N ] ρi = j=1



1 D ≤ 0 0 otherwise   δi = min Dij ρi < ρj χ(·) =

γi = ρi ∗ δi

(2) (3) (4)

where N is the total amount of samples. D = Dij − Dc . As to the second problem, the initialization of the algorithm needs to involve the truncation radius and the number of high-density centers. These two parameters need a priori knowledge on the one side and manual determination on the other hand. After adjusting the appropriate parameters, that is, when the high-density center is clearly distinguished from other sample points, take the high-density center as the starting point, and divide each sample point into the cluster where the nearest neighbor of high-density is located to complete the division. Take UCI data set Jain as an example. It can be seen in Fig. 1 and 2 that when different d c values are taken, two high-density centers can be clearly seen in the classical and improved decision diagrams. But if the number is right, the locations of the high-density centers are not expected. It the Fig. 3, when the d c value is inappropriate, the result is very poor, because the clustering is based on the center position. If the center position is wrong, it will inevitably lead to disastrous consequences of subsequent clustering. In astrophysics, according to the current theory, gravity is a higher dimension than four-dimensional space-time, which has more advantages for grasping spatial distribution. The research based on data gravity comes from universal gravity, Lizhi Peng et al.

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Fig. 1. Two kinds of decision graphs on Jain with d c = 10

Fig. 2. Two kinds of decision graph on Jain with d c = 12

Fig. 3. Clustering results comparison on Jain between with d c = 10 and d c = 12

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used data gravity for classification learning [10]. The gravity formula is shown in the Eq. (5). Fi,j = G

Mi ∗ Mj r2

(5)

where Mi and Mj represents the mass of two celestial bodies and r is gap between them. G is a constant.

3 Method DPC algorithm consists of two parts: the discovery of gravity center and extended clustering based on local spatial similarity. The algorithm will be designed from these two aspects. 3.1 Discovery of Gravity Centers The data gravity center is related to the density of data distribution and local data volume. The judgment basis of the centers is based on the gravity value of k-nearest neighbor (KNN) of each sample. The data gravity formula evolved from Eq. (5), as shown in the Eq. (6). Fi =

k j=1

1 rij2

j ∈ [1, k]

(6)

Next, the gravity value between each sample and its k-nearest neighbors are compared. Those larger than the gravity value of all k-nearest neighbor samples are regarded as local gravity centers or potential gravity centers. Still take Jain data set as an example. When k is 10, the distribution diagram of local gravity center is obtained, as shown in the Fig. 4:

Fig. 4. Gravity center distribution (Red Star mark).

Obviously, there are two banded clusters in Jain dataset, and the gravity centers found are far more than 2. Fortunately, their distribution covers two clusters. What we

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need to do next is not to screen the core cluster centers, because the decision graph has failed before. The first two high-density points obtained according to the γ ranking graph are not the center points we want. DPC-GP algorithm does not directly consider the problem of further determining the center. Instead, it adjusts and filters in real time in the clustering process. 3.2 Clustering Process of IDPC-GP After the centers are determined, cluster mining is started in an extended way, and classes are built by KNN similarity. All special cases are handled in the process of expansion, such as whether it is a cluster center in local high density. KNN similarity is defined as: suppose the set KNN(x i ) represents the k nearest neighbor of sample x i , KNN(x i ,k) represents the k nearest neighbor of sample x i , and KNN similarity is as Eq. (7):      (7) KNN _Sim xi , xj = KNN (xi ) ∩ KNN xj  /k where |KNN (xi )∩KNN (xj )| means the intersection number of k nearest neighbors of two samples. Clustering starts from each local high gravity center and uses KNN similarity as the measurement standard for extended clustering. When KNN similarity is higher than the similarity threshold s, the two samples will be divided into the same class. In this process, multiple sub clusters will be merged, because there will be more high gravity centers than the actual clusters. The flow of the whole algorithm is shown in the Table 1. Table 1. The flow of IDPC-GP.

IDPC-GP Algorithm Input: X, k , s; Output: Array cluster; 1. Establish k nearest neighbor matrix KNN of sample set X ; 2. Calculate the local gravity value of each sample point by Eq.(6); 3. The samples whose gravity value is higher than that of all k nearest neighbors are defined as local high-density centers, and the set of high-density centers C={c1 , c2,..., cn } is get, where 1≤n≤N. 4. Calculate KNN similarity between each pair of samples with Eq.(7). 5. Taking each high gravity center as the starting point and KNN similarity as the measurement standard, extended clustering is carried out. Tag array cluster records clustering. 6. If high gravity centers are found to belong to the current cluster during clustering, they will be merged together. 7. After all clustering based on high gravity center is completed, the remaining points are regarded as outliers.

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4 Experiments 4.1 Datasets The test datasets utilized in the experiments are four representative graphic datasets from UCI: Jain, spiral, unbalance, Flame, R15, aggregation, PathBased and 4k2_far. It contains banded, striped, unbalanced, multi type combination and other data sets, which can comprehensively test the performance of the approach. 4.2 Parameter Test The IDPC-GP algorithm involves a core parameter k. The gravity center is determined by the distribution of KNN, that is, the parameters are related to the value of k. In order to verify the robustness of the IDPC-GP to the k, the influence of this parameter on the approach effect is specially tested. Based on the idea of IDPC-GP, as long as the high gravity center group covers all natural clusters, all cluster structures can be found and the clustering task can be completed. In the Table 2, we tested the number of high gravity centers mined under different k values on 8 UCI data sets, and these local centers cover all natural clusters. Table 2. Effective coverage test of high gravity center. k

5

6

7

8

9

10

15

20

25

Jain

36

31

29

25

21

18

14

12

10

Spiral

31

16

10

6

5

4

3

3

3

Unbalance

648

545

462

402

353

315

196

129

89

Flame

23

17

11

10

9

7

5

4

3

R15

46

41

35

32

22

19

16

15

15

Aggregation

90

75

61

55

45

40

22

18

16

PathBased

35

30

22

18

14

12

4

4

2

4k2_far

37

30

28

20

18

11

9

6

6

In Table 2, the first row shows different values of k in [5, 25], and the other rows show the number of gravity centers on different data sets and at different values of k. All the high gravity centers in the above are natural clusters of full coverage data sets. The experimental results show that in a relatively wide range of k, the high gravity centers can effectively cover all natural clusters, which provides accurate positioning for the subsequent discovery of clusters. 4.3 Performance Comparison Test From the above experimental results, we can see that the clustering effect of IDPC-GP algorithm in unbalanced, non-spherical and banded unbalanced data sets is satisfactory.

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Of course, the DPC algorithm can achieve the same effect on these data sets, but it has high requirements for parameter combination. While the IDPC-GP algorithm is insensitive in parameter settings and has stronger parameter tolerance. To prove the ability of the IDPC-GP to discover clusters after discovering local centers, the clustering effects of several algorithms are tested and the results are displayed visually as shown in the Fig. 5. Obviously, in the face of different types of data sets, the clustering effect is satisfactory.

Fig. 5. Clustering effect of IDPC-GP algorithm on shape data sets.

As shown in Table 3, taking Jain data set as an example, with the change of k value, the change of s value in Fig. 5 can be achieved. Here, we show the case of making s value as constant as possible. Table 3. Collocation value of k and s. Arguments k

5−11

12−22

23−25

s

0.15

0.4

0.5

Finally, to test the robustness of the IDPC-GP to parameter s, we take data Jain as an example. When the results of clustering are optimal, the values range of k and s is shown in the Table 3. The s value corresponding to each k value is not unique. For example, when k = 15, the value range of the most s value reached by the IDPC-GP algorithm on

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the Jain dataset is [0.2, 0.45]. Compared with DPC algorithm, the parameter d c value can only be in [10, 15], and the parameter selection space is larger.

5 Conclusion Aiming at the problem that DPC algorithm cannot automatically determine the number of high-density centers and is sensitive to parameters, a clustering algorithm IDPC-GP based on local data gravity center and KNN similarity extension is proposed. The local data gravity center can well cover the main clusters in the data space, and can accurately find the distribution of all clusters. Taking each gravity center as the starting point, extended clustering based on KNN similarity can complete the automatic clustering process without human intervention. The value of the important parameter k involved in the algorithm has a very low impact on the effect of the algorithm, which improves the universality of the algorithm. Compared with DPC algorithm, it has some advantages in parameter adaptation and universality.

References 1. Jian, H., Xu, E.: An improved density peak clustering algorithm. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 211–221 (2017) 2. Wang, Y., Wang, D., Zhou, Y., et al.: VDPC: variational density peak clustering algorithm. Inf. Sci. 621, 627–651 (2021) 3. Rui, Z., Tao, D., Shouning, Q., et al.: Adaptive density-based clustering algorithm with shared KNN conflict game. Inf. Sci. 565(5), 344–369 (2021) 4. Ren, C., Sun, L., Yu, Y., et al.: Effective density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging. IEEE Access 99, 1 (2020) 5. Tao, L.I., Hongwei, G.E., Shuzhi, S.U.: Density peaks clustering by automatic determination of cluster centers. J. Front. Comput. Sci. Technol. 10(11), 1614–1622 (2010) 6. Jiang, P., Zeng, Q.: An Improved density peak clustering algorithm based on grid. Comput. App. Softw. (2019) 7. Jinyin, C., Xiang, L., Haibing, Z., et al.: A novel cluster center fast determination clustering algorithm. Appl. Soft Comput. 57, 539–555 (2017) 8. Liu, R., Huang, W., Fei, Z., et al.: Constraint-based clustering by fast search and find of density peaks. Neurocomputing 330(FEB.22), 223–237 (2019) 9. Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014) 10. Peng, L., Zhang, H., Chen, Y., Yang, B.: Imbalanced traffic identification using an imbalanced data gravitation-based classification model. Comput. Commun. 102, 177–189 (2017)

A DPC Based Recommendation Algorithm for Internship Positions Rui Zhang1(B) , Lingyun Bi1 , and Tao Du2,3 1 School of Information Engineering, Shandong Management University, No.3500, Dingxiang

Road, Changqing District, Jinan 250357, Shandong, China [email protected] 2 School of Information Science and Engineering, University of Jinan, Jinan, China 3 Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

Abstract. In the graduation internship activities, the choice of centralized internship based on school-enterprise cooperation units is limited, and the independent internship method will inevitably lead to the problem of blind job selection due to students’ lack of social experience. In this paper, we mine historical practice data and propose a recommendation algorithm based on binomial association rules and density peak clustering analysis named DPC-RA. Through binomial association rules, students’ internship results in different units are associated with the classification data of unit names, and the internship unit name data is numerically realized. DPC (density peak clustering) algorithm is used to cluster and mine internship data, and obtain multiple clusters. This method can provide accurate personalized work recommendations and improve the quality of internship. Experiments show that this method is effective. Keywords: Recommendation · Density peak clustering · Digitization

1 Introduction DPC algorithm relies on the concise data spatial distribution principle to capture the local and global data distribution, so as to obtain excellent clustering division effect [1, 2]. Therefore, this achievement has been published in the Journal of Sciences and widely cited. As an important branch of intelligent recommendation algorithm, recommendation system based on clustering algorithm has made a variety of attempts and practices to realize the basic recommendation function in various fields [3]. In the cases where clustering algorithm is applied in recommendation system, most of them use K-means for pattern mining [4–6]. As we all know, K-means algorithm is the representative of partition based clustering algorithm [7, 8]. DBSCAN algorithm is the most classic density based clustering algorithm, which can mine non spherical clusters, but the effect is very sensitive to parameters [10]. The clustering mining effect of non spherical spatial distribution data is not very satisfactory [9]. As an outstanding density clustering algorithm, DPC algorithm can find and mine non spherical and arbitrarily distributed clusters [11]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 292–299, 2023. https://doi.org/10.1007/978-981-19-9968-0_35

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SNNDPC introduces KNN similarity based on DPC to improve clustering effect, but at the same time, it improves the computational complexity [12]. Therefore, this paper uses DPC algorithm to mine and analyze the historical internship data, and establishes a recommendation model to realize the personalized internship position recommendation function for college students who are about to graduate internship.

2 Related works In the fields of machine learning, data mining and big data analysis, data is the raw material and the soil for burying knowledge. However, the collected data not only need to complete the cleaning work, but also need to digitize and standardize a large number of classified feature data. Common numerical methods include one hot coding, dummy variable coding, label coding and so on. The first two use different bits of the vector to mark different categories, and the latter is jointly marked by multiple features in the vector, but these methods do not take into account the meaning of the feature itself, which will affect the depth of data mining to a certain extent. The novel density based clustering method of DPC is used two important parameters δ and ρ to grasp the local and global distribution of the whole current sample space. Parameter ρ represents the local density and obtain local distribution and δ represents the distance from each sample to the nearest point which the density is higher [1]. The mathematical expression is shown in equations (1), (2) and (3): N   (1) χ dij − dc i, j ∈ [1, N ] ρi = j=1



1 d ≤ 0 0 otherwise   δi = min dij ρi < ρj χ=

(2) (3)

where N represents the total number of samples. d = dij − dc . Next, drawing the decision graph with two parameters, the abscissa represents density and the ordinate represents distance. The cluster center and outliers can be clearly seen in the graph, this kind called classical decision graph. Taking DPC on UCI data set spiral [13] as an example, the classical decision graph is shown on the left of Fig. 1. According to the principle of the algorithm, the point with higher density and farther relative distance is likely to be the center point of high density, that is, the cluster center. In order to make it more central and more prominent, an improved decision graph is proposed, that is, the two parameters are multiplied (γi = ρi ∗ δi ) to make the gap between samples bigger and make the discrimination greater. γ as ordinate to draw a improved decision graph. The specific figure is on the right of Fig. 1. Obviously, there are three centers in spiral . K-means is commonly used in recommendation system [4]. DPC has obvious advantages to K-means. The effect of the two algorithms on spiral are shown in the Fig. 2. Facing to the irregular and non spherical distributed clusters, it is obvious that DPC algorithm is more practical than the partition based algorithm represented by K-means algorithm. In the application scenario of recommendation system, the mining task of local spatial structure is more suitable for density based clustering algorithm.

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Fig. 1. Decision graph of DPC on spiral

Fig. 2. Comparison of clustering results on spiral

3 Method The research work of the algorithm is mainly divided into two parts: one is to digitize the classified data, the other is to use DPC algorithm to cluster the standardized data, establish a clustering model and realize the recommendation function. DPC based recommendation algorithm for internship positions function flow is shown in Fig. 3. The work of this paper mainly includes the digitization of classified data and the implementation of recommendation algorithm based on DPC. First, classified data digitized. Historical data is composed of students’ school performance information, personal basic information and internship information, including student number, name, gender, class, major, courses performance, internship nature, internship performance, internship unit, grade and other dimensions. Internship unit needs to be digitized as sub-type data, and replaced by internship unit code first, measure and calculate the sample distance between internship units through the scores of internship units and students participating in each unit, that is, count the average scores of students participating in internship in each unit, and establish the distance matrix

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Fig. 3. Test paper generation process

between internship units. The distance calculation formula is as Eq. (4): S_matrix(i, j) = |Ave_scorei − Ave_scorej | = |

Sum_scorej Sum_scorei − | Sum_stui Sum_stuj

(4)

where i and j represent the numbers of two internship units respectively. Sum_score i represents the total internship scores of all students interning in unit Sum_stui represents the number of students interning in unit i, so as to obtain the classification distance matrix(CDM) of internship unit dimension. Matrix CDM is a symmetric matrix with N × N. Second, the recommendation algorithm based DPC. The recommended function is implemented by DPC algorithm. The specific algorithm principle remains unchanged. The specific algorithm implementation is adjusted according to the creation of CDM matrix. That is, when calculating the Euclidean distance between two samples, it should be discussed separately when it comes to the number dimension of the internship unit. The details are shown in the Table 1 below. After the Euclidean distance matrix is created, the clusters can be divided according to the original DPC clustering method. Referring to Fig. 4, after several clusters and corresponding high-density centers are obtained by DPC algorithm, the clustering model is obtained. The features of the data used for clustering include target features, such as internship units. For graduates who are about to participate in internship, there is no such data, so they only need to compare other feature data with each cluster center, divide them into the cluster where the nearest cluster center is located, and then find several nearest neighbors in the cluster, which are sorted from large to small according to the

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dimension of internship performance, Recommend the corresponding internship unit again to complete the recommendation function.

4 Experiments 4.1 Datasets At present, this algorithm is recommended by a single major. The data set comes from the graduation practice data of graduates majoring in Internet of things application technology of our university last year, mainly including 38 dimensions of data, such as student number, name, gender, class, course results, practice unit, practice method and Practice results. The algorithm only cares about gender, the results of various subjects during school, the final practice results and the number of practice units. The gender dimension is a dichotomous attribute. Men and women are replaced by 1 and 0 respectively. In fact, the dimension of internship unit is mainly reflected by the students’ comprehensive internship results, that is, it has the strongest correlation with the results, that is, it is numerically calculated with formula 4, and finally the data set available for the algorithm is obtained.

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4.2 Algorithm Implementation and Comparison Test Because the DPC algorithm has a high tolerance rate for the parameters of the truncation distance d c . In a large range close to the optimal parameters, the impact of d c on the clustering effect is small. We take one of the parameters to show the experimental effect, that is, d c = 1.8. The corresponding classical decision degree and improved decision diagram are shown in the Fig. 4, It can be clearly seen that the distribution of 4 high-density centers is different from that of other samples.

Fig. 4. Decision graph of DPC on spiral

It can be seen in Fig. 5 that when the interval of the truncation distance d c is [1.0, 2.8], the number of suspected cluster centers is unstable at the beginning on the left, and it starts to be stable at 4 when d c is equal to 1.8. According to the improved decision graph of nearby parameters, when d c is 1.8, the cluster centers are the most obvious and concentrated, and the degree of discrimination is the highest. While, as can be seen in Fig. 6, when d c is equal to 1.4 and 2.2 respectively, although the high-density centers can also be separated, they are not very obvious. so the algorithm selects the clustering result when d c is 1.8 for analysis.

Fig. 5. Relationship between the number of suspected cluster centers and d c

In order to test the effectiveness of DPC in recommendation effect, it is compared with K-means, DBSCAN and SNNDPC algorithms respectively. The comparative experiment mainly evaluates the clustering effect and the numerical effect of classified data through the tightness (CP and CT ) index. The indexes can reflect the compactness within

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Fig. 6. Improved Decision Graph of DPC with d c = 1.4 and d c = 2.2 respectively

the cluster and the overall compactness, and can be calculated by Eq. (5) and Eq. (6) respectively:  Ni   j=1 Xij − 1NNi j ∈ [1, Ni ] (5) CPi = Ni   N   (6) CT = j=1 Xj − 1NNj N where Ni represents the number of samples in cluster i. 1NNi means the nearest neighbor of X i and it is in the same cluster with X i . The smaller the value of CP and CT the higher the similarity in the cluster and it illustrate the better effect of the clustering. Table 2 shows the CP values of each cluster of the four algorithms under the same conditions of clustering four clusters. The experimental results show that for the CP index of each cluster, the results of DPC algorithm are significantly better than those of K-means, DBSCAN and SNNDPC. Ave_CP is the average of CPs. Although the result of SNNDPC is the best, there is little difference between DPC and it. The CT value of DPC algorithm is the best compared with the other three algorithms. Although the effect of SNNDPC is not much different from that of DPC, the time complexity is much higher due to SNN. The above also proves that the clustering effect is improved after the binomial statistical digitization of the classified data of internship units through relevant dimensions. Table 2. Comparison between DPC and other methods. Algorithm

CP 1

CP 2

CP 3

CP 4

Ave_CP

CT

DPC (d c = 1)

0.8241

0.8456

0.8620

0.8861

0.8545

0.8622

K-means (k = 4)

0.8398

0.8544

0.8740

0.8861

0.8635

0.8657

SNNDPC (k = 8)

0.8357

0.8539

0.8419

0.8861

0.8544

0.8623

DBSCAN (MinPts = 5, d c = 1)

0.8398

0.8544

0.8740

0.8861

0.8636

0.8908

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5 Conclusion Because of the unknowness and uncertainty, compared with supervised learning or classification algorithm, unsupervised clustering analysis has a low proportion in practical application, while unsupervised learning can mine interesting knowledge in practical application. In this paper, DPC algorithm is introduced into the recommendation system to realize the recommendation function of general internship post selection. Otherwise, a numerical method based on statistical correlation is proposed for classified data. Retain and improve the amount of information in the original data set, so as to improve the depth and quality of data mining. Practice has proved that this idea is effective and has practical application value.

References 1. Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014) 2. Jiang, P., Zeng, Q.: An improved density peak clustering algorithm based on grid. Comput. Appl. Softw. (2019) 3. Song, Z.: Research and Application of Clustering Algorithm in Recommendation System. Nanjing University of Posts and Telecommunications (2018) 4. Jaafar, B.A., Gaata, M.T., Jasim, M.N.: Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms. Indones. J. Electr. Eng. Comput. Sci. 19(3), 1635 (2020) 5. Jain, S., Sharma, M., Kumar, P.: Recommendation system for breast cancer treatment using k-means clustering algorithm. Indian J. Public Health Res. Dev. 10(4), 202–207 (2019) 6. Himel, M.T., et al.: Weight based movie recommendation system using K-means algorithm. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp. 1302–1306 (2017) 7. Kanungo, T., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002) 8. Bai, L., et al.: Fast density clustering strategies based on the k-means algorithm. Pattern Recogn. 71, 375–386 (2017) 9. Xiao X. Research on selective Clustering Fusion Algorithm Based on fractal dimension. Hefei Polytechnic University (2015) 10. Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. International Conference on Knowledge Discovery & Data Mining (1996) 11. Rui, Z., et al.: Adaptive density-based clustering algorithm with shared KNN conflict game. Inf. Sci. 565(5), 344–369 (2021) 12. Liu, R., Wang, H., Yu, X.: Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf. Sci. 450(1), 200–226 (2018) 13. Chang, H., Yeung, D.-Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191– 203 (2008)

Optimal Method of Air Cargo Loading Under Multi-constraint Conditions Yuheng Lu1 , Chunyun Dong1 , Meng Nan2 , Xiaolong Chen1(B) , and Yufan Wei1 1 Xi’an Key Laboratory of Intelligent Instrument and Packaging Test, Xidian University, Xi’an

Shanxi 710071, China [email protected] 2 Qingan Group Co., Ltd., 17, Xi’an Shanxi 710071, China

Abstract. The cargo loading optimization problem of the aircraft under multiple constraints is studied in this paper, which is of great significance to aircraft safety as well as operational efficiency and profit. Firstly, a multi-objective mixed integer programming model is established to maximize the assembly loads and minimize the center of gravity deviation by considering the constraints of the cargo aircraft center of gravity, the cargo aircraft weight, the cargo hold structure, the isolation of dangerous goods, and the oversized cargo loading. Secondly, a new improved genetic algorithm is designed to solve the model, which adopts strategies such as elite preservation and exponential fitness function to speed up the convergence and guarantee stability. Finally, numerical simulations are carried out considering the Boeing 777 freighter. The results show the feasibility and effectiveness of the proposed model and method, and can provide a reference for future airline stowage work. Keywords: Air cargo · Loading optimization · Mixed integer programming · Genetic algorithm

1 Introduction The loading of air cargo at terminals requires accurate planning to ensure efficient operation and reduce costs during the limited time at the airport [1]. Feng et al. [2] generalized it as the weight and balance problem (WBP) of the aircraft. This problem is of crucial importance to airlines because the generated aircraft loading plan ensures that the aircraft’s center of gravity (CG) remains within a safe range during its operation. Furthermore, studies have shown that a 75 cm shift in the center of gravity of an Airbus A340 aircraft could result in wasting 4 tons of fuel per 10,000 km of flight [3]. Lurkin et al. [4] showed that significant gains in cost reduction and operational efficiency can be obtained by optimizing the weight and balance problem. However, the current airlines are still manual to calculate cargo loading plan and loading plan. Under normal circumstances, it is time-consuming and inaccurate to calculate and solve the loading optimization problem manually [5]. Therefore, it is of great significance to study WBP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 300–308, 2023. https://doi.org/10.1007/978-981-19-9968-0_36

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This paper studies the loading optimization problem of the main cargo hold of a aircraft with known ULDs to be loaded. First, select a suitable set of ULDs from the available ULDs to load in the main cargo compartment of the plane to achieve the maximum loading capacity to obtain the maximum transportation profit, which is a knapsack optimization problem [6]; Second, each cargo needs to be loaded to a designated position on the deck under a set of constraints, so this is again an assignment problem. The WBP model proposed in this paper is a combinatorial optimization problem. According to realistic constraints of airline freighter stowage, a mixed integer programming model is established, and an improved GA is designed to solve the problem.

2 Construction of Cargo Aircraft Loading Optimization Model 2.1 Problem Description Loading space of the aircraft includes the main cargo compartment and the front and rear lower cargo compartments. ULDs are mainly loaded in the main cargo compartment of the cargo plane. Therefore, the model in this paper only considers the optimization problem of loading in the main cargo hold. Restricted by the curved inner contour of the cargo hold, different loading areas have different restrictions on the shape and weight of the containerized cargo [7].The main cargo compartment of the cargo aircraft is shown in Fig. 1.

Fig. 1. Plan view of the main cargo hold of the aircraft.

The air cargo load planning problem involves selecting a certain number of ULDs from a set of available ULDs and assigning them to specified positions in the cargo hold under the condition of satisfying a large number of linear and nonlinear constraints. 2.2 Model Construction In the actual cargo aircraft loading operation, the flight operation usually pursues the maximum profit while ensuring the operation safety. Therefore, the maximum aircraft transportation payload and the minimum take-off center of gravity offset are taken as the objective function of the model: max z = ρ

N ULD N POS 

wi · xij − (1 − ρ)|CGOPT − CGTOW |

(1)

i=1 j=1

where ρ denotes the weight factor, and after the two objectives are normalized, the weights of the two optimization objectives are adjusted. The symbols used in the relevant formulas in the cargo aircraft loading model are shown in Table 1. The constraints of the model are as follows:

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Symbol

Meaning

Symbol

Meaning

N ULD

Total number of ULDs to be loaded

OEW

Aircraft empty weight/kg

i

ULD number

TOF

Airplane take-off fuel/kg

wi

Mass of i-th ULD/kg

TripF

Airplane range fuel/kg

hi

Height of i-th ULD/cm

MTOW

Aircraft maximum take-off weight/kg

N POS

Number of aircraft cargo bays

MLW

Aircraft maximum landing weight/kg

j

Aircraft cargo bay number

MZFW

Aircraft maximum fuel-free weight/kg

BAj

Lever arm of the j-th loading position/in

MPL

Aircraft maximum payload/kg

BAOEW

Aircraft empty lever arm/in

Sep

A collection of non-adjacent condition-constrained ULDs

BAFW

Aircraft fuel tank lever arm/in

Pair

A collection of ULDs decomposed by very large ULDs

CGZFW

Airplane oil-free center of gravity/in

W

Light side loading limit weight/kg

CGTOW

Takeoff center of gravity/in

LA

Symmetric point linear load

CGLW

Airplane Landing Center of Gravity/in LB

Actual linear load on heavy side (kg/in)

CGOPT

Optimal take-off center of gravity of the aircraft/in

LC

Maximum loading linear load on one side (kg/in)

Hj

The j-th loading position height limit/cm

PL

ULD Bottom Plate Length (in)

ρ

Weight factor

xij

Decision variable

1) Basic constraints: In order to describe the allocation of ULDs in the cargo hold, let the decision variable be:  xij =

1 the i − th ULD is placed in the j − th bay ∀i = 1, · · · , NULD ∀j = 1, · · · , NPOS 0 others

(2)

Each ULD can only be loaded in one bay: N POS j=1

xij ≤ 1 ∀i = 1, · · · , NULD

(3)

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Only one ULD can be loaded in each bay: N ULD

xij ≤ 1 ∀j = 1, · · · , NPOS

(4)

i=1

2) Aircraft weight constraints: The maximum allowable weight without fuel, the maximum take-off weight and the maximum landing weight will be given in the cargo aircraft load balance manual. Corresponding to different flights, the maximum payload limit of the cargo aircraft can be calculated. ⎧ ⎨ MTOW − OEW − TOF MPL = min MLW − OEW − (TOF − TripF) (5) ⎩ MZFW − OEW N ULD N POS 

wi · xij ≤ MPL

(6)

i=1 j=1

3) Structural constraints of cargo hold: ULDs loaded in each loading position must meet the weight and height restrictions of the loading position. N ULD

wi · xij ≤ max wj ∀j = 1, · · · , NPOS

i=1 NULD 

hi · xij ≤ max Hj ∀j = 1, · · · , NPOS

(7)

(8)

i=1

4) Constraints on the center of gravity (GC) of cargo plane: To ensure flight safety, the GC of the plane must always be kept within a safe range. Corresponding to the barycentric envelope plot, the barycentric coordinates are always within the barycentric envelope. BAOEW · OEW +

N ULD N POS

wi · BAj · xij

i=1 j=1

CGZFW = OEW +

N ULD N POS

(9)

wi · xij

i=1 j=1

BAOEW · OEW + BAFW · FW +

N ULD N POS

wi · BAj · xij

i=1 j=1

CGTOW = OEW + FW +

N ULD N POS

(10)

wi · xij

i=1 j=1

BAOEW · OEW + BARFW · RFW +

N ULD N POS

wi · BAj · xij

i=1 j=1

CGLW = OEW + RFW +

N ULD N POS

(11)

wi · xij

i=1 j=1

min CGw ≤ CGw ≤ max CGw w ∈ {ZFW , TOW , LW }

(12)

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5) Left and right asymmetric loading constraints of the cargo hold: The weight of the left and right ULDs in each loading area is controlled so that it remains within the allowable range of the aircraft’s structural loads and balance. W = {LA + [0 − LA × (LB − LA)/(LC − LA)]} × PL

(13)

6) Isolation of dangerous goods: According to the different properties of dangerous goods, dangerous goods can be divided into 9 categories. Before loading, airlines will classify cargo containing dangerous goods, and the loading plan needs to consider the safe isolation of ULDs containing dangerous goods [8]. xi1 j1 + xi2 j2 ≤ 1 : j1 closeTo j2 ∀(i1 , i2 ) ∈ Sep, ∀j1 , j2 = 1, . . . , NPOS

(14)

7) Loading of large ULDs: Large ULDs occupy 2 loading positions. When loading such ULDs, they are decomposed into 2 sets of small ULDs, and it is stipulated that the two ULDs in the set must be in the same group and adjacent to each other. xi1 j1 + xi2 j2 = 2 : j1 closeTo j2 ∀i1 , i2 ∈ Pair ∀j1 , j2 = 1, . . . , NPOS

(15)

3 Algorithm Design Genetic algorithm is better than other intelligent algorithms in large-scale optimization process by simulating the natural evolution process to find the optimal solution. However, the traditional genetic algorithm has the problem of slow convergence and instability when solving optimization problems. Therefore, this paper designs a new genetic algorithm based on real coding to solve the problem. Each stowage plan corresponds to an individual with a coding length of n, the position of the gene indicates the location of the ULD. The algorithm solution flow is shown in Fig. 2. The details of the algorithm design are as follows. The population initialization adopts partition initialization. The ULDs are grouped according to attributes, and the gene sequences are assigned according to regions, which solves the problem that the random generation of the whole cabin sequence has a small probability of obtaining a feasible solution, and speeds up algorithm convergence speed. The fitness function takes the objective function. For individuals who do not meet the constraints, the fitness function value is set to 0. Exponential transformation is used to increase the gap between individuals and improve the convergence of the algorithm. The selection operator uses a combination of optimal saving and roulette. The crossover operator adopts grouped multipoint crossover. The mutation operator adopts grouped inverse mutation and direct single-point mutation in the R area to enhance the global retrieve ability of the algorithm.

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Fig. 2. The solution process of genetic algorithms.

4 Example Analysis This paper takes B777F cargo plane as an example to verify the effectiveness of the model and algorithm. Under the loading configuration on both longitudinal sides of the ULD, the main cargo compartment can be loaded with a total of 27 ULDs. Due to the limitation of the outline and structure of the aircraft, the height of the ULD that can be loaded in the B-M area is limited to 300 cm, the A and P areas are 295 cm, and the R area is 244 cm. The basic operating parameters of the aircraft given by the aircraft manufacturer are shown in Table 2. The weight limit of the main cargo compartment of the B777F freighter is shown in Table 3. Table 2. B777F basic operating parameters. Items

MTOW

MLW

MZFW

OEW

Values

347451 kg

260815 kg

248115 kg

141805 kg

Table 3. B777F freighter main cargo compartment maximum load limit. Loading area

A、B、C、D、E

F、G、H、+0.3J

0.6J、K、L、M、P

R

Weight limit

41149 kg

48487 kg

40010 kg

3543 kg

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The center of gravity envelope of this type of aircraft is shown in Fig. 3, from which the safety range of the center of gravity of this type of aircraft can be read. Using the data of a domestic flight in China for verification, the TOF of the takeoff fuel of the cargo plane is 26430 kg, the fuel capacity of the cargo aircraft TripF is 13028 kg, and the optimal take-off center of gravity is 28% MAC. Taking 30 A-type ULDs to be loaded as an example, the specification parameters of the ULDs are randomly generated based on a normal distribution function.

Fig. 3. B777F center of gravity envelope.

The algorithm is implemented using MATLAB. The population size of the genetic algorithm is set to 50, the evolutionary generation is 300, the crossover probability is 0.85, and the mutation rate is 0.1. These parameters are the optimal parameters after many tests and comparisons. Based on the above parameters, the example is solved five times, and the convergence of the algorithm is obtained as shown in Fig. 4.

Fig. 4. Convergence of stowage results from multiple calculations.

It can be seen from Fig. 4 that the algorithm converges in a relatively fast time, and it converges to the optimal solution and tends to be stable after about 150 generations of

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evolution. However, it takes 300 generations to test the convergence and stability of the traditional genetic algorithm under the same parameters. For comparison, this example is given to three stowage personnel for stowage. The comparison results are shown in Table 4. The results show that the stowage scheme obtained by the improved genetic algorithm is better than manual stowage. Table 4. Manual and improved genetic algorithm stowage comparison chart. Items

Manual stowage results

Improved GA stowage results

Offset of CG(%MAC)

Total load(kg)

Offset of CG(%MAC)

Total load(kg)

1

0.0022

72865

0.0103

71350

2

0.0035

72861

0.0130

72001

3

0.0043

72845

0.0160

71500

AVG

0.0033

72857

0.0131

71617

5 Conclusion The air cargo load planning problem belongs to a class of complex combinatorial optimization problems. In this paper, combined with the realistic constraints considered in the airline’s stowage, a mixed integer programming model is established with the two objectives of the maximum cargo weight and the minimum offset of the center of gravity, and an improved GA is designed to solve the problem. The algorithm improves the convergence and global optimization ability of the algorithm through elite selection and exponential transformation. Compared with manual loading, a better loading solution can be obtained, which can provide a reference for the cargo loading department.

References 1. Wong, E.Y.C., et al.: Closed-loop digital twin system for air cargo load planning operations. Int. J. Comput. Integr. Manuf. 34(7), 801–813 (2020) 2. Feng, B., Li, Y., Shen, Z.-J.M.: Air cargo operations: Literature review and comparison with practices. Transp. Res. Part C Emerg. Technol. 56, 263–280 (2015). https://doi.org/10.1016/j. trc.2015.03.028 3. Brandt, F., Nickel, S.: The air cargo load planning problem - a consolidated problem definition and literature review on related problems. Eur. J. Oper. Res. 275(2), 399–410 (2019) 4. Lurkin, V., Schyns, M.: The airline container loading problem with pickup and delivery. Eur. J. Oper. Res. 244(3), 955–965 (2015) 5. Vancroonenburg, W., et al.: Automatic air cargo selection and weight balancing: a mixed integer programming approach. Transp. Res. Part E Logist. Transp. Rev. 65(1), 70–83 (2014) 6. Zhao, X., Yuan, Y., Dong, Y., Zhao, R.: Optimization approach to the aircraft weight and balance problem with the centre of gravity envelope constraints. IET Intell. Transp. Syst. 15(10), 1269–1286 (2021). https://doi.org/10.1049/itr2.12096

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7. Limbourg, S., Schyns, M., Laporte, G.: Automatic aircraft cargo load planning. J. Oper. Res. Soc. 63(9), 1271–1283 (2012) 8. Wong, E., Ling, K.: A mixed integer programming approach to air cargo load planning with multiple aircraft configurations and dangerous goods. In: 2020 7th International Conference on Frontiers of Industrial Engineering (ICFIE), pp. 123–130 (2020)

Research on Software Synthesis Method for Spacecraft Control System Yi Li, Xiaogang Dong(B) , Xiaofeng Li, Bin Gu, Xingsong Zhao, Yanxia Qi, and Bo Yu Beijing Institute of Control Engineering, Beijing 100090, China [email protected]

Abstract. As a research hot pot of computer science, software intelligent synthesis is of great significance for the development of spacecraft embedded products with high reliability and high efficiency. In this paper, the current research status of software synthesis methods is investigated. Facing the problems of large search space and ambiguity of user intention expression in practical applications, a software synthesis method for spacecraft control system is proposed based on the idea of integrated circuit synthesis. The semantic expression model and software IP modeling method for specific scenarios are established. Finally, the typical case of satellite sun capture is taken to verify the method. The test results show that the method in this paper is correct and feasible, and provides technical support for the subsequent construction of software IP library and rapid program synthesis of spacecraft embedded software. Keywords: Software synthesis · Software IP · Semantic model · Spacecraft control software

1 Introduction Control system is an important part of spacecraft, as the soul of control system, the scale and complexity of software are increasing with the continuously improving degree of autonomous intelligence of space missions [1]. In order to better meet the new needs of future space missions, it is necessary to further improve the efficiency and quality of software development by new methods and theories [2]. In recent years, the academic and industrial circles have carried out a lot of research on improving the intelligent development ability of software, among which the program synthesis method is getting more and more attention [4]. The concept of program synthesis refers to the automatic construction of computer programs according to the user’s intention [3]. It is a way to realize the self editing of computer programs [6]. A influential achievement in this direction is the syntax guided program synthesis method SyGuS (syntax guided synthesis) proposed In 2013 [5]. Based on SyGuS, literature [8] proposed a method synthesis enumeration search and top-down deductive search. Literature [9] provided an algorithm for solving problem selection in interactive program synthesis using optimal decision tree. Facing to the embedded software architecture generation, literature [7] established an embedded © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 309–318, 2023. https://doi.org/10.1007/978-981-19-9968-0_37

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system model integration semantics and model transformation architecture generation method. However, the above methods only perform well in small-scale programs, cannot adapt to large-scale software development such as spacecraft control software. It is still difficult to obtain and accurately describe the software requirements specification; The ability to quickly search in the huge program space to find programs that meet the specifications is limited [1]. In this paper, a software synthesis method for spacecraft control system is proposed based on the idea of integrated circuit (IC) design. The high-level description and logic synthesis is introduced into the modeling and abstraction of software reusable assets, the semantic expression model and software IP modeling method for specific scenarios are established. Taking the typical working condition of satellite sun capture as an example, and the functional software products are constructed by means of software IP synthesis. The test results show that the method in this paper is correct and feasible, and provides technical support for the construction of software IP library and rapid and intelligent program synthesis of future spacecraft embedded software.

2 Abstract Expression and Semantic Model for Software IP 2.1 Enlightenment of IC Design Thought for Software Reusable Assets The traditional software development based on reusable assets is the system integration and assembly based on software components to realize software reuse, such as Simulink [10], AADL [11], the temporal automate generation method [12] and the hybrid automate generation method [13]. The above work has a weak ability to encapsulate component behavior. The current spacecraft embedded software is mainly developed by customization method, and the utilization rate of software assets is low. In IC project, circuit system design and IP core design are separated from each other, which leads to the automation of large-scale IC design. Digital circuit designers just need to search and call the required IP cores in the library to complete the rapid synthesis of complex circuits in a short time Consider the software reusable assets are similarly abstracted and semantically constrained based on IP core for the embedded software, the rapid synthesis of software products based on software IP is feasible. 2.2 Model Definition of Software IP Based on the related concepts of software IP proposed in reference [1], this paper establishes a software IP model for the spacecraft embedded software. Different from packages (or libraries) in traditional programming languages, the Software IP is a kind of specification of formal description, with independent symbolic expression and graphical expression, and can be automatically mapped to software code. The following features are essential for Software IP: (1) A reusable domain knowledge unit with relatively independent functions and explicit context dependencies and constraints; (2) Domain specific description language can be used to describe at high level; (3) It can be managed in the way of database according to certain regulations and integrated into the system through search.

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2.2.1 Appearance Model of Software IP As a reusable knowledge unit, this paper defines the appearance model of software IP as the following triples: Entity = port Element Definition: port = {direction, type}; Indicates the external access port of software IP. It is an interface for data interaction with other software IP, which contains two attributes: direction and type, where direction refers to the direction of interface signal, including input, output and input/output, and is defined as follows: direction = {in, out, inout}; type refers to the interface data type, which is defined as: type ∈ (DataTypestd ∪ DataTypeusr ), Represents all data types defined by the software programming language, and the user-defined data structure. parameter Element Definition: parameter = {value, type}; It refers to the configuration parameters of software IP, which is used to set the initialization state of software IP or change the structure, behavior or running state of software IP. It contains two attributes: value and type, where value represents the specific value of the configuration parameter, and the type attribute defines the type attribute of the same port element. behavior Element Definition: behavior = {function, time sequence}; Indicates the behavior of software IP, and describes the function and timing characteristic of software IP. The behavior element contains two attributes: function and time sequence; Function is a function based on normalized description, and time sequence is a timing characteristic of normalized description. Based on the above definition, the appearance of an independent software IP in tabular form is defined as follows (Table 1): 2.2.2 Semantic Model of Software IP The semantic model describes the function, structure and behavior of the system in a given formal language [14]. The semantic characteristics of software IP are composed of its attributes, actions, axioms and logic conveyed by behavior description [14].The software IP semantic model established in this paper includes three dimensions: interface semantics, behavior semantics and structure semantics. 1 interface semantics Si The dependency between all nodes in the appearance model and external data and signals (where the node corresponds to the port element in the appearance model). Si = { direction, type}; Si contains two attributes: direction and type. Where direction refers to the external dependency of the interface, including dependency, impact, dependent & influential. It is defined as follows: direction = {dependent, influential, dependent & influential}; type represents interface semantic types, including data dependency and control dependency. The definitions are as follows: type = {data, ctrl}; where, data refers to the data dependency and ctrl refers to the ctrl dependency.

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2 behavior semantics Sb All internal action sets of the software IP. Sb = ; where commandi represents a collection of all instructions within a behavior element in appearance model, functioni represents all functions and software IP collections within the behavior element. 3 structure semantics Ss Execution sequence and branch structure of all actions in software IP behavior semantic sb. Ss = {sequential, single_branch, multi_branch}; Structural Semantics includes three attributes: sequential, single_branch and multi_branch. For sequential execution, the symbol “B1;B2” is used to indicate the sequence of actions, for single_branch, the symbol “B1|B2” is used to indicate the different branches, while the multi_branch expressed in FSM (finite state machine).

3 Domain Oriented Software IP Construction This paper selects a typical working condition of spacecraft control system - Satellite sun capture, establishes software IP appearance model and semantic model for this specific field, the satellite sun capture function is a common safety mode during mission: the satellite rotates in a certain direction with a fixed angular speed until the sun sensor sees the sun and keeps the satellite attitude towards the sun to ensure the energy security of the satellite. The process can be divided into 3 phases: Rate Damping, Sun Search and Sun Oriented, as shown in the follow phase-event matrix. Table 1. Example of software IP definition in tabular form. Software IP entity

Element

SoftwareIP int port1 :in char port2:in para1 para2 ĂĂ paraN

float port3:out

double port4:out Behavior= < function, time sequence>

Attribute

Realization form

port

Direction/type

parameter

Value/type

Variable data structure

behavior

Function time sequence

Function

As shown in Table 2, the satellite sun capture function can be divided into the following four software IP: 3.1 Data Sample Software IP: DataSample DataSample software IP completes the data sample and conversion of gyro and sun sensor, corresponding to the “data sample” column in Table 2.

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Table 2. Phase event matrix of satellite sun capture function. Event Phase

Data sample

Attitude calculate

Control calculate

Rate damping

Collect gyro and Calculate satellite Calculate the air sun sensor data body angle and injection control angular velocity quantity according to the current attitude

Phase transition When the angular velocity is less than the predetermined value, it turns to the sun search phase

Sun search

When the sun sensor shows that the sun is seen, turn to the sun orientation phase

Sun orientation

–

Appearance Model: Software IP entity

DataSample

Element

Name

Type

Direction

port

gyro [3]

uint32

in

sun [2]

uint16

in

uint32 gyro[3] :in char seesun[2]:out double sunang[2]:out uint16 sun[2]:in double rate[3]:out

sunang [2], rate [3] double out seesun [2], done

char done:out Behavior= < sample gyro, sample sun sensor>

char

out

parameter – behavior

Sample the gyro sensor data, convert to rate data; sample the sun sensor data, convert to see sun flag and sun angle;

Semantic Model: Name

Type

Direction

Si

gyro [3], sun [2], seesun [2], sunang [2], rate [3]

data

Dependent

rdy, done

ctrl

Influential

Sb

B1: sample the gyro sensor data, convert to rate data; B2: sample the sun sensor data, convert to see sun flag and sun angle;

Ss

B1; B2

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3.2 Attitude Calculation Software IP: AttCalc AttCalc software IP receives the output data of DataSample software IP, calculates the current attitude angle and angular velocity of the satellite, corresponding to the “attitude calculation” column in Table 2. Appearance Model: Software IP entity AttCalc

Element

Name

Type

Direction

port

seesun [2], rdy

char

in

double A[3]:out double R[3]:out char done:out

char seesun[2]:in double sunang[2]:in double rate[3]:in

done

char rdy:in

out

sunang [2], rate [3]

Ainit[3] Rinit[3]

Behavior= < calculate attitude angle,calculate attitude angle rate>

double

A [3], R [3]

in out

parameter

Initial value of angle and rate

behavior

calculate attitude angle, calculate attitude angle rate;

Semantic Model: Name

Type

Direction

Si

gyro [3], sun [2], seesun [2], sunang [2], rate [3]

data

Dependent

rdy, done

ctrl

Influential

Sb

B1: sample the gyro sensor data, convert to rate data; B2: sample the sun sensor data, convert to see sun flag and sun angle;

Ss

B1; B2

3.3 Control Calculation Software IP: CtrlCalc Ctrlcalc software IP receives the output attitude angle and angular velocity of AttCalc software IP, calculates the current satellite’s jet control quantity, corresponding to the “control quantity calculation” column in Table 2. Appearance Model: Software IP entity CtrlCalc

double A[3]:out double R[3]:out

uint16 jetout[12]:out char done:out

char rdy:in

PID parameter

Element

Name

Type

Direction

port

A [3], R [3]

double

in

Jetout [12]

uint16

out

rdy

char

in

actuator fix parameter

Behavior= < calculate attitude control moment, output the jet to actuator>

done

out

parameter

PID parameter, actuator fix parameter

behavior

Calculate control moment, output the jet to actuator;

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Semantic Model:

Si

Name

Type

Direction

A [3], R [3], jetout [12]

data

Dependent

rdy, done

ctrl

Influential

Sb

B1: calculate control moment; B2: output the jet to actuator;

Ss

B1; B2

3.4 Phase Transition Software IP: PhaseCvt PhaseCvt software IP receives the output attitude angle and angular velocity of AttCalc software IP, maintains the current phase and next phase according to the attitude data, corresponding to the “phase conversion” column in Table 2. Appearance Model: Software IP entity PhaseCvt

double A[3]:out double R[3]:out char rdy:in

Element

Name

Type

Direction

port

A [3], R [3]

double

in

char next phase:inout current phase

next phase attitude cond

Behavior= < judge the attitude condition, convert to next phase >

rdy

char

in

phase

char

inout

parameter

Current phase, next phase, attitude cond

behavior

Judge the attitude condition, convert to next phase;

Semantic Model: Name

Type

Direction

Si

A [3], R [3]

data

Dependent

phase, rdy, done

ctrl

Influential

Sb

B1: from phase ratedamp to phase sun search; B2: from phase sun search to phase sun oriented; B3: Null

Ss

Case current phase: when ratedamp: B1|B3; when sun search: B2|B3; when sun oriented: B3

4 Domain Oriented Experimental Verification Based on the method above, this section selects an actual satellite engineering as the test object, synthesis the sun capture software by the four software IP mentioned above. The composition diagram of four software IP is shown in Fig. 1, DataSample IP samples the signal gyroscopes sensors and digital sun sensors, passes the processing results to

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AttCalc IP; by the results, the AttCalc IP calculates the attitude angle and angle velocity of satellite, by which the CtrlCalc IP generates the jet control quantity to maintain the attitude of satellite and the PhaseCvt IP maintains the current phase and next phase according to the attitude. DataSample

uint32 gyro[3] :in char seesun[2]:out double sunang[2]:out uint16 sun[2]:in double rate[3]:out char done:out Behavior= < sample gyro, sample sun sensor>

AttCalc

char seesun[2]:in double sunang[2]:in double rate[3]:in char rdy:in

double A[3]:out double R[3]:out char done:out Ainit[3] Rinit[3]

Behavior= < calculate attitude angle, calculate attitude angle rate>

CtrlCalc

double A[3]:out double R[3]:out char rdy:in

uint16 jetout[12]:out char done:out PID parameter

actuator fix parameter

Behavior= < calculate attitude control moment, output the jet to actuator>

PhaseCvt

double A[3]:out double R[3]:out char rdy:in

char next phase:inout current phase

next phase attitude cond

Behavior= < judge the attitude condition, convert to next phase >

Fig. 1. Schematic diagram of sun capture software product synthesis based on software IP.

The relevant engineering parameters are set as follows: (1) The initial angle is 0°, the initial angular velocity is {0°/s, 0.2°/s, −0.15°/s}; 2) With 3 gyroscopes sensors and to 2 digital sun sensors to measure attitude angle for satellite; 3) The condition for the transition from the Rate Damping phase to the Sun search phase is that the absolute values of the attitude angular velocities are less than 0.1°/s. As shown in Fig. 2, the testing results shows the process of sun capture: the angle velocity was reduced firstly in rate damp phase, and then the satellite rotates in a certain direction with a fixed angular speed until the sun sensor sees the sun during the sun search phase, and finally and finally the attitude angle is stabilized at the attitude towards the sun.

Fig. 2. Test results of sun capture software products based on software IP.

For decades of technological development, the spacecraft control software has accumulated quantity of reusable components. By the software IP appearance model and

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semantic model established in this paper, establishing a software IP library and IP search technology based on requirements expression specification, software products can be quickly synthesized and various applications in spacecraft control system can be adapted.

5 Conclusion and Prospects This paper investigates the current research status of software synthesis methods, combs the problems of large search space and ambiguity of user intention expression in the current practical application, and proposes a software synthesis method for spacecraft control system based on the IC synthesis idea, The semantic expression model and software IP modeling method for the specific scenario of satellite sun capture function are established. Finally, the software product is synthesized by taking the satellite sun capture as an example, and the results are verified by experiments. The results show that the method in this paper is correct and feasible, and provides technical support for the construction of software IP library and rapid intelligent program synthesis of spacecraft embedded software. Acknowledgment. This work is supported by the National Natural Science Foundation of China (6219735).

References 1. Yang, M.F., Gu, B., Duan, Z.H., et al.: Intelligent program synthesis framework and key scientific problems for embedded software. Chin. Space Sci. Technol. 42(4), 1–6 (2022) 2. Li, Y., Li, L., Guo, M.S., et al.: High confidence development technology of application software for GNC subsystem of Chang’E-5. J. Deep Space Explor. 8(3), 244–251 (2021) 3. Gu, B., Yu, B., Dong, X.G., et al.: Intelligent program synthesis techniques: literature review. J. Softw. 32(5), 1373–1384 (2021) 4. Liu, B.B., Dong, W., Wang, J.: Survey on intelligent search and construction methods of program. J. Softw. 29(8), 2180–2197 (2018) 5. Alur, R., Bodik, R., Juniwal, G., et al.: Syntax-guided synthesis. IEEE (2013) 6. Gulwani, S., Polozov, O., Singh, R.: Program synthesis. Found. Trends Program. Lang. 4(1–2), 1–119 (2017) 7. Zhang, N., Dong, Y., Xue, F.: Integrated modeling method of complex embedded system with SAVI framework. Int. J. Perform. Eng. 16(2), 223–237 (2020) 8. Lee, W.: Combining the top-down propagation and bottom-up enumeration for inductive program synthesis. Proc. ACM Program. Lang. 5(POPL), 1–28 (2021) 9. Ji, R., Liang, J., Xiong, Y., et al.: Question selection for interactive program synthesis. In: Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 1143–1158 (2020) 10. Simulink User’s Guide (2013). http://www.mathworks.com/help/pdf_doc/simulink/s1_using 11. Feiler, P.H., Gluch, D.P., Hudak, J.J.: The architecture analysis & design language (AADL): an introduction. Carnegie-Mellon Univ Pittsburgh PA Software Engineering Inst (2006)

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12. An, J., Chen, M., Zhan, B., Zhan, N., Zhang, M.: Learning one-clock timed automata. In: Biere, A., Parker, D. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2020. Lecture Notes in Computer Science(), vol. 12078. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45190-5_25 13. Jin, X., An, J., Zhan, B., et al.: Inferring switched nonlinear dynamical systems. Form. Aspects Comput. 33(3), 385–406 (2021) 14. Wang, A., Liu, J.: Model mapping approach based on semantics consistency. China Science Technology Information (2008) 15. Hou, J.: Semantic consistency of component-based architecture model transformation. J. Jilin Univ. (Eng. Technol. Ed.) 1(40) (2010)

Prediction and Analysis of Infectious Disease Visit Data Based on DPC Rui Zhang1(B) , Yuhui Song1 , and Tao Du2,3 1 School of Information Engineering, Shandong Management University, No. 3500, Dingxiang

Road, Changqing District, Jinan 250357, Shandong, China [email protected] 2 School of Information Science and Engineering, University of Jinan, Jinan, China 3 Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

Abstract. Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work. Keywords: Infectious disease · Density peak clustering · Category features digitized

1 Introduction According to the statistics of WHO, China is still one of the countries with high burden of tuberculosis in the world, and the prevention and control of tuberculosis is urgent [1]. The characteristics of difficult diagnosis and rapid infection of tuberculosis have brought great difficulties to the prevention and control work [2]. With the deepening of big data computing and intelligent algorithms in the field of medical application, more and more researchers also began to apply intelligent methods to the auxiliary formulation of tuberculosis prevention and control strategies. Fu zhiou’s team used a variety of machine learning algorithms and joint models to predict the development trend of pulmonary tuberculosis, and proposed an ARIMA-SVR joint neural network model [3]. Wang Hui proposed GCTRANS model to improve Arima to improve prediction accuracy and effect [4]. The spatial correlation local index (LISA) clustering map was used to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 319–327, 2023. https://doi.org/10.1007/978-981-19-9968-0_38

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analyze the temporal and spatial distribution characteristics of pulmonary tuberculosis, so as to assist the decision-making of prevention and control [5]. Kang Wanli applied spatial analysis method to study the spatial distribution characteristics and spatial accumulation of tuberculosis in China, providing a more scientific basis for the prevention and treatment of tuberculosis [6]. The above use the neural network analysis platform to directly analyze the data of pulmonary tuberculosis, and uses the improved neural network analysis method to support the existing data of pulmonary tuberculosis diagnosis. Unsupervised clustering analysis has more advantages in mining unknown knowledge, which is also the attraction of unsupervised learning. This paper uses the latest classical density clustering algorithm - Density Peak Clustering (DPC) algorithm [7] to deeply mine the diagnostic data of pulmonary tuberculosis in recent years, so as to find deep knowledge and laws and guide the practice of epidemic prevention and control.

2 Related Work DPC algorithm is not only superior in finding non spherical distributed data, but also satisfactory in mining non-equilibrium distributed data. The original algorithm takes into account not only the spatial distribution of local data, that is ρ, but also the spatial relationship between the local spatial distribution of different samples and other highdensity local areas, that is δ [8, 9]. Suppose there are currently N samples. These two important parameters of DPC are obtained from Eq. (1), (2) and (3). N   (1) χ dij − dc ρi = j=1



χ (d ) =   δi = min dij

1 d ≤ 0 0 otherwise

ρi < ρj , 1 ≤ i , j ≤ N

(2) (3)

where d = dij − dc . Based on ρ and δ or γ with sample number to construct the classical decision graph and the improved decision graph respectively to assist in manually determining the appropriate cutoff radius parameter value and the high-density center, where γ = ρ * δ. For example, taking UCI data set unbalance as an example. There are eight clusters in this data set, including three large-scale high-density clusters and five low-density small-scale clusters. It is a typical unbalanced data set, which can be well used to test the performance of the algorithm. Under the condition of truncation radius d c of 5000, two types of decision graph are shown in the Fig. 1. In the left of classical decision graph, there are four (five in fact, two points are so closed that look like one point) points are look like outliers, but they are local high density centers because in the left of them there are many points. Then, there are eight centers in unbalance. Similarly, from the improvement decision graph on the right, eight central points can be determined by enlarging the details. Next, based on the Eq. (4) and these centers, we can get the clustering result as shown in the Fig. 2. Where cluster(i) represents the class the sample i belongs to. cluster(i) = cluster(j) ρi < ρj && dij = δi

(4)

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Fig. 1. Decision graph.

Fig. 2. DPC on unbalance data set.

3 Method

Fig. 3. Data mining flow chart.

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The method for tuberculosis data processing mainly includes two parts, as shown in Fig. 3, data preprocessing and visualization with clustering analysis. The data preprocessing stage includes the following links such as feature selection, bad data cleaning, data standardization of raw Tuberculosis data. The visualization with clustering analysis stage includes data mining by DPC algorithm, establishment of clustering model and show visualization of analysis results that three links. 3.1 Data Preprocessing The data processed this time are the treatment information of tuberculosis patients in all counties and districts of a prefecture level city. The data comes from the team of Du Tao, a teacher from University of Jinan. We simply reduce, standardize and digitize the collected data, reduce the dimensions irrelevant to data analysis, fit, supplement or delete the missing data, and replace the text, date and other information with digital information that can represent its meaning, so as to facilitate the processing of data mining algorithms. The original data includes 18 dimensional features, dimensions after reduction: 1. First diagnosis area; 2. First diagnosis unit; 3. Gender; 4. Age; 5. Occupation; 6. Registered residence; 7. Source of patients; 8. Diagnosis classification of patients; 9. Diagnosis results; 10. Treatment classification; 11. Reaction time; 12. Diagnosis time. Defect data processing: considering the huge amount of data and the relatively small proportion of incomplete data, it will not have a substantial impact on the clustering results, so incomplete records in features such as the date of first diagnosis, the date of symptom occurrence, the date of starting diagnosis and the time of treatment end are directly deleted. Data normalization: The first diagnosis area, first diagnosis unit, occupation, patient source, registered residence, patient diagnosis type, diagnosis result, drug resistance, actual treatment management mode, system management, reason for the end of treatment course, 0-month sequence X-ray examination result and other dimensions are directly normalized by the percentage of the corresponding statistical value in the total value of the corresponding attribute value. Specifically, the gender dimension is set to 0 for men and 1 for women. The treatment classification dimension is set to 0 for initial treatment and 1 for retreatment. The age dimension is divided into [0,18], greater than 18, and the proportion of statistical values in each interval is taken as the normalized one. The dimensions of reaction time and diagnosis time are divided into [0,3], [4,14], [15,30], [31,180]. Statistics are carried out in five intervals greater than 180, and the proportion of statistical values in each interval is taken as the normalized value. The treatment time dimension is divided into [0,7], [8,14], [15,30], [31,180]. Statistics are made in five intervals greater than 180, and the proportion of the statistical value of each interval is taken as the normalized value. 3.2 Data Analysis and Visualization After the above data processing, the standard data set is obtained. In this paper, the DPC algorithm is used to mine the standard data. After several clusters are obtained, the characteristics in the cluster are statistically analyzed, and the analysis results are

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visualized by Echarts visualization tool to assist decision-making. The specific process is shown in the Fig. 4:

Fig. 4. Data mining and visualization process.

4 Experiments In this paper, the static centralized DPC clustering algorithm based on density is adopted, and the classical decision graph and improved decision graph are used to jointly find the density center and determine the number of clusters. Due to the large scale of the complete data, it is not necessary to mine all the data as a whole to determine the center. 4000 pieces of data are randomly selected to find high-density centers, in which the truncation radius d c is equal to 2, and the number of cluster centers is 5 according to the decision diagram. The classical decision graph and the improved decision graph are shown in Fig. 5 respectively.

Fig. 5. Decision graphs.

In the Table 1, the proportion of members in each cluster can be clearly seen. Obviously, the distribution of tuberculosis data in this city is extremely unbalanced. In Fig. 6, by visualizing the proportion of each patient’s occupation in the five clusters, it can be clearly observed that the characteristics of each cluster are different,

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Cluster1

Cluster2

Cluster3

Cluster4

Cluster5

773

5833

1297

490

18056

but the proportion of the overall number of farmers is significantly higher than that of other groups. It can be seen that the epidemic prevention of infectious diseases in rural areas is the top priority. Although the population flow in rural areas is smaller than that in cities, compared with urban health and epidemic prevention measures, rural areas are still relatively backward in the prevention and response of infectious diseases, and tuberculosis is highly infectious.

Fig. 6. Analysis on the proportion of population in various occupations.

In Fig. 7, by comparing the population proportion of each district and county in each cluster, it can be seen that the proportion of patients in District 2, 5 and 13 is relatively large, while in the sixth sub figure, it can be seen that the population of 13 is higher than that of other counties and regions. In the same proportion, the large number of confirmed cases is due to the large population base and strong infectivity of tuberculosis, while the population of District 2 is relatively small, but there are many patients, indicating that District 2 needs to strengthen its work in epidemic prevention and improve its ability of prediction and prevention (Fig. 8). Finally, in the Fig. 9, through the continuous visualization of the proportion of personnel in various diagnostic results, it can be seen that the characteristics are obviously different in different clusters. Through the last sub graph, that is, the overall proportion of various patients, it can be seen that the proportion of smear positive patients and smear

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Fig. 7. Analysis on the proportion of population in various districts.

Fig. 8. Analysis on the proportion of patients in various sources

negative patients is relatively large. Separately, in the first cluster, smear positive patients account for a large proportion, that is, if the data characteristics of other dimensions meet the characteristics of the cluster, it is likely to be smear positive patients, while in the second cluster, smear negative patients account for more than 75%. If the overall characteristics of a sample meet the overall characteristics of cluster 2, then the patient is likely to be smear negative patients, whose infectivity is not strong. Active treatment and improving immunity can be cured. Similarly, other characteristics in the cluster can be obtained, In this way, we can help medical staff to achieve a certain auxiliary decision-making function.

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Fig. 9. Analysis on the proportion of patients with various diagnostic results

5 Conclusion In this paper, the DPC algorithm is used to cluster and analyze the unbalanced pulmonary tuberculosis diagnosis data, and the characteristics of the clustering results are statistically summarized. The analysis results are visualized by Echarts plug-in. Combined with the clustering analysis and visualization results, a number of potential rules are obtained according to each characteristic situation, and the guidance is put forward, which shows that the DPC algorithm is effective in the analysis and application of pulmonary tuberculosis diagnosis data.

References 1. Jun, B.: Study on the relationship between tuberculosis control and health education. Digest World Latest Med. Inf. (26), 75–76, 79 (2021) 2. Ying, B.: Optimization of tuberculosis diagnosis based on machine learning and multigroup strategy. Guangdong Medical Association. 2018 annual conference of laboratory medicine of Guangdong Medical Association and the first Southern conference of laboratory medicine, pp. 1–42 (2018) 3. Zhiou, F., Yang, Z., Cheng, C., et al.: Application of time series analysis and machine learning in predicting the trend of pulmonary tuberculosis. China Health Stat. 37(2), 6 (2020) 4. Hui, W.: Research and implementation of tuberculosis infectious disease prediction model based on machine learning. University of Electronic Science and technology (2020) 5. Yinfa, Z., Shufang, L., Zhisong, D., et al.: Analysis of temporal and spatial characteristics of pulmonary tuberculosis in Fujian Province from 2011 to 2020. Chin. J. Anti Tuberc. 43(8), 4 (2021) 6. Wanli, K., Peizheng, L.: Application of spatial analysis method in the distribution of tuberculosis in China. In: 2007 National Academic Conference of China Anti Tuberculosis Association (2007) 7. Jinyin, C., Xiang, L., Haibing, Z., et al.: A novel cluster center fast determination clustering algorithm. Appl. Soft Comput. 57, 539–555 (2017)

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8. Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014) 9. Mehmood, R., Zhang, G., Bie, R., et al.: Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing 208(C), 210–217 (2016) 10. Rezaei, M., Fränti, P.: Set-matching measures for external cluster validity. IEEE Trans. Knowl. Data Eng. 28(8), 2173–2186 (2016)

Research on the Method of Improving the Accuracy of MRP Calculation Through Big Data in Aerospace Enterprise Wen Liu, Ruihua Li(B) , and Yuzhu Li Beijing Institute of Control Engineering, No. 104 Youyi Road, Haidian District, Beijing, China [email protected]

Abstract. MRP calculation is the key of ERP, and while it brings convenience to enterprise planning management, it also leads to deviation in planning due to data inaccuracy involved in the MRP calculation generally. This paper details the method of correcting MRP parameters through big data technology to improve the accuracy of MRP calculations. This method is validated by the practical application case in an aerospace institute. Keywords: MRP calculation · Big data · Parameter optimization

1 Background Since aerospace products are complex in composition, expensive in price, reliable in quality, and highly technical in development, aerospace materials present their own characteristics. For example, there are many varieties of materials, small batch per volume, highly volatile demand plan, long procurement cycle, high quality requirement, etc. [1], which set high requirements for the accuracy of production planning and procurement planning and increase the difficulty of production and procurement. MRP (Material Requirement Planning) calculation is the key function of ERP (Enterprise Resource Planning), which is mainly responsible for the calculation and then gets the production and procurement plan. Therefore, in order to ensure the on-time delivery of products, the accuracy of MRP calculation needs to be improved as high as possible [2]. MRP mainly solves the problem of how many relevant demanding parts need to be produced or procured. It decomposes the products scheduled by the MPS (Master Production Schedule) into the production plan for the respective parts and procurement plan for the purchased parts according to the product BOM (Bill of Material). Referring current inventory data, MRP deduces the demand quantities and dates for the parts based on the delivery quantities and dates for the final products from the MPS in the standard of scheduled time and quantity, until the manufacturing order placement data for all the self-made parts and the purchase order release data for all the purchased parts are derived, and balances the demand resources and available capacity at the same time [3]. MRP is a material resource planning management system supported by computer system. This method suits for single piece and multi-variety small batch manufacturing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 328–333, 2023. https://doi.org/10.1007/978-981-19-9968-0_39

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enterprises, which fits the aerospace manufacturing applications [4]. Many aerospace institutes have established ERP systems and applied MRP deeply. Taking an institute’s ERP system as an example, its MRP functional computing process is seen in Fig. 1.

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Purchase plan of each purchased part (1) Order date and arrival date (2) Demand quantity

Fig. 1. MRP flow chart

According to the delivery date and quantity requirement of products in MPS, regarding the period quantity standard of material as the basis for calculating the recommended time of the material plan, using the BOM as the basis for product parts data decomposition and inventory and in-process and in-transit as resources, MRP executes and decomposes the product requirements into specific parts requirements [5]. According to the calculation material requirement plan for each part, the final production plan and procurement plan are then formed based on purchase or production attributes of the material’s master data. MRP is an ideal planning and scheduling tool that allows the sequencing of procurement and production plans to meet product delivery dates through regular and continuous re-planning and rescheduling, while also allowing for quick forecasting of shortages and overstocks so that measures can be taken in advance to avoid them. This way of generating accurate plans through MRP is very helpful for the product plan and schedule by reducing the pressure for the supply chain and ensuring that the production and procurement of materials are completed on time in the aerospace industry [6].

2 Problems According to the MRP calculation logic, the accuracy of MRP planning data relies on the accuracy of MPS, BOM, the accuracy of inventory information, and the accuracy of period quantity standards. Through practice, it has been found that the accuracy of the demand quantities derived through MRP can be guaranteed if these factors are simultaneously accurate. However, in the process of actual application, it was found that we cannot guarantee that all the factors affecting the results of the MRP calculation are accurate, especially

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the accuracy of the material period standard [7] so that some of the proposed start date for production and procurement derived from MRP could not complete the material production or procurement according to the planned completion time, and there were even very obvious deviation [8]. This leads to the fact that MRP can only guide the demand quantity of production and procurement operations in the actual application process, but cannot be used to guide the production and procurement start time, which also causes the practical difficulties that the plan is poorly executable and cannot be put into production according to the ‘plan’; not only that, because of the characteristics of aerospace procurement materials, such as small quantity of single demand, high variability of demand plan and long procurement cycle, the procurement plan derived from traditional MRP has problems such as insufficient ability to cope with changes, high cost of multiple small purchase and high risk of purchase parts arrival [9]. These problems not only affect the efficiency of supply chain staff, but also increase the difficulty of implementation, and bring great challenges to the construction of ERP, MES (Manufacturing Execution System), SRM (Supplier Relationship Management) and other information systems. These problems boil down to the inaccuracy of the information provided to MRP calculations.

3 Solution Ideas The most effective way to solve these problems is to improve the accuracy of MPS, product BOM, inventory information, and quantity standards, which are the “operational parameters” of MRP. The “operational parameters” directly determine the results of MRP operations, and the process of executing procurement and production based on MRP results is a continuous confirmation and correction of the operational parameters. For example, the production cycle and procurement cycle in the quantity standard are often determined by designers or buyer based on experience, and do not necessarily match the actual time cycle used in the process. If this validation process is carried out continuously and the results are solidified to optimize the data of the quantity standards, it will help to improve the accuracy of MRP calculations. The use of big data technology, through the “data + experience + algorithm model” approach, can provide us with a good way to constantly confirm the parameters and achieve the correction of MRP parameters (see Fig. 2). In the long term research and production management process of enterprises, ERP, MES, SRM and other systems have accumulated a large amount of practical production and procurement related business data. These actual empirical data could be integrated, cleaned and organized to form data assets, and then combined with algorithmic models and big data analysis and mining, and then it can be analyzed to derive correction suggestions for the parameters required for MRP calculations, helping to improve the accuracy of the parameters and ultimately enhance the accuracy of MRP.

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4 Application Cases Based on the above solution ideas, an aerospace institute has carried out various practices to improve the accuracy of MRP calculation in the process of actual production planning management with the help of data assets and big data technology. The following is a typical case to introduce how to improve the accuracy of MRP calculation by correcting the calculation parameters through big data. The material production cycle is an important data in the material’s period quantity standard, since this data predicts the time cycle required for each material in the actual production processing. In the actual MRP calculation, it speculates the production time required based on the production cycle data to for each material node, so the more accurate this data, the more accurate the MRP calculation gives the production suggestion time, the higher the executability of the plan. Material production cycle data is generally maintained manually by users, such as designers, who usually set it up in a predictive manner for the first time and do not perform separate corrective maintenance thereafter. This situation is prone to estimation errors and changes in material production cycles over time due to environmental changes and changes in the ease of access of lower-level materials, none of which can be updated in a timely and accurate manner. This can lead to inaccurate MRP recommendations, which in turn can lead to incorrect transfer orders, making the product production or procurement time deviate from the actual demand time too early or too late, causing unnecessary risk to the supply chain as a whole, and also causing additional costs for the product. In this paper, to improve the accuracy of material production cycle as an example, we first obtain the data from MES system in data assets based on the historical production of

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materials, including material number, production quantity, order start time and current production cycle information of all completed production orders. In order to make a reasonable forecast based on the historical production cycle, this scenario uses the basic data analysis model to analyze the data for several experiments, and finally chooses the least squares method of one-dimensional linear regression for linear fitting and point estimation of individual values for the fitted regression equation. It should be noted here that in the process of consideration we conduced correlation test between single production quantity and production cycle, and found that the correlation between the two was not significant, combined with the actual production situation and considered the uncertainty of the planned quantity in the MRP calculation process, so this factor was not involved in the fitted equation as an influencing factor. The one-dimensional linear regression model is known to be represented as, y = β0 + β1 x + ε

(1)

In this formula, x is the independent variable, y is the dependent variable, β 0 and β 1 are parameters of the model, ε is a random variable of the error term. Several basic assumptions in the regression model are described. (1) The dependent variable y and the independent variable x have a liner relationship. (2) In repeated sampling, the independent variable x takes on a fixed value, i.e., x is assumed to be non-random. (3) The error term ε is a random variable with expectation 0, i.e. E(ε) = 0. (4) The variance σ 2 of ε is the same for all values of x. (5) The error term ε is a random variable that follows a normal distribution and is independent, i.e., ε ~ N(0, σ 2 ). According to the assumptions in the regression model, the expectation value of ε is 0. So the one-dimensional linear regression approach takes the following form, E(y) = β0 + β1 x

(2)

Least squares is a mathematical optimization technique that finds the best functional match for the data by minimizing the sum of squares of the errors [10]. The use of least squares makes it easy to find unknown data and to minimize the sum of squares of the errors between these found data and the actual data. We ranked a material by each production time from 1 to n and used the order as the independent variable factor x in the linear regression model and the period of n times of production as the dependent variable y. For n pairs of observations of x and y, a linear fit was performed using least squares estimation, resulting in the corresponding estimated regression equation: y= β 0 +βˆ 1 x. The estimated regression equation is then used to find an estimate of an individual value of y for a specific value yˆ n+1 on the condition of a specific value x n+1 for x. Then the value of y n+1 is the proposed parameter correction for this material calculated by the algorithmic model. After the parameter correction suggestion is obtained, the cycle data is automatically updated to the business system on a monthly basis to maintain the dynamic accuracy of the production cycle data. 





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After 3 months of continuous correction of the material production cycle, the production cycle of the material is compared with the actual production cycle, and the relative error δ statistics are shown in Table 1. Table 1. Production cycle relative error statistics Relative error range

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It is obvious from the statistics that the production cycle error of the material is significant reduced after the correction according to the parameter correction suggestion, and the accuracy of MRP calculation is effectively improved.

5 Summary The validation of the application case demonstrates the feasibility of using big data technology to improve the accuracy of MRP calculations by making corrections to MRP calculation parameters. In addition to the production cycle correction described in this paper, MRP parameters can be corrected in other ways, including: purchasing cycle, BOM raw material usage, safety stock strategy, planned marginal code, receiving processing time, minimum lot size, and so on. We hope that this article will help readers in improving the accuracy of MRP calculations.

References 1. Chen, S., Cao, S., Li, H., Yu, L.: Analysis and research on value enhancement in aerospace materials supply chain management based on big data. Netw. Informatiz. 36(12), 154–157 (2017) 2. Zhang, H., Wei, J., Jiang, N.: Insights from supply chain management of large aerospace companies in developed countries. Logist. Technol. 39(11), 121–124 (2016) 3. Dai, J.: Example study of MRP in ERP system. J. Changsha Civ. Aff. Vocat. Technol. Coll. 17(01), 74–75+144 (2010) 4. Ye, F.: Research on Supply Chain Management-based Material Procurement Management Strategy of Aerospace Academy. Harbin Institute of Technology, Harbin (2010) 5. Zhong, L.: The algorithm of materials net requirements calculation in MRP. Comput. Knowl. Technol. (19), 4564–4566 (2013) 6. Xie, Z.: Research and Implementation on Material Requirements Planning Based on Forecast Order. Soochow University, Jiangsu (2013) 7. Du, Y.: Research on the Global Standards and Benefits of Iron and Steel Rolling in Baotou Steel. Inner Mongolia University of Science & Technology, Inner Mongolia (2021) 8. Li, Q.: Flexible improvement of MRP in Jobbing work circumstance. J. Jiangsu Univ. Sci. Technol. (Nat. Sci. Ed.) 30(4), 355–361 (2016) 9. Chen, M.: Management and collaborative research of project plan, production plan and procurement plan. SHANGPINYUZHILIANG (9), 29–30 (2020) 10. Jia, J., He, X., Jin, Y.: Statistics. 7th edn. China Renmin University Press, Beijing (2018)

Design and Application of Bad Block Management Strategy for On-Board Solid-State Memory Hongjun Ma(B) , Jianhong Xiao, Dayang Zhao, and Yifeng He China Academy of Space Technology, Xi’an, China [email protected]

Abstract. NAND flash has a wide application in on-board solid-state memory for its high storage density and fast read-write speed. However, due to the inevitable bad block of flash, the accuracy and speed of mass storage array are affected. According to the effect caused by the existence of bad block in solid-state memory, this paper proposes a real-time bad block detection and replacement method. The practice proves that this management strategy of bad block can effectively improve the reliability of on-board solid-state memory and the success rate of space mission. Keywords: NAND flash · Bad block · Management strategy

1 Introduction On-board solid-state memory is one of the core units in satellite data processing system, which carries the payload date recording and playback function. Solid-state memory which is based on NAND flash has become the main data storage method for satellites and other aerospace equipment because of its high storage density, small size, light weight, vibration resistance, impact resistance, wide operating temperature [1, 2]. Due to manufacturing problems, NAND flash will inevitably produce bad blocks when it leaves the factory. And over time, as flash reaches erasure limits, the number of bad blocks will increase [3, 4]. The generation of bad blocks will affect the storage rate, the accuracy and integrity of stored date, and even determine the success or failure of space exploration mission. In order to ensure the reliability of stored date, it is necessary to consider the processing method of bad block. Efficient and reliable bad block management strategy is of great significance to on-board solid-state memory.

2 On-Board Solid-State Memory Architecture 2.1 System Composition and Principle On-board solid-state memory adopts CPU (central processing unit) processor and FPGA (field programmable gate array) to realize data storage and bad block management. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 334–340, 2023. https://doi.org/10.1007/978-981-19-9968-0_40

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block diagram of solid-state memory is shown in Fig. 1. The components involved in flash bad block management include CPU processor, DMA (direct memory access) controller FPGA, MRAM (magneto resistive random access memory) and flash storage array. DMA controller FPGA is connected with flash storage array and MRAM to realize the operation of flash storage array and MRAM. The CPU processor operates MRAM and flash storage array through DMA controller FPGA to obtain flash state parameter and maintain address mapping table [5, 6].

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Fig. 1. Block diagram of solid-state memory

2.2 Traditional Bad Block Management Strategy Traditional bad block detection and replacement method is realized by reading the erasure status word of flash self-check [7]. During the flash self-check, the erasure status word is used to confirm the status of the current storage block, which is either good or bad. This strategy is suitable for the situation that the performance of bad block is completely degraded. However, it is found in engineering practice that there exist critical bad block with correct erasing state words but incorrect status. For critical bad block that cannot be detected by erasing flash, the traditional bad block management strategy of on-board solid-state memory is to lock the bad block address by analyzing the original data received on the ground and replace bad block position by satellite instruction. This method requires a lot of manpower and time cost.

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Therefore, the traditional bad block management strategy cannot well adapt to the current and future requirements of high reliability and long-life satellites [8].

3 Bad Block Detection Replacement Strategy The problems of poor real-time performance and low accuracy of traditional bad block processing methods, a real-time bad block detection method based on the correctness of playback data was proposed. The Process of bad block management for solid-state memory is shown in Fig. 2. In the normal playback working mode of the satellite, the real-time EDAC (error detection and correction) function is used to detect bad blocks, and the detected bad blocks are automatically replaced. EDAC is an important effect of data fault-tolerant design in solid state memory.

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The CPU processor telemetry notifies the ground of the presence of new bad block and maps the bad-marked addresses in the address mapping table to the back-up area during memory pre-shutdown.

Complete

Fig. 2. Process of bad block management

In the data recording process of DMA controller FPGA, hamming code is used for every 48-bit of data, and 8-bit EDAC code is generated and stored in a special flash

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independent of the effective data storage area. During data playback, DMA controller FPGA reads the original 48-bit load data and 8-bit EDAC code data simultaneously form flash memory, and then corrects the 48-bit load data according to 8-bits EDAC code. If a 1-bit error occurs, FPGA corrects the error according to the EDAC code and restore the correct 48-bit data output. If two or more bits of error occur, the 48-bit payload data cannot be correctly restored, but a multi-bit error message can be given. According to years of engineering practice experience, there is almost no multi-bit error in normal 48-bit load data. Therefore the interpretation basis of flash bad block is redefined, and the flash block that exceeds the error correction capability of EDAC designed by solid state memory is real bad block. If multiple multi-bit errors occur in flash data of the same page, the address block of current playback is considered as bad block. DMA controller FPGA generates bad block identifier and marks bad block location ‘1’ in the address mapping table according to bad block identifier and the current replayed block address. DMA controller FPGA notifies the CPU processor of the new bad block and the CPU processor sends it down to the ground via telemetry. During memory preshutdown, the CPU processor checks the address map table and replaces the address map marked as bad block ‘1’ with the backup zone.

4 Implementation of Bad Block Management Strategy The implementation method of bad block management strategy is shown in Fig. 3.

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Fig. 3. Implementation diagram of bad block management strategy

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4.1 Data Detection Module This module is implemented in DMA controller FPGA and is responsible for using EDAC function. In the process of playback, the module determines whether the data has multi-bit errors and uses this as the bad block detection criterion to generate bad block identification. The bad block flag is output to telemetry output module and bad block marking module, and finally passed to the bad block replacement module. 4.2 Bad Block Marking Module This module is implemented in DMA controller FPGA and generates bad block identification after detecting the bad block. In this module, the MRAM timing sequence of the address mapping table is generated, and the representation content of the current playback address in the address mapping table is placed at a high position ‘1’ to identify the current block as bad block. 4.3 Telemetry Output Module This module is implemented in CPU processor. The CPU processor passes reads back the DMA block identifier status through IO port and sends the DMA block identifier down through telemetry to inform the ground of the new bad block. 4.4 Bad Block Replacement Module This module is implemented in CPU processor. After receiving pre-shutdown instruction, the address mapping table is checked by CPU processor. If the high level of content in the address mapping table representation is found to be ‘1’, it indicates that the block is 000h

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Fig. 4. The replacement schematic of bad block

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a bad block marked by DMA controller, and replace the marked bad block with good block in backup area. The replacement schematic is shown in Fig. 4. Each memory board has an independent address mapping table. In the bad block replacement module, the new memory board is identified based on the DMA bad block identifier, and then the corresponding address mapping table is checked based on the DMA bad block identifier. The range of blocks to be checked is: 000 ~ FFF. CPU processor checks the high level of content in the address mapping table representation. If the high level of content is ‘1’, it indicates that the address represented by this representation is a bad block. The bad block represented by this representation needs to be replaced with the farthest good block in the backup area, and the update of the address mapping table is completed at the same time.

5 Comparison Between Traditional Method and Real-Time Detection Strategy Through the in-orbit measured data of satellites, the comparison results are as follows. Real-time: The time of bad block detection and replacement has changed for at least 6 h to almost real-time. Detection accuracy: Traditional bad block detection method cannot effectively detect the critical bad block, and the real-time detection replacement Strategy of Bad Block Management improves the detection accuracy of bad block by 90% and reduces capacity loss rate by 80%.

6 Conclusion In order to solve the problem of flash bad block, a real-time bad block detection method for on-board solid-state memory is designed in this paper. The in-orbit test proves that this scheme is reasonable and feasible. It is a perfect solution for bad block management and has great application value.

References 1. Furano, G., Fabian, M.: NAND flash storage technology for mission-critical space applications. IEEE Aerosp. Electron. Syst. Mag. 28, 30–36 (2013) 2. Ashraf, M., Dastur, J.: Software based NAND flash management techniques. In: International Conference on Computing, Engineering and Information, pp. 168–171 (2009) 3. Supriya, H.: Study of bad block management and wear leveling in NAND flash memories. Int. J. Res. Eng. Technol. 2(10), 284–288 (2013) 4. Cai, Y., Mutlu, O., Haratsch, E.: Program interference in MLC NAND flash memory: characterization, modeling and mitigation. In: IEEE 31st International Conference on Computer Design (ICCD), pp. 123–130 (2013) 5. Chihiro, M., Ken, T.: Dynamic adjustment of storage class memory capacity in memoryresource disaggregated hybrid storage with SCM and NAND flash memory. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27(8), 1709–1810 (2019)

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6. Dong, G., Pan, Y., Xie, N.: Estimating information-theoretical NAND flash memory storage capacity and its implication to memory system design space exploration. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 20(9), 1705–1714 (2012) 7. Takeuchi, K.: Novel co-design of NAND flash memory and high-speed solid-state drives. Very Large Scale Integr. (VLSI) Circ. 44(4), 1224–1227 (2009) 8. Peng, L., Wei, D.: A competing risk model of reliability analysis for NAND-based SSDs in space application. IEEE Access 7, 23430–23441 (2019)

An Automated Scoring System for Photoshop Course in Secondary Vocational Colleges Peng Liu1 , Zhiyan Wang1 , Xiufang Liu2 , and Wenbo Wan1(B) 1 School of Information Science and Engineering, Shandong Normal University, Jinan, China

[email protected] 2 Jinan Secondary Vocational School of Technology, Jinan, China

Abstract. With the rapid development of information technology and the growing demand for professional skills by professionals, improving students’ practical software operation level has become an important part of students’ skill training. Photoshop, as one of the most commonly used software in the design field, is getting more and more attention. In secondary vocational and technical schools, the Photoshop course has become a major course for information technology majors. Combined with the practical application background, this paper designs a system for automatic scoring of Photoshop images. The system can comprehensively compare and score the similarity of the edges, colors and textures of the two images. We collect the Mean Opinion Score (MOS) of teachers in specialized classes on images and compare our proposed method with other classical methods according to MOS. Experimental results show that our designed Photoshop image scoring system is completely feasible and outperforms other classical methods. Keywords: Image quality assessment · Feature extraction · Scoring system · Photoshop course · Secondary vocational teaching

1 Introduction With the rapid development of information technology and the increasing demand for professional skills of vocational education talents, improving students’ practical software operation level has become an important part of students’ skill training. Adobe Photoshop is software developed by Adobe Systems for image manipulation and design. As one of the most commonly used software in the design field, it has rich functions, wide application scenarios, so it is warmly welcomed by people [1]. Today, in secondary vocational and technical schools, the Photoshop course has become a major in information technology majors. Proficient application has important guiding significance for the cultivation of students’ graphic design skills and the in-depth study of subsequent courses. The Photoshop course is mainly based on the computer-based test, which is divided into objective questions and subjective questions. In the subjective questions, the test questions will give students reference images and instructions. Students will need to use Photoshop software to create the same effect as the reference image. In this process, it © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 341–348, 2023. https://doi.org/10.1007/978-981-19-9968-0_41

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is not mandatory to use a specific tool, nor is it mandatory to process according to the operation steps, but the final processing effect is used as the scoring standard. That is, the grading teacher needs to observe the similarity between the images submitted by the students and the reference images. The higher the score is if the student-submitted image is more similar to the reference image. Conversely, the lower the similarity, the lower the score. Currently, Photoshop’s subjective questions still use the observer’s “Mean Opinion Score”. This method has always been regarded as the best scoring method, but there are problems such as low evaluation efficiency and many subjective interference factors. Considering the practical application background, it is necessary to find a reliable automatic scoring algorithm for Photoshop images, and design a system that can score Photoshop images. The Photoshop image scoring system we designed has three important implications: First, the Photoshop image scoring system can become a software for learners to practice and test by themselves. In their spare time, students can practice relevant knowledge through this system, obtain their own grades through the Photoshop image scoring system, understand their mastery according to the grades, and make self-correction. Second, Photoshop image processing is a basic course for information technology majors in secondary vocational and technical schools. The automatic scoring system can also change the traditional manual review mode, which is a progress to improve the review efficiency and improve the curriculum system. In addition, it is also of positive significance to promote the teaching reform of secondary vocational education and the change of teaching concept. Third, from the perspective of practical research and education, the design and implementation of the Photoshop image scoring system incorporates the concepts of humanism and fragmented learning. The systematic research and development technology can provide research and development ideas for later researchers and educators. At the same time, the research process and method provide a reference for future practical and theoretical research on image similarity. In this paper, we proposed a new Photoshop Image Quality Assessment (PsIQA) method by combing the similarity of the color, edge and texture for the submitted images in photoshop course, and then comprehensively gives the scoring result. We then compared the correlation between the system rating results and the mean opinion score. Experimental results show that our designed Photoshop image scoring system is completely feasible and outperforms other classical methods.

2 Proposed PsIQA Method For a photoshop image, the basic features that make up the whole can be divided into color, texture and edge [2]. Judging the similarity of images by these three features can not only objectively reflect the differences between images, but also be consistent with the subjective judgment observed by human eyes. In order to facilitate an overview of the specific algorithm for scoring in this paper, Fig. 1 presents the framework diagram of the adopted algorithm, and the details are elaborated in subsections:

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Fig. 1. The algorithm frame diagram of the system

2.1 Edge Similarity Usually, when observing an image, the human visual system is the first to receive image information. After research, the edge information of the image plays a very important role in the observation of the image content by the human visual system, and the image edge information is the more important information in the image [3]. We first use the canny operator to extract edges from the reference image and the submitted image, and obtain the edge pixel set ER of the reference image and the edge pixel set ES of the submitted image. Then, the edge pixels of the reference image and the edge pixels of the submitted image are matched. However, when people subjectively score Photoshop images, there are subtle changes in the image position, size, and angle, which actually have little impact on human visual perception. Therefore, without affecting human visual perception, we allow the corresponding pixels in the image of the submitted image to move within a certain range. This range is a rectangle M with (i, j) as the center and blk as the distance. As long as the positioning within the range of the rectangle M is successful, the marking is successful. The calculation of edge similarity is as follows: SIME =

count(match(ER (i,j),ES (x,y))) , (x, y) N

∈ M (i, j, blk)

(1)

Among them, SIME represents edge similarity, match is used to locate the reference image pixels in the range M of the submitted image, count is the total number of successfully matched pixels, and N represents the total number of edge pixels of the reference image. 2.2 Color Similarity The color feature is a very important part of the image, and it has strong robustness and the advantage of rotation invariance [4]. As we all know, every color image in the computer is composed of three channels of red, green and blue. So we can do RGB channel separation of the input Photoshop image. Then, the histogram of each channel of the reference image and the submitted image is calculated, and the histogram saves a lot of important information of the image [5]. The histogram matching result of the R channel is calculated as follows: SIMCR =

min

s (t)) min(HRr (t),HR  T T t=0 HRr (t), t=0 HRs (t)

(2)

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The value range of t is [0 , 255] and the value of T is 255.Using the same method, we can get histogram matching results for three channels of RGB: SIMCR , SIMCG and SIMCB . Finally, the three results are averaged to get the color similarity of the two images by: SIMC =

SIMCR + SIMCG + SIMCB 3

(3)

2.3 Texture Similarity As a basic feature of an image, texture can not only reflect the homogeneous phenomenon in the image, but also show the surface color and grayscale changes of the image [6]. In this part, we mainly perform the following operations for texture similarity: First, we obtain the reference image grayscale co-occurrence matrix GLCM R and the submitted image GLCM S [7]. The second step is to calculate the correlation between the two grayscale co-occurrence matrices. The higher the correlation is, the higher the texture similarity is, and the lower the correlation is, the lower the texture similarity is. SIMT stands for texture similarity, it is calculated as follows: SIMT =

     R R GLCM S − GLCM S m n GLCMmn − GLCM  2    2    R − GLCM R )( S S ) ( m n GLCMmn m n GLCMmn − GLCM

(4)

Finally, the final similarity sim is determined according to the similarity of the three, the calculation method is as follows: SIM = SIME × P + SIMC × Q + SIMT × Z

(5)

The letters P, Q, Z represent the weights of edges, colors and textures, respectively. The value of the weights can also be changed according to the focus of the test. In this paper, we set P to 0.2, Q to 0.4, and Z to 0.4.

3 Proposed Scoring System for Photoshop Course 3.1 Environment The computer used in the experiment in this paper is configured with Intel® Core i7 12700F CPU (frequency is 2.1 GHz) and memory size is 8 GB; the system is developed with python programming language and tested under the 64-bit Windows10 platform.

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3.2 System Function The main purpose of the system is to help users get results quickly, so the interface of the system should tend to be simplified without affecting the functionality and aesthetics. On the contrary, if the interface of the system is too complicated, it will have a bad effect on improving the user’s concentration. The system interface is shown in Fig. 2:

Fig. 2. The interface of the Photoshop image scoring system

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In the Photoshop image scoring system, users can upload reference images and student-submitted images as needed, and the two images can be displayed side-by-side on the monitor. Users can click the test button to get their own scores, and can see their scores visually. At the same time, the pointer on the instrument panel indicates the corresponding position. Users can also make better progress by planning to intuitively understand their abilities, including composition ability, color ability and detail ability.

4 Experimental Results 4.1 Evaluation Protocols In order to test the performance of the scoring system proposed in this paper, we need professional scoring data for comparison. So we built a small database of 1000 images by collecting Photoshop assignments from Secondary vocational and technical schools. Each image is graded by five teachers of the Photoshop Course, and the average of the five teachers’ scores is taken as the Mean Opinion Score (MOS) value of the image. Each teacher needs to be in a specific environment when grading, without the interference of sound and strong light. In order to avoid visual fatigue affecting the grading, we ask teachers to take a 5-min rest after each 20 images to make sure the image’s MOS is accurate. In the field of image quality evaluation, the MOS value is also used as the subjective recognition ground truth [8]. In other words, the MOS value is used as the basis. The closer the score of the automatic scoring system is to the MOS value of the image itself, the better the performance of the system. We chose the commonly used correlation measures Pearson’s Linear Correlation Coefficient (PLCC), Spearman Rank-order Correlation Coefficient (SROCC), Kendall Rank-order Correlation Coefficient (KROCC) and Root Mean Square Error (RMSE) to measure the correlation between the scores obtained by our automatic scoring system and the MOS value of the image itself. PLCC can measure the degree of linear correlation between score and MOS value, and the larger its value, the more linear the correlation. SROCC and KROCC are used to measure the monotonicity and order correlation between score and MOS value. RMSE is a measure of the difference between values, and the smaller its value, the smaller the difference between the system score and the MOS value. PSNR and SSIM are classic image similarity evaluation metrics, they have milestone significance in the field of image research and are widely used for image quality assessment [9, 10]. We will compare experiments with these two classic algorithms. The experimental results are shown in Table 1. Our automatic scoring system indicators are better than other classic algorithms and have good performance. 4.2 System Test We selected typical test scores in the experiment to illustrate the feasibility of the system. As shown in Fig. 3:

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Table 1. Comparison results of the system PLCC

SROCC

KROCC

RMSE

PSNR

0.887

0.759

0.583

11.684

SSIM

0.904

0.801

0.636

10.83

PsIQA

0.973

0.904

0.783

5.826

Fig. 3. Typical test scores in the experiment

Among them, Fig. 3(b) is less different than the reference image, only missing the word Mid-Autumn Festival, and its similarity calculation score with the standard answer image is 99. Figure 3(c) is similar to the reference image, the edge features are partially missing, the color and texture are basically consistent, and the calculated score is 88. Figure 3(d) shows obvious missing elements compared to the reference image, with a calculated score of 64. The image in Fig. 3(e) is very different from the reference image, the color and texture information of the image is severely damaged, and the calculation score is 26. Figure 3(f) An image completely irrelevant to this exam, with a calculated score of 0. 4.3 Computational Cost Since students need a lot of skills of Photoshop to practice, teachers are often faced with a huge image grading job. Manual scoring is time-consuming and labor-intensive, and personal aesthetics are different, so the scores given will be affected by a large number of external factors. We tested the performance test of the system’s computing speed through experiments. The test results show that the user’s interaction response time is very short whether it is processing a single image or multiple images. The results are shown in Table 2:

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Test item

Test results

The time it takes to score an image

0.34 s

Time taken to score a set of images (about 30 to 50 images)

5.94–8.26 s

Time taken to score 1000 images

112.72 s

5 Conclusion This paper designs a Photoshop image scoring system, which can judge the similarity between the reference image and the submitted image based on the features of edge, color and texture, and then give a comprehensive score. Experiments show that the Photoshop image scoring system we established is feasible. Based on the characteristics and needs of learners, this system designs a concise and effective interface. In the score display interface, users can not only see their own scores, but also their grades and analysis of their scores. Especially for students in secondary vocational and technical schools, the Photoshop image scoring system is a very convenient learning tool, which can help them understand their mastery of photoshop anytime, anywhere. And it solves the problem that performance feedback cannot be obtained without subjective scoring, and improves the efficiency of self-help learning.

References 1. Satish, M.D.P.: A review paper on comparative study in advancements in photoshop. IJNRDInt. J. Novel Res. Dev. (IJNRD) 7(2), 352–361 (2022) 2. Varish, N.: A modified similarity measurement for image retrieval scheme using fusion of color, texture and shape moments. Multimed. Tools Appl. 81, 1–33 (2022) 3. Zhang, X., Feng, X., Wang, W., Xue, W.: Edge strength similarity for image quality assessment. IEEE Signal Process. Lett. 20(4), 319–322 (2013) 4. Wang, W., Lin, Y., Zhang, S.: Enhanced spinning parallelogram operator combining color constraint and histogram integration for robust light field depth estimation. IEEE Signal Process. Lett. 28, 1080–1084 (2021) 5. Veluchamy, M., Subramani, B.: Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction. Appl. Soft Comput. 89, 106077 (2020) 6. Tian, X., Chen, Y., Yang, C., Ma, J.: Variational pansharpening by exploiting cartoon-texture similarities. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021) 7. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973) 8. Hofbauer, H., Autrusseau, F., Uhl, A.: To recognize or not to recognize–a database of encrypted images with subjective recognition ground truth. Inf. Sci. 551, 128–145 (2021) 9. Sabilla, I.A., Meirisdiana, M., Sunaryono, D., Husni, M.: Best ratio size of image in steganography using portable document format with evaluation rmse, psnr, and ssim. In: 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), pp. 289–294. IEEE (2021) 10. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

Discussion on the Application of Contributing Student Pedagogy in Vocational Education “Computer Network Technology” Fanjie Lv

and Jiande Sun(B)

School of Information Science and Engineering, Shandong Normal University, Jinan, China [email protected]

Abstract. The background of the new era has put forward higher requirements for vocational teachers engaged in the field of information technology. The promotion of educational modernization has brought new changes in the connotation of vocational education, and the improvement of teaching quality has brought new challenges. The contributing student pedagogy improves students’ learning initiative and gives full play to the subjectivity of students in teaching by changing the teaching mode of teachers’ teaching and students’ learning. To change the traditional teaching mode and method of the vocational education course and enhance students’ comprehension of computer network technology in the new period and teaching conditions, vocational schools can try to adopt the contributing student pedagogy to the vocational education course “Computer Network Technology”. The understanding and application of knowledge can improve the teaching quality of vocational education, and promote the development of students’ learning ability and practical ability, and cultivate students with all-round development. Keywords: Contributing student pedagogy · Vocational education · Computer network technology

1 Introduction “Enhancing the adaptability of vocational and technical education” is a clear requirement for vocational education [1]. The new era puts forward new requirements for the school development and professional training of vocational education. The teaching design should adapt to the development of the times and cultivate talents who are more suitable for social development and market demand [2]. How to cultivate skilled talents in the new era requires further changes in the design of teaching methods. Most of the traditional classroom teaching mode adopts the teaching method, that is, a rigid mode in which teachers teach students to listen, which is not conducive to stimulating students’ initiative and interest in learning. Computer major courses have dual meanings of theory and practice. The traditional classroom is inefficient, and students’ academic performance is not satisfactory, resulting in students gradually losing interest and motivation

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 349–354, 2023. https://doi.org/10.1007/978-981-19-9968-0_42

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to study courses. Therefore, it is necessary to change teaching methods, improve students’ enthusiasm for learning, and allow students to better study courses and improve their practical ability. Intelligent technology and education converged in the twenty-first century, and technology related to the development and use of intelligent education started to advance quickly [3]. At the same time, it also provides technical support for new teaching forms, and provides a more convenient teaching environment and teaching application for teachers’ teaching and students’ learning improvement. The contributing student pedagogy can also use the technology and equipment of the new era to carry out educational activities in vocational schools, mobilize students’ enthusiasm for learning, and further promote students’ growth and development.

2 Contributing Student Pedagogy The concept of contributing student pedagogy was first proposed by BettyCollis and JefMoonen, a university professor in the Netherlands. Contributing student pedagogy is a teaching method in which students co-create resources, share and respect other learners based on network systems [4]. In a joint study, John Hamer et al. believed that intrinsically contributing student pedagogy is a kind of teaching method that encourages students to learn and experience effectively in the sharing of classmates, and is a kind of teaching method that respects the behavior of other students [5]. In the classroom of contributing student pedagogy, students will be more active, and they can give full play to the subjective initiative of students, instead of the boring traditional teaching form where teachers talk and students listen. The advantage of contributing student pedagogy is that it is different from the traditional teaching form. The teaching approach is student-centered, allowing students to take full advantage of their initiative while learning and encouraging team members to work together. The teacher plays more of the role of participants and instructors [6]. In order to adapt to the development of vocational education, vocational schools can apply the contributing student pedagogy to the vocational education course “Computer Network Technology” to improve students’ learning ability and cultivate vocational education talents.

3 Current Situation of Vocational Education Teaching and Necessity of Shared Teaching Method 3.1 Vocational Education Reform and Transformation Vocational education strongly advocates that vocational education needs reform or improvement of teaching forms, and the improvement of teaching methods is conducive to the development of students. The focus of vocational education is to cultivate and develop technical talents, and even include some talents in urgently needed industries [7]. In today’s era of rapid development of information technology, the development of vocational and technical education must keep up with the trend of the times and the wave of technological reform in order to cultivate talents and technical personnel who are really in demand.

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3.2 The Advent of the Information Age and the Development of Smart Education The era of education informatization has arrived, and smart education has begun to enter the public’s field of vision. Policies and teaching reforms for vocational education have also followed. The intelligent new classrooms are equipped with a full-featured teaching system. The level of teaching hardware and software of the vocational education course “Computer Network Technology” has been improved, which provides a better teaching environment for students receiving vocational education and provides strong support for the implementation of teaching methods [8]. Under the new educational policy and educational environment, the teaching method reform of vocational schools also needs to make corresponding adjustments and changes with the trend of the times, so as to cultivate skilled talents that the society really needs. 3.3 The Necessity of Shared Teaching Method The existing vocational education course “Computer Network Technology” has the shortcomings of single and boring teaching methods, and the training direction and orientation of vocational education students are not clear and specific. The “Computer Network Technology” course is a compulsory course for computer application majors, and has the characteristics of combining theory and practice [9]. In teaching, we must pay attention to the explanation of computer network knowledge, and also take into account the investigation of computer network experiments. Vocational education courses need to learn knowledge from books, apply theory to practice, improve students’ practical ability and sense of contribution, and cultivate skilled talents in line with vocational education [10]. This is not only a test for students, but also an inspection of teachers’ teaching methods and teachers’ literacy. The application of the contributing student pedagogy requires the mastery of teachers and the cooperation of students. Only by correctly understanding the shared teaching method can reasonably use the teaching method to achieve learning results. If the concept of contributing teaching cannot be deeply understood, the cultivation of students’ subjective initiative and the method of learning by doing in the teaching process will be lacking, which will have a negative impact on the students of vocational education.

4 Cases of Teaching Design and Contributing Student Pedagogy in the Course “Computer Network Technology” 4.1 Case Introduction The case of this time is “The Composition of Computer Networks” in the “Computer Network Technology” course, the content of this section belongs to the content with more theoretical knowledge, and the practical part is not obvious. This instructional strategy aims to encourage students’ initiative in learning, advance their comprehension and use of computer network knowledge, and enable them to put what they have learned into practice outside of the classroom. Students learn new knowledge independently, identify problems and complete tasks designed for teaching and learning, and finally acquire knowledge about the composition of computer networks. Students complete the

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knowledge and further organize the learning content to form a corresponding report, mind map or PPT. 4.2 Teaching Objectives and Requirements Let students memorize the composition structure of a computer network, including system composition and two-layer subnet structure. Students can distinguish the hardware system of server, workstation, communication equipment and transmission medium as the hardware system of computer network. Students can remember the knowledge and content of network card, and distinguish interfaces of different network cards (see Fig. 1).

Fig. 1. Various interfaces of the network card

4.3 Learning Target Create learning objectives based on the content of this section as follows (see Table 1): 4.4 Task Design The specific task design is shown in the Table 2. By completing specific tasks, students can share the learning process and learning outcomes, mobilizing students’ learning initiative and the spirit of learning cooperation. 4.5 Evaluation Method Learning assessment should use a combination of teacher assessment, peer assessment and student self-assessment to ensure accuracy and diversity of assessment. Diversified shared assessment runs through the entire teaching process, including before, during and after class. Reasonable use of teaching evaluation can not only ensure the objective evaluation of the teaching process, but also adjust the teaching progress of teachers in time.

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Table 1. Learning objectives of “The Composition of Computer Networks” Knowledge and skills

Master and understand the basic concepts of computer network 1. Master the system composition of computer network, the composition of hardware system, software and software system 2. Familiar with the concept of two-tier subnet structure, and can distinguish 3. Understand file server, print server, application system server

Process and goals

Through the independent online access to materials or textbooks and the process of searching for resources, students can learn to use tools to complete the learning of new knowledge and summarize the methods of collecting materials, so that they can be more proficient in their use and operation in future learning

Emotional attitudes and values Cultivate students’ good study habits, improve students’ awareness of sharing, and cultivate students’ cooperative spirit and hands-on ability

Table 2. “The Composition of Computer Networks” task design Task presentation format

Mission name

The specific content and knowledge points of the task

PPT Report, class lecture and mind map

1. The hardware system of the computer network

Hardware systems: servers, workstations, communication equipment

2. Classification of hardware system servers

File server, print server, application system server, communication server

3. Hardware system communication equipment network card

The concept, function and classification of network card

4. Classification and concept of software system of computer network

1. Network operating system 2. Network application service system

5. Two-tier subnet structure of computer network

Communication subnet, resource subnet

6. Presentation and reporting of learning outcomes

Methods, types and requirements of reporting

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5 Conclusion and Future Outlook In the teaching of “Computer Network Technology”, the use of the contributing student pedagogy can greatly mobilize students’ enthusiasm for learning and initiative. With the teacher’s guidance, students can complete their learning tasks independently and develop good learning habits and a sense of cooperation. The contributing student pedagogy can promote the development of students in the development of practical problems, and the communication between students and teachers is obviously more than the traditional one. Student motivation is increased before, during and after class, and students begin to engage in inquiry-based learning. For teachers, the use of this pedagogy in vocational education courses has improved teachers’ research on teaching methods, deepened their understanding and use of teaching materials, adjusted to changes in teachers’ roles, developed students’ learning initiatives and habits, and promoted students’ overall development. To sum up, the vocational education course “Computer Network Technology” adopts the “contributing” model of contributing student pedagogy, which meets the training requirements of vocational school computer application technology professional information technology courses, which can break the traditional teaching model and promote students’ overall development. In the future, the design of teaching activities will be further improved, and the application of the contributing student pedagogy in vocational education courses will be continuously improved, so that teaching reform will benefit students and cultivate professional skilled talents that meet the needs of society.

References 1. Commentary Department of this newspaper: Accelerating the construction of a modern vocational education system. People’s Daily (2021) 2. Vocational Education Law of the People’s Republic of China. People’s Daily (2022) 3. Cai, B.: Artificial Intelligence Empowers Classroom Revolution: Essence and Concept. Educational Development Research (2019) 4. Collis, B., Moonen, J.: Flexible Learning in a Digital World: Experiences and Expectations. Kogan Page, London (2001) 5. Hamer, J., et al.: Tools for “contributing student learning.” ACM Inroads 2, 78–91 (2011) 6. Wang, Y., Deng, D.: The Shared Teaching Method in the Web2.0 Era: Connotation, Technology and Strategy, pp. 16–22. China Electronic Education (2013) 7. Xu, Y., Xue, K.: The world of work in the era of intelligent manufacturing: the demands of skilled talents and the reform of vocational education, pp. 18–23. China Vocational and Technical Education (2021) 8. Quan, G., Gu, X.: Observation on “Informatization” of Education for Smart Education, pp. 35– 44. China Electronic Education (2018) 9. Yan, X., Cao, Y., Zhang, L.: Problem-based teaching mode in computer network courses, pp. 79–81. Computer Education (2017) 10. Shi, W., Lin, Y.: The transformation of vocational education talent training mode in the new technology era, pp.34–40. China Electrochemical Education (2021)

Research on NURBS Interpolation Algorithm Based on Newton Iteration and Adams Equation Yunsen Wang(B) , Lin Li, Chengzhi Ma, SiPei Shao, and Yangchuang Cao Shandong Institude of Space Electronic Technology, Yantai 264670, Shandong, China [email protected]

Abstract. Through analyzing the principle of spline curve interpolation in aerospace CNC machining, the reasons that lead to feedrate fluctuation were pointed out. And a real-time interpolation algorithm based on Non-Uniform Rational B-Splines (NURBS) curve was proposed. And the detailed realization of this proposed algorithm has also been given. To avoid derivative calculation of conventional methods, Newton iteration algorithm is adopted to calculate interpolation values. And the Adams prediction equation was reduced down to simple linear recursive function, which is used to compute the iterative initial value of an interpolation parameter. Simulation results indicate that the proposed interpolation algorithms have advantages of low computational consumption, fine stability, and high process accuracy. The proposed method could answer the real-time interpolation requirements, and abate the feedrate fluctuation to an idea status. Keywords: NURBS curve · Feedrate fluctuation · Newton iteration method · Adams prediction equation

1 Introduction NURBS curves are one of the most widely used parametric curves in high precision machining or manipulator motion control of aerospace field for it can represent the analytical curves and free-form curves precisely and uniformly. Various NURBS interpolation algorithms had been proposed, such as Uniform interpolation [1], approximation interpolation [2], ordinary differential equation [3] and Newton iteration interpolation [4]. However, the convergence condition of these methods are very complex. Furthermore, to improve the interpolation speed and accuracy, numerous real-time forward-looking interpolation algorithms have been put forward [5–7]. Other algorithms focus on the path control to ensure the chord error [8, 9] or federate fluctuation [10]. Based on the methods above, this paper improved the algorithm’s real-time performance by using the linear recurrence equation to acquire the initial value of interpolation parameter. Through using Newton iteration function, the improved algorithm could have higher speed and accuracy. And simulation experiments are illustrated to test and verify the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 355–361, 2023. https://doi.org/10.1007/978-981-19-9968-0_43

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2 NURBS Function and Interpolation Theory 2.1 The NURBS Formulation The k-order NURBS curve could be delimited by Eq. (1): n 

C(u) =

wi Pi Ni,k (u)

i=0 n 

(1) wi Ni,k (u)

i=0

where {Pi } represents control points, u represents the interpolation parameter, {wi } represents corresponding weights, and U = {u0 , u1 ,…ui ,… un+k+1 } is the knot vector. Ni,k (u) are k-order B-spline basis functions. The recursive equations are as follows: ⎧  ⎪ ⎨ N (u) = 1 ui < x < ui+1 i,1 0 Otherwise (2) ⎪ ⎩ Ni,k (u) = u−ui Ni,k−1 (u) + ui+k+1 −u Ni+1,k−1 (u) ui+k −ui ui+k+1 −ui+1 Here define

0 0

= 0.

2.2 Parametric Curve Interpolation Theory Chord is frequently used to replace arc in a NURBS interpolator, as shown in Fig. 1. C(u) is the spline curve, and u represents the parameter. The current interpolation point Pi = C(ui ), the next one Pi+1 = C(ui+1 ). L i is the path increment at the ith sampling cycle. C(u) Pi

Pi+1 Li Pi+2

Fig. 1. Parametric curve interpolation

The interpolation process is mainly to calculate the next parameter. The next parameter ui+1 is derived by:  Li = Vds (ui ) · T = C(ui+1

) − C(ui ) = Pi Pi+1  (3) u Pi Pi+1  ≈ uji+1 C  (u) du Here T is the sampling time, Vds (ui ) is the desire speed at the point Pi. After the parameter ui+1 being obtained, the next point Pi+1 can be calculated by Eq. (1).

Research on NURBS Interpolation Algorithm Based on Newton Iteration

Commonly-used Taylor’s expansion method is defined by 

 2 (u ) dC(u) d 2 C(u)  Vds   i du du2 Vds (ui ) u=ui 2  ui+1 = ui +  T− T   4  dC(u)    dC(u)  du  2   du u=u

357

(4)

u=ui

i

And the feedrate fluctuation can be measured by feedrate fluctuation rate δi:   C(ui+1 ) − C(ui ) Vi δi = × 100% = 1 − × 100% Vds (ui ) Vds (ui )T

(5)

3 NURBS Interpolation Algorithm 3.1 Velocity-Adaptive Module Then the chord error E i can be derived by Fig. 1 and Eq. (6):     2  L V (ui )T 2 i 2 2 Ei = ρi − ρi − = ρi − ρi − 2 2

(6)

where ρi is the curvature radius and V (ui ) is the instantaneous velocity at the parameter ui . To bind chord error, the feed speed would be adjusted timely according to the curvature radius. The accuracy limited velocity V (ui ) could be described as: ⎧  ⎨ F, T ρ 2 − (ρi − ER)2 > F i 2 V (ui ) = (7) ⎩ T ρ 2 − (ρ − ER)2 , otherwise 2

i

i

where ER is the acceptable chord error, F is the programming speed. 3.2 Real-Time Interpolation To estimate interpolation parameters, three factors are necessary to implement Newton iteration: iterative formula, initial value of ui and termination condition. 3.2.1 N Iterative Formula Assuming ui is the current parameter, and ξ is the forward. The algorithm applies the procedure of approaching chord. From the definition of feedrate fluctuation rate in Eq. (5), we can create the constructor function as follow: F(ξ ) = C(ξ ) − C(ui ) − V (ui )T The problem converts to get the value of ξ ∗ that answer the follow equation:   F ξ∗ = 0

(8)

(9)

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The standard Newton iterative equation is given as: ξk+1 = ξk −

F(ξk ) Fξ k

The Cartesian coordinates of the derivative of F(ξ ) is  1 F  (ξ ) = C(ξ )−C(u (x(ξ ) − x(ui ))x (ξ ) + (y(ξ ) i )   −y(ui ))y (ξ ) + (z(ξ ) − z(ui ))z  (ξ )

(10)

(11)

Let y(ξ ) − y(ui ) z(ξ ) − z(ui ) x(ξ ) − x(ui ) − → , , ) Eξ = ( C(ξ ) − C(ui ) C(ξ ) − C(ui ) C(ξ ) − C(ui ) − →   Cξ = x (ξ ), y (ξ ), z  (ξ )

(12) (13)

Then the iterative function could be simplified as:

−1  ξ  ξk · C ξk+1 = ξk − E F(ξk ) k

(14)

C(u) is the regularization parameter curve, so the formula above can satisfy super linear convergence. 3.2.2 Termination Condition The stop condition of Newton iterative function is always determined by: |ξk+1 − ξk | ≤ ε

(15)

where the ε represents the upper limit of error. However, the value of ε is hard to estimate, and the speed precision could not be satisfied. In this paper, the terminated condition for the corrector is elected as:    Vk+1 − Vk    × 100% ≤ δ (16)  V  k+1 where δ is the upper limit of feedrate fluctuation rate. Furthermore, in order to reduce imputation calculation time, the maximum number of iterations K can be another termination condition together with δ. 3.2.3 Initial Estimation To further reduce the operation period, the linear recurrence equation is applied to acquire the initial value of ξ . The second-step Adams prediction equation is described by: ui+1 = ui +

 T   3ui − ui−1 2

(17)

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To reduce the complexity of the equation, the difference approximation is applied to evaluate the parameters, where ui ’ and ui-1 ’ are facilitated by: ui =

ui − ui−1  ui−1 − ui−2 , ui−1 = T T

(18)

Then ui+1 could be calculated by ui+1 = 2.5ui − 2ui−1 + 0.5ui−2

(19)

To guarantee ui+1 > ui , when the value of ui+1 calculated by formula (19) is less than ui , the value of ui+1 could be estimate by ui+1 = 2ui − ui−1 = ui + (ui − ui−1 )

(20)

4 Simulation and Experimental Verification A typical testing NURBS curve is selected to test the proposed method, and the curve has multiple sharp angles, which is shown in Fig. 2. The configuration parameters are given in the Table 1. The superior limit of velocity is supposed F = 100 mm/s. Let the sample time of CNC system T = 0.005 s and the tolerable chord error ER = 0.001 mm. The maximum number of iterations K = 10, and the upper bound of feedrate fluctuation rate  = 10−6 %. Figure 3 shows the velocity and chord error profiles. The feedrate in the interpolator based on velocity-adaptive module is within the maximum velocity value F. And the chord errors are also less than the tolerance ER, which is shown in Fig. 3. To verify the advantages of our proposed algorithm further, this paper makes a comparison between Taylor’s expansion method and proposed method on feedrate fluctuation. The results of contrastive experiments are shown in Fig. 4 and Table 2. From the Fig. 4(a), we can see that all interpolation points’ feedrate fluctuation rate of our proposed method is limited within the tolerance (10–6 %). Figure 4(b) shows the feedrate fluctuation profile of 2-order Taylor’s expansion algorithm, which is still relatively large. It was apparent from the Table 2 that the average feedrate fluctuation of Taylor’s expansion method rate can reach 1.62 × 10−2 %. In this study, the average feedrate fluctuation rate of proposed algorithm can reach the 10–8 % order of magnitude. The total interpolation step is 195, and the statistic number of iterations of the proposed algorithm is 234. That means after iterating only one time, nearly all the interpolation points’ feedrate fluctuation rate could reach 10–6 % order of magnitude. The simulation results explain the advantages of the proposed algorithm. Taylor’s expansion algorithm aims to approximate to the arc, while the proposed Newton iteration method aims to approximates to the chord. The proposed method is closer to the principle of interpolation, and can receive better feedrate control effect.

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(10,20), (8,8), (0,20), (10,0), (20,20), (12,8), (10,20)

Knot vectors

0, 0, 0, 0, 0.25, 0.500, 0.750, 1, 1, 1, 1

Weight vectors

1, 1, 1, 1, 1, 1, 1

Fig. 2. The test NURBS curve

(a)The velocity curve

(b)The chord error curve

Fig. 3. The velocity and chord error profiles

(a)The proposed method

(b) Taylor’s expansion method(2nd-order)

Fig. 4. The feedrate fluctuation profiles Table 2. Comparisons between Taylor’s expansion method and proposed method (%)

Taylor’s expansion method (2nd -order) The proposed method

Maximum

Negative maximum

Average (Absolute value)

10.34

−2.34

1.62E−02

−9.16E−07

2.36E−08

8.66E−07

5 Conclusion In this paper, an Adams linear recurrence combined with Newton iteration NURBS interpolator is proposed. And the proposed method could restrict the chord error and feedrate fluctuation to satisfy the requirements of machining accuracy, and get a better

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speed control effect. Simulations results illustrate that the proposed interpolation method could receive more superior machining accuracy and speed.

References 1. Bedi, S., Ali, I., Quan, N.: Advanced interpolation techniques for CNC machines. ASME J. Eng. Ind. 115(8), 329–336 (1993) 2. Shipitalni, M., Koren, Y., Lo, C.C.:Real-time curve interpolation. Comput. Aided Des. 26(11), 832–838 (1994) 3. Cheng, C.W., Tsai, M.C., Maciejowski, J.:Accurate feedrate control of CNC machine tools along NURBS curves. In: 43rd IEEE Conference on Decision and Control, Nassau, pp. 14–17. IEEE Press (2004) 4. Sun, H.Y., Fan, D.P.: Feedrate-control method for CNC real-time parametric curve path interpolation. China Mech. Eng. 19(17), 2032–3036 (2008) 5. Li, H., Wu, W.J., Rastegar, J., et al.: A real-time and look-ahead interpolation algorithm with axial jerk-smooth transition scheme for computer numerical control machining of micro-line segments. Proc. Instit. Mech. Eng. J. Eng. Manuf. 233(9), 2007–2019 (2019) 6. Luo, L., Gao, M., Huang, Z.L., et al.: B-spline curve look-ahead algorithm with flexible acceleration and deceleration method. Mach. Des. Manuf. 2017(9), 254–256, 260 (2017) 7. Hu, L.C., Shou, H.H., Shen, J.: An adaptive configuration method of knots and data parameters for NURBS curve interpolation. In: Proceedings of the 18th International Conference on Computational Science and Applications, Melbourne, pp.1–6. IEEE Press (2018) 8. Du, X., Huang, J., Zhu, L.M., et al.: An error-bounded B-spline curve approximation scheme using dominant points for CNC interpolation of micro-line toolpath. Robot. Comput.-Integr. Manuf. 64, 101930 (2020) 9. Ji, G.S., Yu, W.J., Chen, Z.P.: Interpolation for NURBS curve with corner on curve splitting according to its critical curvature. J. Mech. Eng. 54(19), 150–157 (2018) 10. Huang, H.M.: An adjustable look-ahead acceleration/deceleration hybrid interpolation technique with variable maximum federate. Int. J. Adv. Manuf. Technol. 95, 1521–1538 (2018)

Fake AP Detection Technology Based on Improved DBSCAN Algorithm Zhaoyuan Mei1(B) , Chenwei Wang2 , Lvxin Xu3 , and Songlin Sun1 1 School of Information and Communication Engineering, Beijing University of Posts and

Telecommunication, Beijing, China [email protected] 2 Google Inc., Mountain View, USA 3 Electronic Information Engineering of International College, Chongqing University of Posts and Telecommunications, Chongqing, China

Abstract. With the rapid development of the mobile Internet, mobile users’ access to the Internet through wireless networks has become one of the main access methods for obtaining information. However, the openness of wireless networks makes them vulnerable to fake AP attacks. Currently, detection research for such attacks is often based on whitelisting, however it is not always feasible to register device fingerprints in advance in practice. Aiming at such situation, this paper proposes a pseudo AP detection method based on DBSCAN algorithm. Using the nonlinear phase error feature extracted from the channel state information (CSI), the method can cluster the fingerprint data of different devices into different clusters, and then can judge the existence of the fake AP attack behavior. At the same time, this paper improves the algorithm and introduces the K-means nearest neighbor idea into the DBSCAN algorithm, so that the parameters can be adaptively selected according to the characteristics of the sample itself. The experimental results show that the method proposed in this paper can divide different wireless devices, and then can accurately detect the fake AP attack behavior in this scenario. Keywords: Wireless network · Fake AP · Channel state information · Nonlinear phase error · DBSCAN

1 Introduction As an effective mobile Internet access technology, Wireless Local Area Network (WLAN) has become an important part of modern computer platforms and embedded systems [1]. However, due to the open nature of wireless networks, it is vulnerable to various attacks. At present, the fake AP attack is one of the security problems in WLAN network all the time [2]. However, traditional wireless security protocols based on cryptography have been found to have great vulnerabilities [3]. Therefore, it is very important to find a way to efficiently detect potential attacks from malicious attackers. In recent years, fake AP detection technology based on WLAN physical layer has received extensive attention and research, and many excellent results have emerged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 362–370, 2023. https://doi.org/10.1007/978-981-19-9968-0_44

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Reference [4] proposed a detection method based on cross-power spectrum, with an accuracy rate of more than 90%. Reference [5] proposed a detection method based on deep learning, which uses short-time Fourier transform to extract fingerprint features, with an accuracy rate of over 94%. At the same time, some scholars have studied the method of detecting fake APs by extracting physical features caused by device hardware in CSI. Reference [6] proposed a pseudo AP detection method based on nonlinear fingerprint features of power amplifiers; Reference [7] used the carrier frequency offset estimation is used as a device fingerprint and achieves better performance in detecting illegal users. The methods proposed in the above documents often require pre-constructing a legal device fingerprint database, and by comparing the fingerprint of the device to be recognized with the fingerprint data in the database, it is determined whether the connected device is a fake AP. However, in real-world applications, it is not always feasible to build a legal device fingerprint database in advance. This paper calls this scenario an unfamiliar scenario, in which the above methods will not be applicable. In view of this situation, this paper proposes a fake AP detection method based on the improved DBSCAN algorithm, which not only realizes the fake AP detection in unfamiliar scenes, but also greatly reduces the cost.

2 CSI Phase Analysis CSI describes the combined effects of fading, scattering, and energy dissipation from the signal transmitter to the receiver [8], which contains amplitude and phase information. In wireless communication, due to the hardware of the transmitter and receiver and the influence of the transmission environment, the phase of the receiving end is inconsistent with that of the transmitting end. The phase measured by the receiving end can be expressed by the following formula: ∅i = θi − 2π Nki δ + β + z

(1)

where ∅i represents the phase of the receiving end, θi represents the original phase without interference, ki is the subcarrier number, N is the number of fast fourier transform shops, according to the IEEE 802.11 standard, N = 64, δ represents the time offset error, β represents the total phase offset error, and z represents the noise interference. At the same time, some scholars still found a nonlinear phase error from the CSI phase, and confirmed that this error is caused by the I/Q imbalance of the network card and the imperfect oscillator. After research, it is found that the error is consistent in different data packets of the same device, but different in different devices. Assuming that i . is used to represent the nonlinear phase error, it can be seen from the above analysis that Eq. (1) can be rewritten as: ∅i = θi − 2π Nki δ + β + i + z

(2)

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3 Fingerprint Feature Extraction 3.1 Preliminary Experiment and Analysis In the current research, some scholars mathematically model the nonlinear phase error [9], the model is as follows:   ·fk ·ki ·σ +εθ ) (3) i = arctan εα × sin(2π cos(2π ·fk ·ki ·σ ) where εα represents the gain mismatch of the data transmission channel, εθ represents the phase mismatch of the data transmission channel, σ represents an unknown time offset component, fk represents the frequency interval between the subcarriers of the CSI data, and ki is the subcarrier number, these unknown parameters can be obtained by fitting a large number of data packages. Accordingly, this paper uses the model for preliminary experimental analysis, fits these parameters through CSI data and the least squares method, and generates a nonlinear phase error fingerprint image, as shown in Fig. 1. However, in practice, if the mathematical model is used to detect a fake AP device, a large amount of data is often required for fitting to obtain unknown parameters, thus requiring more resources. Therefore, this paper utilizes a new method to extract nonlinear phase error features.

Fig. 1. Nonlinear phase error model fitting image

3.2 Phase Unwrapping The original CSI phase is solved using the arctangent function, and the calculated phase distribution is often between [−π, π]. In order to correctly analyze the relationship between the CSI data and each subcarrier, the phase needs to be processed. Therefore, in this paper, by adding or subtracting 2π to the phase of the latter subcarrier, the unwrapped phase of the latter subcarrier can be obtained, as shown in the following formula. 

∅latterphase = ∅latterphase ± 2π

(4)

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3.3 Phase Filter Due to the dynamic interference of the actual environment, there is an error in the measured CSI phase, as shown in Fig. 2. Therefore, in order to obtain accurate device fingerprint information, it is first necessary to eliminate these error effects. After analyzing the obtained data, it is found that there are some smoothly varying phases in some sampled CSI data. Therefore, this paper designs a phase filter to filter out the phase due to environmental interference. The specific ideas are as follows: (1) First, calculate the slope ki between all adjacent subcarriers in the CSI data, and put these slopes into a list, that is, ki = [k1 , k2 , · · · , k28 , k29 ]. (2) Second, calculate the mean square error value vi of all slopes ki in the list, and record it. (3) Finally, a threshold vmin is set through the calculated mean square error v_i. When vi < vmin , it indicates that the CSI phase in the data frame changes steadily, and the data packet can be saved. As shown in Fig. 3, after filtering by the filter, the influence of the environment is removed, and the phase fluctuation is small.

Fig. 2. CSI phase in real environment

Fig. 3. Phase filtered by the phase filter

3.4 Fingerprint Feature Extraction For the extraction of device fingerprint features, this paper starts from the formula principle and extracts nonlinear phase error features from CSI. According to the idea of linear transformation, the following formula is used for processing: a=

∅n −∅1 Kn −K1

=

θn −θ1 Kn −K1

−

2π N δ

(5)

After, this paper performs the following operations for the original CSI phase ∅i : 

∅i = ∅i − aki = θi −

θn −θ1 Kn −K1 ki

+ β + z + i

(6)

where the true phase θi can be expressed as the sum of the original phase θ and the component error ei , where the component error is different between subcarriers. Afterwards,

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θ , β and z are collectively classified as z ∗ , and ei and i are collectively classified as Ei , representing the nonlinear phase error caused by component error and I/Q imbalance, then Eq. (6) can be modified as: 

∗ 1 ∅i = − Kθnn −θ −K1 ki + z + Ei

(7)

Next, to remove the total phase offset error, sum the subcarrier-1 and subcarrier 1 phases in a single CSI packet: 



∅1 + ∅−1 = 2z ∗ + E1 + E−1

(8)

It can be seen from Fig. 1. that sub-carrier-1 and sub-carrier 1 can be considered as image sub-carriers, which means that E1 + E−1 is almost equal to zero, then: z∗ ≈





∅1 +∅−1 2

(9)

Accordingly, this paper subtracts z ∗ from each received CSI phase value to obtain the final expression of the device fingerprint: Ei = ∅i +

θn −θ1 Kn −K1 ki

= ∅i  + μki

(10)

At the same time, it can be seen from Fig. 1. that at the position closer to the center sub-carrier, the difference in nonlinear error value between adjacent sub-carriers is very small, which can be approximately equal to zero. Therefore, μ can be calculated as: 



μ ≈ ∅−2 − ∅−1

(11)

Due to the approximate solution method used in the mathematical analysis of nonlinear phase error, there are still some errors in the obtained eigenvalues. In order to better fit the fingerprint, E−28 and E28 are respectively equal to zero to calculate The μ1 and μ2 are respectively brought into the sub-carriers before and after to calculate the eigenvalues of different sub-carriers. At the same time, the smooth tool in Matlab is used to smooth the fingerprint feature data.

4 Algorithm Improvement The DBSCAN algorithm is an unsupervised clustering algorithm. The algorithm needs to pre-determine two important parameters, namely the neighborhood radius Eps and the density threshold MinPts. The traditional DBSCAN algorithm requires manual constant adjustment of parameters, which often consumes a lot of time and resources. Therefore, this paper improves the hyperparameter selection of the DBSCAN algorithm, so that it can automatically obtain the optimal parameters according to the characteristics of the sample distribution. The specific ideas are as follows: (1) Calculate the candidate Eps parameters. This paper uses the K-mean nearest neighbor idea to calculate the candidate Eps parameters. The specific method is as follows:

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Step 1: calculate the distance between all sample points in the sample data set M, and generate a matrix, that is, the following formula: Mn×n = {D(i, j)|1 ≤ i ≤ n, 1 ≤ j ≤ n}

(12)

where Mn×n represents the generated matrix, n represents the number of samples in the data set M, and D(i, j) represents the distance between sample point i and sample point j. Step 2: arrange the values of each row in the matrix calculated in step 1 in ascending order, and then all the distance elements in the Kth column can be combined into a distance vector DK . Step 3: average the elements in the K-nearest neighbor distance vector obtained in step 2, and use this value as the candidate Eps parameter of this paper, which can be specifically expressed as: Eps = {DK |1 ≤ K ≤ n}

(13)

Figure 4 shows the curve of the Eps parameter changing with the K value. It can be seen that the Eps increases steadily with the increase of the K value. (2) Calculate the candidate MinPts parameters For each candidate Eps parameter, the number of objects contained in each sample point in the Eps neighborhood is calculated in turn, and the mathematical expectation is calculated for all the results as the candidate MinPts parameter, which is the following formula: MinPts =

1 n

n 

Ni

(14)

i=1

where Ni represents the number of samples contained in the area with Eps as the radius of the ith sample point, and n represents the total number of samples in the dataset. Figure 5 shows the graph of the MinPts parameter changing with the K value. It can be seen that MinPts increases steadily with the increase of the K value.

Fig. 4. Eps and K value

Fig. 5. MinPts and K value

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(3) Adaptive selection of optimal parameters of DBSCAN algorithm In this paper, the corresponding values of the Eps parameter and the MinPts parameter are input into the algorithm for cluster analysis. When the number of clusters remains stable for the first time within a certain range and has no obvious change, this paper considers this time as the optimal number of clusters, and Record the maximum K value corresponding to the number of clusters. Finally, the Eps and MinPts corresponding to the recorded K value are selected as the optimal algorithm input parameters. This paper randomly selects four device fingerprint datasets for cluster analysis. Figure 6 is a graph showing the change of the number of clusters with the value of K. It can be seen from the figure that when K = 8, the number of clusters begins to remain stable, and ends when K = 98. Therefore, the corresponding Eps and MinPts under K = 98 are selected as the optimal parameters, specifically Eps = 0.093, MinPts = 107. This paper also draws a graph of the clustering accuracy changing with the value of K. As shown in Fig. 7, it can be seen that when K is 98, the clustering accuracy reaches the maximum value, and when K is greater than 98, the clustering accuracy drops sharply, which confirms the feasibility and effectiveness of the improved algorithm proposed in this paper.

Fig. 6. Number of clusters and K value

Fig. 7. Clustering accuracy and K value

5 Simulation Experiment and Analysis 5.1 Experimental Setup In this paper, a system is built in a real environment to conduct simulation experiments. The experimental area is a laboratory environment with a stack of 7 m × 4 m and many items. This paper firstly uses the Intel5300 network card and CSI Tool to collect CSI data from ten wireless devices, and extracts 1000 fingerprints from each device as a data set for false AP device detection. The nonlinear phase error fingerprint feature of each device contains the information of 30 sub-carriers. However, there is a certain correlation between most of the sub-carriers. If the information of all sub-carriers is used as characteristic data, certain redundancy is often generated. Therefore, this paper uses the principal component analysis method to reduce the dimension of the feature data, and takes the contribution rate

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of the feature as the selection standard, and obtains the three principal components while retaining 90% of the contribution rate. In order to verify the feasibility of the method proposed in this paper, it is necessary to use the features extracted in this paper to effectively distinguish different devices, that is, by collecting CSI data of different devices and extracting nonlinear phase error fingerprint features, and then using the DBSCAN algorithm for cluster analysis, and finally Different clusters can be formed effectively, so even if an illegal device modifies its own identifier to be the same as that of a legitimate device, different clusters will often be formed due to different fingerprint characteristics, which can effectively determine whether there is a fake AP attack in the environment. 5.2 Simulation Analysis In this paper, two devices are randomly selected as legal devices among ten wireless devices, and the fingerprint data of these two devices are selected to be input into the DBSCAN model. At the same time, one device is randomly selected as the attack device among the remaining devices, and the fingerprint data of this device is input into the DBSCAN model. In the above operations, the former simulates a scenario in which there is no fake AP attack behavior. At this time, the output of the DBSCAN model should be two clusters, and the identifiers corresponding to each cluster are inconsistent; the latter simulates the existence of fake AP attack behavior. At this time, the output of the DBSCAN model should be multiple clusters. At this time, although the attack device modifies the device identifier to be consistent with the legal device, the existence of the attack behavior can still be effectively discovered through the division of different clusters.

Fig. 8. Only legal devices

Fig. 9. Attack device exists

The DBSCAN clustering results are shown in the figure above, and Fig. 8 shows the clustering results when there are only legal devices. It can be seen from the figure that two clusters are formed after the DBSCAN model clustering; when there are attacking devices, the DBSCAN clustering results New clusters start to appear and gradually form new clusters, as shown in Fig. 9 Although the attacking device modifies its own identifier to be the same as that of the legitimate device, according to the device fingerprint information and the bound identifier information in the cluster diagram, the existence of

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the fake AP attack behavior can be effectively detected. The feasibility and effectiveness of the method proposed in this paper are verified.

6 Conclusion In practical applications, considering that the fingerprint database cannot be constructed in advance in unfamiliar scenarios, this paper proposes a pseudo AP detection method based on the improved DBSCAN algorithm. Using the nonlinear phase error fingerprint feature extracted from CSI, the method can cluster the fingerprint data of different devices (indicated by device identifiers) into different clusters, and then can judge the existence of fake AP attack behavior. Through the experimental analysis, it can be seen that the method proposed in this paper can distinguish different wireless devices, and then accurately detect the fake AP attack behavior in an unfamiliar environment. After that, we will solve the problem of WiFi fingerprinting in more complex and AP scenarios. At the same time, we will continue to study WiFi signals and strive to find more features to improve the identification effect.

References 1. Huang, J., Liu, B., Liu, P., et al.: Towards anti-interference wifi-based activity recognition system using interference-independent phase component. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 576–585. IEEE (2020) 2. Zhang, Z., Hasegawa, H., Yamaguchi, Y., et al.: Rogue wireless AP detection using delay fluctuation in backbone network. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 1, pp. 936–937. IEEE (2019) 3. Vanhoef, M.: Key reinstallation attacks breaking WPA2 by forcing nonce reuse. KU Leuven August (2017) 4. Fang, D., Hu, A., Shi, J.: An all-data-segment radio frequency fingerprint extraction method based on cross-power spectrum. In: 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 384–388. IEEE (2022) 5. Huang, D., Al-Hourani, A., Sithamparanathan, K., et al.: Deep learning methods for device authentication using RF fingerprinting. In: 2021 15th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–7. IEEE (2021) 6. Lin, Y., Gao, Y., Li, B., et al.: Accurate and robust rogue access point detection with client-agnostic wireless fingerprinting. In: 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10. IEEE (2020) 7. Hua, J., Sun, H., Shen, Z., et al.: Accurate and efficient wireless device fingerprinting using channel state information. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 1700–1708. IEEE (2018) 8. Dai, H., Shi, W., Zhou, Z., et al.: Authentication method for wifi connection of devices based on channel state information In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 37–43. IEEE (2020)

Vehicles Location Estimation and Prediction in Integrated Sensing and Communications: Sensing Vehicles as Extended Targets Guanyu Zhang(B) , Zhilei Ling, and Songlin Sun Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected]

Abstract. In the implementation of vehicular networks, obtaining the location information of the vehicles is a very important step. This allows effective control of the beam emission direction, resulting in more accurate, efficient and economical communication. In this paper, we consider the integrated sensing and communication (ISAC) technology in downlink communication from roadside unit (RSU) to vehicles. Since the time delay and Doppler parameters are needed in this process, we choose orthogonal time frequency space (OTFS) modulation to assist ISAC communication. This allows the physical parameters to be extracted directly through the delayed Doppler domain channel. Unlike other vehicles sensing schemes, we do not model the vehicle as a point target, but as an extended target with multiple scatterers. After obtaining the vehicle sensing parameters, the RSU can formulate the narrow beam according to the predicted parameters for effective communication. Finally, we show the estimated accuracy and performance gain compared with the baseline method that treats the vehicle as a point-like target. Keywords: Integrated sensing and communications · Downlink communication · Location estimation · Orthogonal time frequency space · Vehicular networks

1 Introduction In recent years, with the significant development of automotive industry and mobile networks, vehicular communication is envisioned as a key technology in the future, which will pave the way for traffic management, autonomous driving, and intelligent transportation systems, etc. [1, 2]. In this case, the estimation of location of vehicle is essential for both vehicle-to-infrastructure (V2I) communications and real-time collisionprevention in autonomous driving [3, 4]. In such scenarios, sensing and communications are demanded simultaneously, even sharing the same frequency spectrum and hardware architecture. Since ISAC can combine communication system and sensing system to efficiently use wireless spectrum resources, it is considered as one of the key technologies for future wireless networks. In the ISAC scenario, how to improve the energy efficiency is a very important issue. [5] proposes a new network architecture that is based on a deep learning © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 371–379, 2023. https://doi.org/10.1007/978-981-19-9968-0_45

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environmental resource prediction module and a self-organizing service management module. [6, 7] present an energy-efficient waveform design in ISAC systems. It can maximize the communication energy efficiency while ensuring the radar accuracy. But, in terms of sensing, they are not considered in enough detail in locating the target. However, the implementation of OTFS technology can help to tackle the issue of locating targets. As one of the promising techniques to facilitate ISAC, OTFS has drawn significant attention in recent years. Compared with the orthogonal frequency division multiplexing (OFDM) techniques, OTFS modulation applied in the delay Doppler (DD) domain instead of the time frequency (TF) domain, providing a strong delay-resilience and Doppler-resilience [8]. As the physics of propagation can be directly utilized by the DD domain, OTFS is naturally suitable for sensing the parameters of physical world, providing a new vision for achieving ISAC. The combination of OTFS and ISAC was proposed and investigated in [9], which shows that OTFS can achieve accurate radar estimation as well as the data detection. [10] proposed a hybrid digital-analog beamforming method to perform detection and parameters estimation with OTFS modulation, where a maximum likelihood scheme is adopted. The above works demonstrated the promising ability of transmitting communication signals and simultaneously sensing parameters with OTFS modulation. In addition, when applying OTFS in vehicle networks, RSU can predict the state of to-becommunicated vehicle, based on the estimated parameters, thus assisting beamforming in high-mobility vehicular communication networks. To this end, a novel ISAC-assisted OTFS transmission scheme was proposed in [11], where the downlink beamformers at the RSU side were formulated according to the predicted parameters, thus saving communication overhead of performing channel estimation. On top of that, a ISAC transmission framework was proposed in [12], where the mismatch of the reflection strengths between the radar sensing and communication was considered. The strategies of beam tracking, angle of arrival (AoA) estimation algorithms, and power allocation for radar sensing were also investigated. Nevertheless, the above works treated the to-be-communicated vehicle as a point-like target, where the sensing parameters each vehicle is estimated by a single scatter. However, in practical vehicle links, the vehicles are more likely to reflect multiple scatterers since the vehicle tend to act as an extended-target in both angle and range domains. It has been proved in [13] that the precise position of communication receiver (CR) should be estimated for extended-targets for dual-functional radar-communication (DFRC) signals in vehicle communication networks. To address this issue, we propose to sense the vehicle as an extended target to improve the accuracy of vehicle location estimation. We obtain the sensing parameters of multiple scatterers to estimate the exact location of the CR. Then, the OTFS-ISAC signal is used to predict the motion state. Thus, the beam emission direction is controlled to better complete the communication between RSU and vehicle. To verify the effectiveness of our proposed method, we also compare the performance of the point-like target sensing and the extended-target sensing. Numerical results show that there is a significant performance gain in sensing the vehicle as an extended-target compared to the baseline method that models the vehicle as a point-like target.

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2 System Model As illustrated in Fig. 1, we consider a scenario where an RSU is equipped with a uniform linear array (ULA) with Nt transmit antennas and a ULA with Nr receive antennas, serving the to-be-communicated vehicle equipped with a single antenna. The vehicle is moving along the road which is parallel to the ULA. For vehicle sensing, we model the vehicle as an extended-target which reflects I scatterers to the RSU. Meanwhile, we set the road as the x-axis and assume that the vehicle moves along the x-axis in the positive or negative directions.

Fig. 1. A diagram of the considered vehicular network.

Considering the vehicle as an extended-target, the RSU receives the echoes contributed by the vehicle’s I scatterers. For the i-th scatter, as discussed above, we denote the data symbol at time-slot t as ci (t). Then, the transmitted signal can be formulated using a beamforming vector as c˜ i (t) = pi ci (t),

(1)

where pi ∈ CNt ×1 is the transmit beamforming vector, which is determined by the estimated value of the angle-of-departure (AoD) θˆi , given by   (2) pi = a θˆi , where θi is the angle between the location of the vehicle relative to RSU and the y-axis. Then, the transmitted OTFS-ISAC signals will be reflected by I scatterers on the vehicle. The received echoes ei (t) can be expressed as √ ei (t) = ς pi αi b(θi )aH (θi )˜ci (t − εi )ej2π μi t + w(t), (3) where ς , pi and αi denote array gain factor, the allocated transmit power to the i-th scatterer and the reflection coefficient, respectively. a(θi ) and b(θi ) denotes the transmit

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steering vectors and the receive steering vectors of the RSU, respectively. And εi and μi denote the round-trip time-delay and the Doppler frequency for the i-th scatterer of the vehicle. w(t) ∈ CNr ×1 represents the complex additive white Gaussian noise with zero mean and variance σ12 . In terms of downlink communication model, we refer to the OTFS communication model in article [11]. Thus, in this scenario, the OTFS-ISAC signals are used for vehicle tracking and communication. The signal received at the vehicle can be expressed as.   (4) y(t) = h(t)a θˆCR c(t) + z(t), where z(t) denotes the complex additive white Gaussian noise with zero mean and variance σ22 .θˆCR represents the estimated angle of CR. h(t) is the downlink channel model, which can be expressed as [14]  c h(t) = ej2π μt aH (θCR ), (5) 4π fc d 2 θCR and d represent the angle and the distance between the RSU and the CR of the vehicle, respectively. fc , c and μˆ denote carrier frequency, the speed of light and the Doppler shift, respectively.

3 Parameter Estimation with OTFS Modulation In this section, we present a scheme for predictive transmit beamforming design for downlink transmission by estimating and predicting vehicle locations using OTFS-ISAC signals, where the vehicle is regarded as an extended-target. Firstly, we describe the general process of sensing-assisted predictive beam tracking. Then, we present the details of parameter sensing and prediction. Finally, sensing-assisted downlink communication is discussed. 3.1 General Process of Sensing-Assisted Predictive Beam Tracking As an extension of the baseline to target sensing in article [11]. Our sensing-assisted communication scheme for extended-targets can be generally divided into following steps: Division of the Vehicle as Extended-Target: In order to make the estimated performance more accurate, we divide a vehicle into I scatterers, uniformly distributed in all parts of the vehicle. OTFS signals are transmitted to these I scatterers and I echoes are received at the receiver side of the RSU for analysis. Parameter Estimation at the Current Moment: After I echoes are received at the RSU side, the estimates of time delays and Dopplers of the i-th scatterer are firstly obtained by matched filtering. Then the estimation of the angle can be obtained by maximum likelihood estimation (MLE) at the time κ. With the estimated values of the basic parameters εˆ i , μˆ i and θˆi . We can obtain the estimated values of the location and velocity of each scatterer at time κ.

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Parameter Prediction at the Next Moment: The estimates of sensing parameters at time κ can help RSU to predict parameters such as vehicle location, speed, angle and distance at the next time slot, i.e., at time κ + 1. Thus, a dynamic topology of vehicle information prediction has been constructed. Identification of Communication Center (CR) and Downlink Communication: In the above process, the RSU obtains the estimation of the sensory parameter information for each scatterer, which is uniformly distributed in the vehicle. These values can help the RSU to determine the CR of the vehicle. RSU takes weighted mean values of the angle and distance parameters of each uniformly distributed scatterer. Then, the estimated value of the CR location can be obtained. In this way, the RSU can control the transmitting beamformer and transmit the OTFS-ISAC signals to the specified CR direction.

3.2 Sensing Parameters of the CR After obtaining the estimated values of location, angle and other parameters of each scatterer according to the above process. The RSU can calculate a more accurate CR location of the vehicle to transmit the beam. The CR angle and distance parameters are determined using the method of taking the weighted average, given by. I 1  · ρi θˆi , I

(6)

1  ˆ ρi di , dˆ CR = · I

(7)

I 1  · ρi vˆ i , I

(8)

θˆCR =

i=1 I

i=1

vˆ CR =

i=1

where d denotes the distance between the vehicle and RSU, v denotes the speed of the vehicle, and ρi denotes the weighting factor. Next, the RSU can obtain location estimates from angle and distance parameters, given by. uˆ x,CR = dˆ CR sin θˆCR ,

(9)

uˆ y,CR = dˆ CR cos θˆCR .

(10)

3.3 Downlink Communication After obtaining the estimated parameters of the vehicle CR, the predicted channel gain and Doppler shift can be predicted by the RSU as h=

c 4π fc dˆ CR

,

(11)

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μ=

  v i cos θˆCR fc c

.

(12)

By adopting the formula as in [11], the received SNR can be represented as follows SNR =

√ 2      pNt aH (θCR )a θˆCR x[k,l]

= pNt E

σ22   2   H a (θCR )a θˆCR  σ22

(13) ,

where E and p denote the data symbol power and the allocated transmit power, respectively. It can be observed that when the estimated angle θˆCR is equal to the true angle θCR , the SNR achieves its maximum.

4 Simulations In this section, we present numerical results to verify the effectiveness of the proposed method. We set carrier frequency for the RSU at 30 GHz, then we consider 140 time instants and use T = 0.02 s as the duration for each instant. The number of transmitting and receiving antennas is set to Nt = Nr = 64. In simulations, we model the vehicle as a cuboid of 1.8 × 5 mand then divide it into 6 parts uniformly, i.e. I = 6, assuming that each part has a scatterer at its centroid. The locations of each scatterer are given in Table 1. Beyond that, we assume that the coordinate of the RSU is (0 m, 0 m), the initial location of the vehicle is (13 m, 20 m) and the velocity of the vehicle is 10 m/s, moving in the direction of the positive x-axis. 4.1 Simulation of Angle Estimation To show the prediction accuracy, we compare the real angle and the estimated angle of CR relative to the RSU and the y-axis, using the extended-target method and the baseline point-like target method in Fig. 2 (a). By magnifying the local details of the fitting curve, we can observe that the estimated angle of CR using the extended-target method is closer to the real situation, which only has a slight jittery near the curve of the real angle, verifying the effectiveness of our method of estimating the angle of CR. Further, we calculate the root mean square error (RMSE) in Fig. 2 (b) using the extended-target method and the baseline point-like target method respectively. From its local enlarged figure, we can clearly observe that the RMSE of the estimated angle using the extended-target method is below 0.15 rad on average, lower than 0.3 rad of the baseline point-like target method, which shows the accuracy of estimating angle using the extended-target method more significantly. 4.2 Simulation of Location Estimation Since we assume that the vehicle is moving along the x-axis before, we only consider the error of location at x-axis. In Fig. 3 (a), we show the estimated location of extendedtarget method and the baseline point-like target method. In the diagram, the black line

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Fig. 2. (a) The estimated angle of CR using extended-target method and point-like target method respectively. (b) RMSE of the angle using the extended-target method and the point-like target method respectively.

represents the real location of the vehicle CR. The red and blue lines represent the extended-target and baseline point-like target methods, respectively. It can be seen that our proposed extended-target method estimation can obtain a more accurate vehicle location of CR. Next, the RMSE of the estimated location of extended-target method and the baseline point-like target method is shown in Fig. 3 (b), which verifies the effectiveness of our proposed method for estimating locations as well.

(a)

(b)

Fig. 3. (a) The estimated location of CR using extended-target method and point-like target method respectively. (b) RMSE of the location using the extended-target method and the point-like target method respectively.

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4.3 Comparison of Communication Performance In Fig. 4, we depict SNR of the extended-target method and baseline point-like target method obtained by Eq. (13). It is obvious to see that SNR of the extended-target method is higher and more stable than SNR of the point-like target method. That is because that by taking the vehicle as an extended-target, the angle and location of CR can be estimated more accurately, so as to ensure the beam alignment of radar with greater probability to obtain higher signal power. Due to the great impact of beam alignment on the SNR, better tracking of the angle and location of CR will further achieve a more stable SNR curve. It is worth mentioning that the signal does not decay when the vehicle approaches the RSU on account of the same reason.

Fig. 4. SNR of the extended-target method and point-like target method.

5 Conclusion In this paper, we propose a new scheme for sensing vehicle locations in vehicle networks. Innovative use of the vehicle to be sensed as an extended-target, reflecting multiple echoes to the RSU. This effectively improves the accuracy of vehicle CR estimation by the RSU to control the beam direction for more effective communication transmission. Simulation results demonstrated a higher accuracy for estimating the angle and distance of the vehicle, also revealing significant gain on communication performance.

References 1. Siegel, J.E., Erb, D.C., Sarma, S.E.: A survey of the connected vehicle landscape—architectures, enabling technologies, applications, and development areas. IEEE Trans. Intell. Transp. Syst. 19(8), 2391–2406 (2017) 2. Dimitrakopoulos, G., Demestichas, P.: Intelligent transportation systems. IEEE Veh. Technol. Mag. 5(1), 77–84 (2010)

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3. Hult, R., Campos, G.R., Steinmetz, E., et al.: Coordination of cooperative autonomous vehicles: toward safer and more efficient road transportation. IEEE Sign. Process. Mag. 33(6), 74–84 (2016) 4. Lu, N., Cheng, N., Zhang, N., et al.: Connected vehicles: solutions and challenges. IEEE Internet Things J. 1(4), 289–299 (2014) 5. Zou, J., Mei, K., Sun, S.: multi-scale video inverse tone mapping with deformable alignment. In: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9–12. IEEE (2020) 6. Zou, J., Cui, Y., Liu, Y., et al.: energy efficiency optimization for integrated sensing and communications systems. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 216–221. IEEE (2022) 7. Zou, J., Cui, Y., Liu, Y., et al.: Optimal energy-efficient beamforming for integrated sensing and communications systems. In: 2022 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE (2022) 8. Wei, Z., Yuan, W., Li, S., et al.: Orthogonal time-frequency space modulation: a promising next-generation waveform. IEEE Wirel. Commun. 28(4), 136–144 (2021) 9. Gaudio, L., Kobayashi, M., Caire, G., et al.: On the effectiveness of OTFS for joint radar parameter estimation and communication. IEEE Trans. Wirel. Commun. 19(9), 5951–5965 (2020) 10. Gaudio, L., Kobayashi, M., Caire, G., et al.: Hybrid digital-analog beamforming and MIMO radar with OTFS modulation. arXiv preprint arXiv:2009.08785 (2020) 11. Yuan, W., Wei, Z., Li, S., et al.: Integrated sensing and communication-assisted orthogonal time frequency space transmission for vehicular networks. IEEE J. Sel. Top. Sign. Process. 15(6), 1515–1528 (2021) 12. Li, S., Yuan, W., Liu, C., et al.: A novel ISAC transmission framework based on spatiallyspread orthogonal time frequency space modulation. IEEE J. Sel. Areas Commun. 40(6), 1854–1872 (2022) 13. Du, Z., Liu, F., Yuan, W., et al.: Integrated sensing and communications for V2I networks: dynamic predictive beamforming for extended vehicle targets. arXiv preprint arXiv:2111. 10152 (2021) 14. Ngo, H.Q.: Massive MIMO: Fundamentals and System Designs. Linköping University Electronic Press (2015)

Research on Vehicle and Cargo Matching of Electric Materials Based on Weed Optimization Algorithm Xiao Yu(B) , Dedi Huang, Yanlong Gao, Jun Zhu, and Bohan Ren Material Supply Branch, Beijing Electric Power Corporation, Beijing 100053, China [email protected]

Abstract. In recent years, due to the development of e-commerce and the social economy, the demand for logistics and transportation has become more and more huge. For freight platforms, under the impact of big data, how to reasonably allocate the matching relationship between vehicles and goods is an urgent problem to be solved. Therefore, this paper proposes a vehicle-cargo matching model based on the weed optimization algorithm, which quantitatively models the common vehicle-cargo matching schemes, and integrates various factors to establish an objective function for calculation. A heuristic algorithm weed optimization algorithm is introduced to match the vehicle and cargo in the model. Experiments show that this method can significantly improve the efficiency of vehicle and cargo matching, reduce the generation of local optimum, and can be adapted to solve the actual vehicle and cargo matching problem. Keywords: Vehicle and cargo matching · Heuristic algorithm · Weed optimization algorithm · Logistics and transportation

1 Introduction With the development of the whole country’s social economy and the rise of electronic commerce and logistics commerce, there are great obstacles in the logistics and transportation of electric power companies. The low utilization rate of freight logistics resources, coupled with the difficulty of optimizing and reducing its cost, has caused a very bad impact on its development and innovation [1]. In the matching of a large number of goods and vehicles in different locations, a lot of time and resources are wasted, which greatly increases the cost. Obviously, in the matching scheme under the big data framework, the exhaustive matching method is not feasible. Therefore, the intelligent algorithm is the main solution to the vehicle-cargo matching problem. In 2017, Liu Danxia proposed an innovative vehicle-cargo matching model [2] and established a VRP model with a time window for simultaneous pickup and delivery. Combined with the tabu search algorithm, the calculation speed is increased, and the convergence speed is fast, which has significant advantages over similar algorithms. In 2020, a freight logistics platform vehicle-cargo © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 380–388, 2023. https://doi.org/10.1007/978-981-19-9968-0_46

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matching optimization method based on an improved ant colony algorithm was proposed by the author [3]. The algorithm uses a parameter adaptive ant colony algorithm, combined with bacterial foraging algorithm chemotaxis and k-means The combined clustering technology enables the algorithm to maintain a good diversity while searching for optimization quickly. Most of the current solutions for matching power materials, vehicles, and goods are based on traditional path search, which is time-consuming, low in efficiency, easy to fall into local optimum, and wastes financial and material resources [4]. Based on this, this paper proposes a solution based on the intelligent algorithm of weed optimization, which can greatly improve the efficiency of matching power materials, vehicles, and goods, and reduce the probability of local optimization. The structure of this paper is as follows. The first part introduces the current research status of the vehicle-cargo matching problem, the second part introduces the research method and process of this paper, the third part introduces the experimental results of this research, and the fourth part introduces the model and algorithm of this paper. The discussion is carried out, and the article gives the conclusion in the fifth section.

2 Vehicle and Cargo Matching Mathematical Model This section will introduce the mathematical model of vehicle and cargo matching, from two aspects: the vehicle and cargo matching mechanism and the weed optimization algorithm. 2.1 Vehicle and Cargo Matching Mechanism Vehicle and cargo matching is a common life problem. It is often seen when the logistics platform arranges cargo transportation and vehicle scheduling. The specific manifestation is that within a period of time, the platform will receive multiple cargo transportation applications and the empty vehicle identification of the truck. The platform needs to make a reasonable match of vehicles and goods in the shortest possible time to improve transportation efficiency and avoid waste of resources. As shown in Fig. 1, after the logistics system of the electric power company receives the matching application between the vehicle and the goods, it starts to calculate the matching scheme of the vehicle and the goods. Assuming that there are m vehicles to be matched, the transportation capacity of each vehicle is ci , there are n goods to be matched, and the transportation demand of each good is dj . First calculate the distance between the vehicle and the cargo, denoted as D, and denote the distance between the cargo and the destination as L. When scheduling vehicles and matching goods, the logistics system of the power company will pay attention to the matching rate of vehicles and goods. The best case is that each batch of goods can be transported by vehicles that meet the requirements. The higher the matching rate, the better. The matching rate MR is: n  m 

MR =

xij

j=1 i=1

min(I , J )

(1)

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The vehicle-cargo matching rate MR in the calculation scheme

Cargo empty rate UR in the calculation scheme

Cargo loading rate LR in the calculation scheme

In the calculation plan, the matching income P of vehicle and cargo

Objective function Z

Fig. 1. Vehicle and cargo matching mechanism

Under the condition that the matching rate is satisfactory enough, the logistics system of the power company hopes to reduce the empty driving rate of each vehicle. That is to say, it is hoped that the ratio of the distance of each vehicle to pick up the goods to the total distance is as small as possible, so as to improve the transportation efficiency of each vehicle and reduce the empty driving time. The empty driving rate UR is: n  m 

UR =

Dij xij /Dij + Lj

j=1 i=1 n  m 

(2) xij

j=1 i=1

Similarly, when the matching rate is satisfactory enough, the logistics system of the power company hopes to increase the loading rate of each vehicle as much as possible. Let the goods matched by each vehicle approach the upper limit of the vehicle as much as possible and reduce the waste of space. The reload rate LR is: n  m 

LR =

dij xij /cij j=1 i=1 n  m  xij j=1 i=1

(3)

Combining the empty driving rate UR and the loading rate LR, the logistics system of the power company hopes that the empty driving rate is as low as possible and the loading rate is as high as possible. It sets a matching income P to integrate the empty driving rate and the loading rate. The lower the vehicle empty rate and the higher the load rate, the higher the total matching benefit. λ1 and λ2 are the preferences of the decision-makers of the power company’s freight platform for the two indicators of empty driving rate and loading rate. Limits λ1 λ2 ∈ [0, 1] and λ1 + λ2 = 1. The matching income P is: P = λ1 (1 − UR) + λ2 LR

(4)

Finally, the vehicle-cargo matching problem is abstracted as an optimization problem. The logistics system of the power company sets the objective function Z, which includes

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the matching rate MR and the matching income P. Among them, w1 and w2 are the preferences of the decision-makers of the power company’s freight platform for the two indicators of empty driving rate and loading rate, and restrictions w1 w2 ∈ [0, 1] w1 + w2 = 1. max Z = w1 MR + w2 P

(5)

2.2 Application of Weed Optimization Algorithm in Vehicle and Cargo Matching In the logistics and transportation problems of power companies, the problems often encountered are that the materials are complicated, the distances are long, and the destinations are not concentrated [5]. This makes the traditional path problem solution to the problem of long iteration time, difficult to avoid local optimization and weak universality when solving the logistics and transportation problems of power companies. This seriously affects the efficiency of the power company’s logistics transportation vehicle and cargo scheduling matching [6]. Therefore, this paper adopts a weed optimization intelligent algorithm to solve such problems, learns from the law of natural weed growth, quickly and efficiently screens out suitable transportation schemes, and greatly reduces the number of times falling into local optimum. As shown in Fig. 2, after the logistics system of the power company detects that there is a matching scheme between vehicle and cargo demand, it first randomly generates multiple sets of schemes, the number of which is less than the set upper limit of the number of schemes, initializes the scheme library and enters the iteration. Each round of iteration needs to reconstruct the scheme in each library, and the number of reconstructed schemes is arranged from large to small by the objective function in vehicle and cargo matching, as follows: s = Ffloor (

f (xi ) − Fmin (smax − smin ) + smin ) Fmax − Fmin

(6)

Among them, Fmax and Fmin are the maximum and minimum excellence degree values in the reconstruction of this generation, smax and smin are the maximum number of program offspring and the minimum number of program offspring that can be generated, and f (xi ) the excellence degree value of the i-th program individual, The Ffloor function means round down [7]. The reconstructed scheme structure is randomly reconstructed using a normal distribution with its parent position as the mean, and its standard deviation is determined by the current iteration number, which is: δ = δfinal + ((gmax − g)/gmax )w (δintial − δfinal )

(7)

Among them, g is the reconstruction algebra, δintial and δfinal are the initial and final standard deviation, and w is the nonlinear adjustment factor. When the number of reconstruction schemes plus the number of schemes in the scheme library is greater than the upper limit of the number of schemes, all schemes will be competitively reconstructed, and the vehicle-cargo matching objective function

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will be used to sort and select the schemes, and the selected optimal scheme group will be imported into the scheme library [8]. When the maximum number of iterations is reached, the final solution is output, that is, the optimal vehicle-cargo matching solution is obtained. The specific implementation steps of the algorithm are as follows: 1. After obtaining the transportation demand and application from the vehicle and cargo pulling system, firstly check whether there are any spare vehicles and excess cargo supporting the vehicle and cargo. If so, continue the matching operation. Move to the waiting queue. 2. According to the corresponding relationship between the vehicle and the goods, the distance is queried and a distance matrix is established. At the same time, the distance of the goods beyond the vehicle’s capacity is set to zero. 3. Randomly match vehicles and goods, randomly generate multiple schemes for iteration according to the capacity set by the seed bank, and calculate the quantitative results of each random scheme in combination with the vehicle and goods matching objective function. 4. According to the excellent value of the seed bank during the operation, the size of the seed bank and the method of the selected reconstruction scheme, calculate the matching value of the vehicle, generate a new vehicle-cargo matching scheme, and solve the quantitative result of the new scheme at the same time. 5. If the quantification result of the generated scheme is better than the quantification result of the worst scheme in the seed bank, put the generated scheme into the seed bank, delete the original worst scheme, and update the seed bank. 6. Iteratively generate the vehicle-cargo matching scheme many times, and repeat the fourth and fifth steps to update the seed database until the optimal solution for the out-vehicle-cargo matching is solved. End this match. 7. Record the optimal solution in the database, including information such as the distance between vehicles and cargo, the maximum and average value of excellence, and the number of iterations, clear the seed bank, check the waiting queue, and wait for the next transportation demand and application.

Shipping needs and applicaƟons

Build a distance matrix

YES

Are there any spare vehicles and excess cargo?

NO

Return to the waiƟng signal to move the vehicle or cargo into the waiƟng queue

Randomly generate matching scheme seeds

No

Maximum number of iteraƟons reached

Yes

output opƟmal soluƟon

No

Determine and generate a normal random seed according to the number of iteraƟons

Reach maximum populaƟon size

Yes

The Law of CompeƟƟve Survival, SorƟng Choices

Fig. 2. Weed optimization algorithm

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After the vehicle and cargo matching as shown in Fig. 2, the vehicle and the cargo are matched one-to-one. The vehicle-cargo matching system processes the warehousing requests in the queue in turn according to the request sequence. Optimal matching of vehicles and goods is made based on the attributes of each vehicle and goods. After the matching is completed, the time consumption caused by the decision is predicted and calculated, and the relevant allocation results are recorded (Fig. 3).

3 Experimental Results and Discussion

Fig. 3. Schematic diagram of the case

In a matching case of a power company in Beijing, there are 25 vehicles that match the demand for goods, and their locations, loads, and demands are known. The algorithm needs to iterate the plan so that each vehicle receives the goods and delivers them to their respective destinations while trying to approximate the optimal plan. The picture below shows the location of the vehicle and the cargo in the case. The red dot is the vehicle and the white dot is the cargo. Let the connection parameters of the model of the vehicle-cargo matching problem in the power logistics system be 1 and 2; the maximum number of schemes is 50, the initial number of schemes is 10, the maximum number of schemes is 15, the minimum number of schemes is 0, and the variance reduction index is 3. The initial standard deviation is 10, the final standard deviation is 2, and the number of iterations is 10000. In order to avoid the contingency of the results, this paper conducts ten simulations on the case and selects the optimal objective function with the most occurrences as the matching result. The optimal function, path, and each sub-objective function are shown in Table 1.

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MR

UR

LR

P

Objective function

0.83

1.0

0.30

0.62

0.66

Matching scheme

[13, 24, 10, 3, 5, 19, 4, 11, 17, 20, 2, 16, 14, 7, 0, 15, 22, 18, 8, 6, 21, 12, 1, 23, 9]

function value

In this case, we simulated the model ten times. Of the ten times, six times the results showed the same optimal result, and four times reached the suboptimal state, and the objective function was slightly lower than the optimal value. Some simulation results are shown in the figure below.

1.2 1 0.8 0.6 0.4 0.2 0

Z MR UR LR 0

20000

40000

60000

P

number of iterations Fig. 4. Case simulation 1

From Fig. 4, it can be found that the five function indicators all tend to a fixed value after a certain number of iterations, all can converge, and all converge to the same position. Obviously, the weed optimization algorithm is suitable as a heuristic algorithm in this case. It adopts a normal distribution-based reproduction mechanism, has a high-level search depth, and can ensure the overall search breadth of the algorithm (using a global search method) [9]. At the same time, the iterative reproduction mechanism based on self-adaptation is used. According to the natural reproduction law, the number of seeds reproduced by each individual weed is different. In addition, the introduction of the competitive exclusion mechanism for the children and the parents allows all weeds to reproduce freely before exclusion, competition after reaching the upper limit can retain useful information to the greatest extent, avoiding slow iteration and falling into a local optimal state. For the suboptimal state, it can be found that the algorithm is seriously affected by the initial parameter setting, which brings problems to the optimization. Compared with other algorithms, the search breadth of the algorithm is greatly improved, but the depth is obviously not as good as PSO and other algorithms because it only adopts a normal distribution and is affected by the number of iterations, and does not consider the

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correlation between multiple variables, and there is no information exchange between individuals, it is easy to fall into a local optimum. The next improvement direction will be to optimize the local optimum, mainly for the optimization and improvement of the seed discretization method.

4 Conclusion In this paper, a vehicle-cargo matching model based on a weed optimization algorithm is established, and the optimization function of the model is quantitatively calculated considering the matching rate, empty driving rate, and loading rate of the scheme. At the same time, combined with the heuristic algorithm weed optimization algorithm, through breeding competition and iterative adjustment of the decision-making strategy, the objective function is used to change the preference for specific needs, and the vehicle and cargo matching strategy is adjusted accordingly. The model requires less pre-knowledge and has the advantage of iterative calculation of intelligent algorithms. After adding the idea of pictographic state change and function approximation, it has a significant effect on solving high-level conditional input problems. It can significantly reduce the time and space consumption when calculating the actual logistics vehicle and cargo matching problem, and obtain a vehicle and cargo matching result that is close to the optimal solution in a short time. Fund Project. Supported by the Science and Technology Project of Beijing Electric Power Company: 5G intelligent logistics park technology research.

References 1. Mütze, A., Lucht, T., Nyhuis, P.: Logistics-oriented production configuration using the example of MRO service providers. IEEE Access 10, 20328–20344 (2022) 2. Liu, D.: Research on internet-based vehicle and cargo matching mode and route optimization for intra-city delivery, Master, Southwest Jiaotong University (2017). https://kns.cnki.net/KCMS/ detail/detail.aspx?dbcode=CMFD&dbname=CMFD201702&filename=1017142181.nh&v= 3. Fu, Y.: Optimization Method of Freight Logistics Platform Vehicle and Freight Matching Based on Improved Ant Colony Algorithm, Master, Hefei University of Technology (2020). https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMF D202101&filename=1020419776.nh&v= 4. Sun, Y., Zhang, J., Tang, Q., Yan, Y.: Research on benefit-risk of vehicle-cargo matching platform based on matching degree. In: 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), Beijing, China, pp. 580–584, September 2020 5. Laila, Z., Ouafae, Z.O.: A proposed scale for measuring logistics performance: case of the Moroccan retail sector. In: Income 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), pp. 1–5, December 2020 6. Ling, H., Fu, Y., Hua, M., Lu, A.: An adaptive parameter controlled ant colony optimization approach for peer-to-peer vehicle and cargo matching. IEEE Access 9, 15764–15777 (2021) 7. Liu, Y., Jiao, Y., Zhang, Y., Wang, X.: Invasion weed optimization algorithm for array antenna pattern synthesis. J. Xidian Univ. 41(01), 29–33+86 (2014)

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8. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006) 9. Li, X.-L., Wang, J., Yang, X.: Invasive weed optimization algorithm based on differential evolution operators to solve bin packing problem. In: 2020 Chinese Control And Decision Conference (CCDC), pp. 4141–4145, August 2020

Improved MSFLA-Based Scheduling Method of Electric Power Emergency Materials Tiezheng Wang(B) , Xiao Yu, Ming An, and Liang Shi Material Supply Branch, Beijing Electric Power Corporation, Beijing 100053, China [email protected]

Abstract. Logistics and distribution is a hot topic of organization nowadays. Among them, by arranging a vehicle scheduling plan for the power material storage system, and studying how to timely and effectively complete the power generation equipment supply under the power company’s emergency plan, the process cost can be saved. This paper abstracts the scenario into a vehicle scheduling problem with soft time windows, and uses a heuristic Modified Shuffled Frog Leaping Algorithm to search for a feasible optimal solution to the problem. The algorithm absorbs the advantages of Particle Swarm Optimization Algorithm and Memetic Algorithm, and the two are combined to complementary advantages. It has the characteristics of simple implementation, less control parameters and high performance, and has good solution results in the actual emergency delivery background. Keywords: Power supplies · Vehicle scheduling · Time window · Shuffled frog leaping algorithm

The material supply process is often cumbersome and complicated. In the process of material supply chain management of power enterprises, the process is complex and the management links are numerous [1]. The supply of power materials needs to be optimized and solved urgently from procurement issues, personnel issues, and transportation process issues. With efficient execution within nodes and matching and cooperation between processes, power companies can reduce work costs and increase company operating costs. After considering many other heuristic algorithms, this paper proposes a vehicle scheduling problem with soft time windows based on coding hybrid leapfrog algorithm to optimize the vehicle scheduling problem in the current logistics distribution process.

1 Mathematical Model of Vehicle Scheduling Problem For the transportation of power supply equipment by power companies, the optimal distribution vehicles mainly includes the optimization of collection routes, distribution routes, and the integration of goods assembly. Among them, the scheduling problem, is a very critical part. Reasonable arrangement of the sequence can effectively save the manpower and material resources of distribution, further reduce the logistics cost. Before this, many informatization talents proposed solutions to the problem. Wu Lie © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 389–398, 2023. https://doi.org/10.1007/978-981-19-9968-0_47

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[2] analyzed the dispatching system of emergency repair vehicles on the basis of multiobjective optimization; Zhou Mingzhi et al. [3] applied mobile positioning technology to intelligently analyze the distribution plan and algorithm process of emergency supplies for electric power; Wang Bogen et al. [4] combined with the related process of genetic algorithm, innovatively solve the specific itinerary problem. 1.1 Classical Vehicle Scheduling Problem The Vehicle Scheduling Problem (VRP) refers to the reasonable arrangement of the delivery order of goods, so that all target customers required by the task list can obtain the delivered electric goods, and at the same time can meet the scheduling conditions, such as the limited number of delivery vehicles, the limited amount of goods carried by the vehicles, minimal time cost, etc. The vehicle scheduling problem was first proposed by Dantzig and Ramser in 1959 8. The classic vehicle scheduling problem usually has the following operational requirements: 1. The power warehouse, as the starting point, is fixed and unique during the mission. 2. Each delivery truck pre-loads all the goods at the dispatch point according to the dispatch order, and sets off, passing through the target customers in turn. 3. The number of vehicles is limited by the actual requirements. When all the available vehicles are dispatched and still cannot complete all the dispatch goals, the dispatching fails, and it is considered that the method needs to be replaced. 4. The maximum load of the vehicle is limited by the actual requirements, and each available truck is allowed to have a different maximum load. 5. The journey consumption of the vehicle starting from the warehouse and returning to the starting point after completing all delivery targets is calculated in the cost. According to the background and requirements of the above problem, this paper uses the following mathematical language to describe: ⎧ eij xijt ≤ Qt , 1 ≤ t ≤ V ⎪ ⎪ ⎪ ⎪ i j ⎪  ⎪ ⎪ ⎪ xijt = N ⎨  t i j  . dij xijt p, s.t. minf = ⎪ x0j = 1, xi0 = 1 i j t ⎪ ⎪ ⎪ j i ⎪ ⎪  ⎪ ⎪ d ≤ D uyt t, 1 ≤ t ≤ V ⎩ u

y

Among them, dij represents the distance from i to j; xijt is a logical variable, indicating whether the vehicle is delivered from i to j by vehicle t; p represents the parameter factor, witch is used to control the proportion of path consumption; eij represents the transportation volume from i to j; Qt represents the maximum load of the t-th vehicle; N represents the total number of tasks; V represents the total number of vehicles; Dt represents the maximum distance traveled by the t-th vehicle.

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1.2 Vehicle Scheduling Problem with Time Window Iactual work, the delivery of power goods does not blindly consider the distance consumption, but requires a strong timeliness in the delivery of power materials. Therefore, in order to solve the above-mentioned practical power application scenarios, this paper changes the classic model in 1.1 into: Vehicle Path Planning Problem with Time Window (VRPTW). Under this variant, each target customer has three additional demands: the earliest allowable arrival time, the latest required completion time, and the execution time. The Vehicle Scheduling Problem with Time Window (VRPTW) was described and represented by Desrochers in the paper 9; at the same time, David Li and others in China proposed to use heuristic algorithm to solve VRPTW earlier1011. Under VRPTW, the scheduling process also has the following requirements: 1. to 5. Same as Sect. 1.1. 6. When the transport vehicle arrives at the destination, the arrival time is recorded and compared with the time window required by the target customer: if the arrival time is earlier than the earliest time requested by the customer, the transportation plan will be subject to “waiting penalty”; if it arrives and executes the task If the completion time exceeds the latest completion time, there will be a “delay penalty”. According to the background and requirements of the above problem, this paper uses the following mathematical language to update the description of VRPTW:     dij xijt p + Tt − sj ∗ yijt q + ej − Tt + gj ∗ zijt r minf = i

j

t

⎧ ⎪ ⎨ omitted , same as 1.2 s.t. tijt = min{e i , t∗it } + dij ⎪ ⎩ t  = max tijt , si  + gi ijt

In the formula, Tt represents the current time of the t-th vehicle; sj represents the earliest arrival time required by task j; ej represents the latest completion time required by task j; oj represents the processing time of task j; q and r stand for weight.

2 Design of MSFLA 2.1 Algorithm Types and Selection The traditional exact solution method can theoretically obtain the only optimal solution to the problem, and the result is supported by the correctness of the mathematical formula derivation. However, since the distribution of power materials is derived from the classic vehicle scheduling problem, it is a classic NP-hard problem, which makes the traditional exact solution unfeasible in practice. At this time, random search and optimization iterations are used. The heuristic bionic algorithm has become a powerful tool to solve the problem.

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2.2 The Principle of Shuffled Frog Leaping Algorithm The shuffled frog leaping algorithm (SFLA) is a bionic heuristic algorithm based on the change of frog population distribution on rocks when foraging. This algorithm considers the relationship among a single individual of the foraging frog, a rock centered foraging population, and the overall foraging population. Through mutual transformation and influence among the three, plus random jumping, the algorithm can solve the problem of local optimal solution and complete the solution process. This algorithm was first proposed by Eusuff et al. in 200612. The algorithm assumes that a group of frogs live in a pond, and there are many scattered rocks in the pond. The frogs jump between the rocks to find the most favorable location with the most food. Each frog individual is not only a unit with independent ideas, but also an individual with the ability to communicate with peers so as to have overall consciousness. The frog in the pond changes its position by jumping. The direction of jumping takes into account the food situation of its own position, the overall food level of other frogs on the same stone, and the position of the frog with the best food quality in the whole pond. This decision-making method can guide the movement trend of the frog, At the same time, it also improves the overall search ability of the population for food. 2.3 Mathematical Model of SFLA In fact, the shuffled frog leaping algorithm imitates the communication mechanism within the population, and promotes the convergence of the optimal solution through information sharing between individuals and populations. According to the above introduction, this paper divides SFLA into four parts: Population Initialization, Subgroup Division, Local Search, and Shuffling Operation (Fig. 1).

Start

Initialize the population, and randomly assign each frog in the population to a reasonable position in the feasible solution space

the update termination conditions are met

Calculate the objective function value of frog position in the whole population

Normalize the frog position to prevent jumping out of the feasible solution

Arrange frogs in ascending/ descending order according to their objective function values, and number them

Each frog updates its position according to the algorithm strategy

Fig. 1. Flow chart of SFLA

Population Initialization Population Initialization mainly refers to the determination of parameters, including the number of frogs n, the number of stones in the pond m, and the total number of iterations of the population Iter and so on. Subgroup Division The division of subgroups means that frogs with certain characteristics are divided into the same population by certain standards, that is, they are placed on the same stone.

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The specific operation is to sort all the frogs according to their positions, namely:

   (x1 , x2 , x3 . . . xn ):= xa , xb , xc . . . After that, the frogs will be allocated to the corresponding subgroups according to the above operations. It can be easily concluded that the frog individuals assigned to the same subgroup are actually frogs at the same model position after sorting. If the subgroup is G, then: Gi = (xi , xi + m , xi + 2m . . .) According to the above description, the number of subgroups divided is the number of initialized stones m. For the divided results, it is necessary to record the individuals with the best position in all frogs (global optimal) and the individuals with the best position in each population (local optimal). Considering that the current {xi } sequence is ordered from the best to the worst, it is easy to get the global optimal position xbest and the local optimal position xigbest as follows: xbest = x1 xigbest = xi , i ≤ m Local Search Local search is the evolutionary and iterative process of the population in the SFLA. The local search process needs to be performed on the frog with the worst position in each population. According to the principle of frog jumping algorithm, the basic steps of frog jumping process can be obtained: first, jump the currently selected frog xi to the position of frog xigbest at the best position on the current stone (subgroup), and then come to position xA . At this time, calculate the size relationship between f (xA ) and f (xi ). If the target value of the new position xA exceeds the original position xi , the frog’s jump is ended; Otherwise, frog xi continues to jump towards frog xbest at the global optimal position, arrives at position xB , and also calculates the target value f (xB ) of xB position. If a better target value can be obtained, the jump of the frog ends; Otherwise, frog xi jumps randomly in the solution space to position xC . . Follow the above procedure to update the current position of the frog and the target value at the same time until all the target frogs are updated. This process is shown in the following figure (Fig. 2):

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Update Strategy The frog jumps randomly in the direction of the frog Fb with the best position in the population

Select the worst frog F w in each population

new position is bett er to the original position

Frogs jump randomly in the direction of Fb with the best position of the whole population

Y

N

Y

new position is better to the original position

N The new position is the updated value Fw 'of the frog Fw

jumps randomly in the whole solution space

Fig. 2. Local search algorithm in SFLA

The move function is defined as that the frog xi at a certain position moves to the other frog xj in the population and finally stops at xk , which is expressed as follows:    xk = move xi , xj := xit + rand (0, 1) ∗ xit − xjt t

Then the local search operation is:

xA = move xi , xigbest , xB = move(xi , xbest ), xC = move(xi , xrand ) Therefore:

   xi = max xj s.t. f xj As the judgment of multiple interlocking is involved, the actual algorithm design can also simplify the process to the last one, that is, just take the best of A, B and C for updating. Shuffling Operation After the operations described above, the positions of some frogs have changed. Therefore, it is necessary to put the frogs divided into subgroups back into the pool. At the same time, it is necessary to recalculate the target value of the frogs whose positions have changed to facilitate the next iteration. In addition, it is noted that decimals may occur in the calculation process of (3), but the number of target customers is often an integer in practical application problems.

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Therefore, it is necessary to standardize the location change results so that they can be located in the feasible solution space, namely: 



xi = int(xi ) or xi = round (xi )

2.4 Application of MSFLA in Power Warehouse Cargo Transportation For the above SFLA, if it is to be applied to the freight transportation in the power warehouse, it is necessary to consider that the result of freight transportation is a series of transportation sequences. When and only when the transportation sequence is a feasible solution, the sequence can be used for the iterative calculation of the SFLA. Therefore, it is necessary to establish the corresponding situation between the transportation sequence and the feasible solution space. A reasonable transportation sequence Sr , defined as follows: 1. Traverse the transportation scheme sequence, and set the current sequence number as 1 2. Check the allocation of each vehicle from the perspective of vehicle, and: set the current vehicle as vehicle 1. If the current vehicle’s capacity plus the number of goods required by the current serial number is less than or equal to the current vehicle’s load, the goods will be allocated to the current vehicle; Otherwise, the current vehicle number plus 1 shall be used to re execute this item; 3. Current serial number plus 1 4. If the current serial number is less than N of the total number of target customers, execute again 2.; Otherwise, continue 5.; 5. Decode the transportation scheme. The decoding work is carried out according to the load requirements and quantity requirements of the specified vehicles, that is:   Sr = g1 , g2 , g3 . . . gN To decode it, one of the possible solutions is: vehicle 10 → ga → gb → 0 vehicle 20 → gc → gd . . . At the same time of decoding, it is also to judge whether the distribution scheme meets the requirements under hard conditions, that is, load goods for each vehicle according to the distribution order; When all goods can be allocated to a certain vehicle, count whether the total number of vehicles used is not greater than the number of vehicles required by the task. If met, it will be deemed to meet the hard requirements; otherwise, it will be deemed to completely fail to meet the task requirements. Next, the steps of applying SFLA to the optimization solution under the condition of material transportation in the power warehouse are given:

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1.

Initialize the scheduling problem and determine various needs of target customers, including N, x, d , s, e, o, V , v, r, p, q, as above. 2. Initialize the algorithm and determine the parameters used in the algorithm process, including n, m, f , x, Iter, N , cost, etc. The definitions are the same as above. 3. Using sequence detection method to detect the output, and ensure that the transportation sequence is completely feasible 4. Calculate the target value cost for n frogs, and sort by cost. 5. The sorting results are divided into G subgroups. 6. The worst frog individual xworst in each subgroup will try to jump to the local optimal position, the overall optimal position and the random position respectively, calculate the target value of the new position, and select the corresponding position xbest of the optimal target value as the frog position 7. The frog individuals with changed positions are normalized, and the sequence detection method is used to ensure that the new frog is still in the solution space. 8. for i = 1 to iter: Repeat the above four steps 9. Select the globally optimal individual x1 as the output of the MSFLA. 10. Parse the output x1 into an n-dimensional sequence Sr . Then, analyze Sr into specific vehicle paths V1 , V2 , V3 … And send the paths to specific vehicles.

3 Experimental Results and Analysis

Fig. 3. Path arranged by Beijing PMC for 7 demand points reported at a certain time

During the festival and before and after the New Year, the power supply staff will carry out on-site status monitoring on the power supply facilities in the hot areas, and comprehensively master the equipment operation status. Once an emergency occurs, the power supply staff will record the wrong location of electricity use, and formulate the time for transporting goods and summary of the time consumption according to the priority of the electricity use emergency. Then make a maintenance catalog and report it to the electric storage standby warehouse (Fig. 3). The following is the material transportation situation and objectives reported by the maintenance staff to the reserve warehouse in an emergency (Table 1):

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Table 1. Emergency demand points reported by maintenance staff at a certain time Repair point no

Demand

1

67

2

124

3

36

…

…

Earliest time

Latest time

Repair time

0:00

12:00

1h

8:00

8:30

15 min

10:00

12:00

1.5 h

…

…

…

In view of this material objective, this paper abstracts the process into a vehicle scheduling problem with time windows, selects appropriate reward and punishment parameters, formulates the objective function, and obtains the following operation result data through the MSFLA proposed in this paper (Fig. 4):

Fig. 4. Record of MSFLA with a small amount of data (left) and large amount of data (right).

It can be seen from the figure that the algorithm has completed convergence after 100–200 times. Finally, when three material vehicles are available, the lowest target consumption can be obtained. In addition, from the picture, it can be found that by adjusting the parameters, the function converges quickly in the case of medium parameters. This phenomenon can be considered that the appropriate allocation of the number of frogs and stones can make the convergence speed moderate each time, and at the same time, the pace of each random action will be rationalized, so as to avoid the population being updated too randomly, At the same time, it can make the frogs with poor position in most populations move to the frogs with good position, so that the population will develop well as a whole, making the algorithm easy to find the optimal solution.

4 Conclusions In this paper, a vehicle scheduling algorithm with time window based on MSFLA is proposed. This algorithm completes the calculation of the optimal goal in the practical application background, obtains the global optimal result, and can be resolved into specific vehicle routing arrangements. In the application process of the algorithm, the research found that this method has the characteristics of simple concept, few control

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parameters, small amount of calculation and strong global optimization ability. The advantages of the algorithm combine the genetic based memetic algorithm and particle swarm optimization algorithm to simulate social behavior, which has good research value. Fund Project. Supported by the Science and Technology Project of Beijing Electric Power Company: 5G intelligent logistics park technology research.

References 1. Xie, H.: Integration of supply chain management based on power enterprise material supply. SME Manag. Sci. Technol. (8), 3–4 (2021) 2. Lie, W.: Analysis of electric power emergency repair vehicle dispatching system based on multi-objective optimization. Digit. Des. (II) 9(11), 83–84 (2020) 3. Zhou, M., Shao, H., Cao, L., et al: Power emergency material dispatching system based on mobile positioning technology. Electr. Autom. (2) (2017). https://doi.org/10.3969/j.issn. 1000-3886.2017.02.026 4. Wang, B., Wang, X., Zhang, Z.: Application of genetic algorithm in power maintenance personnel scheduling problem. Mod. Comput. (Prof. Ed.) (8) (2015). https://doi.org/10.3969/ j.issn.1007-1423.2015.12.001 5. Zhou, J.: Research on vehicle optimal scheduling based on hybrid genetic algorithm. J. Hebei North Univ. (Nat. Sci. Ed.) (6) (2009). https://doi.org/10.3969/j.issn.1673-1492.2009.06.012 6. He, J., Xu, W., Zeng, Z.: A two-stage heuristic algorithm for solving two-stage cumulative vehicle routing problem. Mechatronics (4) (2014). https://doi.org/10.3969/j.issn.1007-080x. 2014.04.015 7. Wu, B., Song, Y., Cheng, J., et al.: Research on VRPTW problem based on density peak clustering. Ind. Eng. (5) (2020). https://doi.org/10.3969/j.issn.1007-7375.2020.05.008 8. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959) 9. Desrochers, M., et al.: A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. 40(2), 342–354 (1992) 10. Li, D., Wang, L., Wang, M.: A heuristic algorithm for vehicle routing problem with time window constraints. Syst. Eng. (04), 20–24+29 (1998) 11. Leng, D., Zhang, J., Li, D.: Application of genetic algorithm to vehicle routing problem with time windows. J. Anshan Iron Steel Inst. (03), 2–5 (1999) 12. Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38, 129–154 (2006)

A Survey of Class Activation Mapping for the Interpretability of Convolution Neural Networks Mingwei He1(B) , Bohan Li2 , and Songlin Sun1 1 Beijing University of Posts and Telecommunications, Beijing 100876, China

[email protected] 2 School of Electronics and Computer Science, University of Southampton,

Southampton SO17 1BJ, UK

Abstract. Recent advances in deep learning have brought a new era for many fields, including object identification, image classification, and image compression. These tasks mostly build models based on the convolutional neural network (CNN). The trained models provide “input-output” end-to-end solutions with excellent automatic feature extraction performance. On the other hand, due to the distributed feature coding and the growing complexity of the model structure, users cannot comprehend the model’s internal knowledge representation and the potential reasons for a particular decision. However, applying CNN models in high-risk areas requires a thorough understanding of the reason behind their decisions in order to earn users’ trust. Therefore, CNN’s interpretation capabilities have gradually gained attention. This survey reviews the literature on class activation mapping (CAM), a straightforward technique for interpreting deep neural networks. We summarize the traditional CAM method and related methods based on the traditional model and then discuss new frontiers. This review can provide researchers in the field of CAM with guidance and, hopefully, shed light on future research regarding the interpretability of CNNs. Keywords: Class activation mapping · Convolutional neural networks · Interpretability

1 Introduction Over the past few years, deep neural networks (DNNs) have evolved as a representation of deep learning techniques [1]. The network structure of DNNs and the optimization algorithms [3, 4] have become more complex as training data [2] and computational power have increased. The performance of DNNs on various tasks has vastly improved, resulting in a variety of classical deep network architectures suited to different types of data processing tasks. In image data processing and recognition, CNN is an overall network structure that has achieved excellent results in image classification, target detection, semantic segmentation, and other tasks. This fundamental model has become the most widely used [5]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 399–407, 2023. https://doi.org/10.1007/978-981-19-9968-0_48

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Fig. 1. Comparison of the learning process [6].

As shown in Fig. 1, traditional machine learning algorithms employ manually designed feature sets. Since the designers comprehend the precise significance of the stated features, classic machine learning methods are somewhat interpretable, and the algorithm’s reliance on various features and decision-making basis is generally understood. Deep learning algorithms represented by CNNs are feature learning algorithms. The need for manual feature design disappears when a large number of nonlinear network layers are combined in an advanced design to extract features from raw data at different levels of abstraction. This advantage enables them to learn richer and more exclusive features with a large amount of deep semantic information, outperforming most traditional machine learning algorithms.

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There are limitations to this advantage of CNNs. On the one hand, the internal knowledge representation and precise semantic meaning of CNNs are not well understood. Even model designers struggle to explain what features are learned by CNNs, the specific organization of these features, and the importance of different features, resulting in empirical or even blind iterations for the optimization of CNN models. This not only affects model performance but also leaves potential vulnerabilities. On the other hand, real-world applications based on CNN models have been widely deployed in everyday life, such as face recognition, pedestrian detection, and scene segmentation. However, interpretability and transparency issues pose a significant barrier to their expansion and depth for some industries with a low-risk tolerance, such as the medical, financial, transportation, and military fields. There is a high demand for deep learning models such as CNNs in these fields, but they cannot be used at scale due to security and interpretability concerns. Models may make common-sense errors in practice and are unable to provide reasons for the errors, making it difficult to rely on their judgments. To improve the interpretability and transparency of deep learning models, establish a trust-based relationship between users and decision models, and eliminate potential threats in real-world deployments, a series of approaches to deep learning model interpretability have been extensively researched and proposed by academia and industry in recent years. Among these methods, CAM is an intuitive CNN interpretation method with various advantages, such as low computational effort and strong visual effect. Therefore, this paper focuses on the review of CAM and related methods (Fig. 2).

Fig. 2. The chronology and relationship of CAM methods.

We review five CAM techniques. Except for the basic CAM method, each is an optimization of the preceding CAM methods. Grad-CAM, in particular, eliminates practically all issues of CAM. The final three solutions are optimized based on the specific weaknesses of Grad-CAM.

2 Class Activation Mapping (CAM) Paper [7] introduces the concept of class activation maps. The purpose of this method is to localize targets without using any bounding box. The CAM method replaces the fully

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connected layer in the CNN with a global average pooling layer (GAP) by assimilating the ideas of paper [8]. In the final convolutional layer, it collects the mean value of each feature map, which is then weighted and summed to produce the actual output. In addition, this method forces the final convolutional layer to produce feature maps with the same number of target classes so that classification results can be obtained after the GAP and Softmax layers, and each feature map of the GAP output can be given a meaningful value. When interpreting the model output, the GAP output can be directly visualized, i.e., a heat map is the visual representation of the weighted sum of the corresponding feature maps so that the features that significantly impact the classification results can be identified. The following is the computation for the localization map:    (c) (c) wk Ak (x, y) (1) LCAM (x, y) = ReLU k

where Ak (x, y) is the activation of node k in the target layer of the model at position (c) (x, y), and wk is the weight corresponding to class c for unit k in the fully connected layer. 2.1 Pros and Cons of CAM Since the CAM method has no fully connected layer, it does not require a specific input size, and GAP can use the spatial information more. Moreover, the reduction of parameters increases the model’s robustness, making it less prone to overfitting. However, the CAM method necessitates revising the model’s framework and then retraining the new model to achieve the interpretation of the black box model. Consequently, this method increases the cost of model training and severely limits the model’s application scenarios. In addition, the CAM method only interprets the outputs of the final convolutional layer and not the intermediate layers, leaving many uninterpretable parts in the model.

3 Grad-CAM The problems presented by the shortcomings of the CAM method in Sect. 2.1 are resolved by the advent of Grad-CAM [9, 10], which calculates the interpretation results in the same way as CAM by obtaining a weighted sum of the weights of each feature map to generate the heat map. The critical difference between the two methods is calculating the feature map weights wkc . Grad-CAM is distinct from CAM since the weights are calculated by gradient derivation. The following is the computation for the localization map:    wkc Ak (x, y) (2) LcGrad −CAM (x, y) = ReLU k

The coefficient

wkc

is defined as: wkc =

H W 1   ∂Y c H ·W ∂Ak (i, j) i=1 j=1

(3)

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where Ak (x, y) is the activation of node k in the target layer of the model at position (x, y) and Y c is the model output score for class c before the Softmax layer. After calculating the weights of each feature map within the target category, the corresponding feature maps are weighted and summed to generate the final heat map. Notably, to prevent the final interpretation result from including features from other categories, the ReLU layer is introduced when calculating the heat map to ensure that the final result only considers the features that positively influence category c. 3.1 Pros and Cons of Grad-CAM The Grad-CAM method solves the problem of the CAM method needing to modify the network structure and analyzing any intermediate convolutional layer. Therefore, the Grad-CAM method significantly improves the utilization of interpretable methods. In addition, the authors combined the guided back-propagation algorithm to produce more fine-grained interpretation results. However, there are still problems with the Grad-CAM method: Grad-CAM cannot cover the target completely when multiple similar targets appear simultaneously; the final weighting result may be biased due to the GAP calculation and gradient problems; there are features with large weights, but their corresponding activation features have little impact on the decision; this method only considers the back-propagation gradient and does not consider the effect of forward prediction; neither the coarse-grained heat map generated in the deep layer nor the fine-grained heat map generated in the shallow layer are accurate enough.

4 Related Works In response to the problems of Grad-CAM++, several methods have been subsequently derived to address them. This chapter reviews three CAM methods: Grad-CAM++, Score-CAM, and LayerCAM. 4.1 Grad-CAM++ The Grad-CAM++ method [11], based on Grad-CAM, improves the visual effect of the final interpretation, mainly when multiple target instances appear within a single image. The authors introduce gradient outputs that are weighted for pixels in specific locations. By measuring the significance of each pixel in the feature map, this method produces an accurate representation of the higher order. The following is the computation for the localization map:  wkc Ak (x, y) (4) LcGrad −CAM ++ (x, y) = k

The coefficient

wkc

is defined as: wkc =

W H   i=1 j=1

 αck (i, j) · ReLU

∂Y c ∂Ak (i, j)

 (5)

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where Ak (x, y) is the activation of node k in the target layer of the model at position (x, y), Y c is the model output score for class c before Softmax layer, and αck (i, j) being defined as: αck (i, j)

=

1 ∂Y c i,j ∂Ak(i,j)

=

∂2Y c (∂Ak (i,j))2

2·

∂2Y c (∂Ak (i,j))2

+



∂3Y c a,b Ak (a, b) · (∂Ak (i,j))3

(6)

∂Y c ∂Ak (i,j)

= 1 else 0. The method modifies the weighting coefficients to improve the target interpretation’s coverage. While using the interpretation method to refine model knowledge efficiently, the authors apply it to image caption generation and 3D action recognition, thus expanding the interpretation method’s scope of application. However, this computational correction only addresses the own-derived weights and does not change the situation that the weight assignment itself is problematic. if

4.2 Score-CAM The proposal of Score-CAM [12] redefines the importance of feature weights. As a result of computational processes such as gradient disappearance, there are features with large weights, but their corresponding activation features have little impact on the decision. j Given two feature mappings Ail and Al of convolutional layer l, the generated input region j Al is considered at least as important as Ail for the target class c if the corresponding weights wic ≥ wjc . However, this is not the case. To solve this problem, the Score-CAM method measures the linear weights by computing the global confidence scores of the feature maps through the same neural network structure, thereby obviating the need for gradient computation and resolving issues associated with gradient computation. There are two phases in the Score-CAM calculation procedure. Firstly, it extracts the feature map of the final convolutional layer, as with the previously described methods. Secondly, the response value of the image under the target category is recalculated by upsampling the feature map, obtaining the mask information, and overlaying it with the original map before being inputted into the same network. The final interpretation results are generated by linearly weighting and summing the response values obtained in the second step to the feature maps obtained in the first step. The following is the computation for the localization map:    c c wk Ak (x, y) (7) LScore−CAM (x, y) = ReLU k

The coefficient wkc is defined as:

 wkc = softmax Y c (Mk ) − Y c (Xb ) k

(8)

where Ak (x, y) is the activation of node k in the target layer of the model at position (x, y), Y c (X ) is the model output score for class c before Softmax layer for input X , Xb

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is a baseline image, and Mk is defined as follows: ⎞ ⎛ U (Ak ) − min U (Am ) m ⎠  Xb Mk = ⎝ max U (Am ) − min U (Am ) m

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m

where  refers to the element-wise multiplication and U is the upsampling operation. The Score-CAM method eliminates the effect of gradient calculation on the weights and re-specifies the importance of the feature maps’ activation features to produce more focused visualization results. However, the feature’s importance is obtained by ignoring the background information. In addition, the time required to multiply the feature map with the original map and then calculate the weights using a neural network significantly increases during the calculation process. 4.3 LayerCAM CNN’s final convolutional layer generates the class activation maps. As a result of the low spatial resolution of the last convolutional layer, these class activation maps can only locate coarse regions of the target objects. The authors present a straightforward and efficient strategy called LayerCAM [13], allowing us to gather information ranging from coarse-grained localization to fine-grained details. This method considers the importance of different channels and different spatial locations. The weight of each location shows the importance of this location in the target class feature map. LayerCAM uses backward class-specific gradients. A positive gradient associated with a specific area in a feature map suggests that raising the intensity of this place would positively affect the prediction score for the target class. Therefore, we use positive gradients as weights for locations, whereas we assign zero to locations with negative gradients. To generate the layer-specific class activation map, LayerCAM first multiplies the activation value at each position of the feature map by weight before linearly combining along the channel dimension. The following is the computation for the localization map:    wkc (x, y) · Ak (x, y) (10) LcLayer−CAM (x, y) = ReLU k

The coefficient wkc is defined as: 

wkc (x, y)

∂Y c = ReLU (x, y) ∂Ak (i, j)

 (11)

where Ak (x, y) is the activation of node k in the target layer of the model at position (x, y), and Y c is the model output score for class c before Softmax layer. The LayerCAM method can build accurate class activation maps not just from the last convolutional layer but also from shallow layers where coarse spatial locations and fine-grained object details can be obtained. In addition, layers of class activation maps complement one another, enabling them to be combined to produce more accurate and

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comprehensive class-specific object regions, which is of great help to weakly- supervised tasks. Moreover, LayerCAM can be deployed without changing the network structures or back-propagation techniques. Besides, the ability to generate fine-grained objects can be utilized to precisely find microscopic faults in industrial images.

5 Discussion 5.1 Significance of CAM-Related Methods Following the preceding summary, the significance of CAM-related methods can be summed into three points. Reconcile Knowledge Structure Contradictions. When model results diverge from the direction of people’s understanding, it is necessary to explain the discrepancy between human cognition and machine decision-making. In addition, machine teaching is currently a trending topic, i.e., explaining decisions by machines complements and improves human comprehension of the world. Interpretability is Vital in Specific Areas. The absence of comprehension and validation of systematic decision processes is a severe deficiency. For instance, in the field of medical diagnosis [14], combining deep learning techniques and interpretability to assist doctors in clinical diagnosis can further improve the correctness and diagnostic efficiency, as well as eliminate human subjective errors; in the field of finance, an interpretable financial system can not only obtain accurate prediction results but also help people trust the reasoning behind the model’s decisions. Improve the Precision and Credibility of the Model. Understanding which input features are used to make decisions when the model is correct or incorrect helps humans analyze and diagnose models. This improves the model’s precision and credibility, breaks the deep learning bottleneck, and increases the value of using the model.

5.2 Challenges Raised by CAM-Related Methods Despite the significant progress made thus far, model interpretability-related research still faces multiple challenges. For example: 1. There are no consistent measures for evaluating the interpretability method due to the limitations of human cognition and the complexity of evaluation. 2. Model accuracy is generally inversely related to interpretability and correspondingly to complexity. Consequently, it is necessary to better balance model precision and interpretability. 3. Privacy protection implies the concealment of information, whereas interpretability produces information transparency; hence, privacy cannot be fully protected.

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5.3 Future Research Human knowledge and expert participation can be included in the future direction of deep learning interpretability research. Incorporating human expertise into deep learning models facilitates the comprehension of features and reduces data bias. As the final recipients of the interpretation, human involvement will be the future direction. Using human-computer interaction to enhance human expert participation is a topic to be studied in the future.

6 Conclusion In the first part, we discussed the necessity of interpreting deep learning models. Then, we presented CAM and the related methods and explored their benefits and limitations. Finally, we analyzed the significance, challenges, and future research directions. The interpretability of deep learning is an essential topic of current interest. We believe that this review will assist people in gaining a better understanding of the interpretability framework, and we look forward to more research to advance the field.

References 1. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) 2. Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009) 3. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: ICML (2010) 4. Hinton, G.E, Srivastava, N., Krizhevsky, A., et al. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. (2012) 5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017) 6. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016) 7. Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016) 8. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013) 9. Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) 10. Selvaraju, R.R., Das, A., Vedantam, R., et al.: Grad-CAM: why did you say that?. arXiv preprint arXiv:1611.07450 (2016) 11. Chattopadhay, A., Sarkar, A., Howlader, P., et al.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 839–847. IEEE (2018) 12. Wang, H., Wang, Z., Du, M., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020) 13. Jiang, P.T., Zhang, C.B., Hou, Q., et al.: LayerCAM: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021) 14. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221 (2017)

Robust Multi-person Reconstruction in Right Spatial Arrangement from In-the-Wild Scenes Wei Wang1 , Ronghui Zhang2 , Hai Huang1 , and XiaoJun Jing1(B) 1 School of Information and Communication Engineering, Beijing University of Posts

and Telecommunication, Beijing, China [email protected] 2 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

Abstract. Research on monocular 3D human reconstruction has great progress recent years. Existing methods struggled when input with in-the-wild scenes dues to inherent depth ambiguity, occlusion and interaction. Most existing multiple people reconstruction researches follow two-stage paradigm that first implement body detect in monocular scenes and then estimate multiple people 3D body meshes independently, which result in ignoring the relative depth and interaction between human in the scene. To solve this problem, we present a end-to-end paradigm to directly regress multiple people 3D pose and shape, as well as their relative spatial arrangement from monocular in-the-wild scenes. We present a multi-task feature extractor framework that efficiently comprising 2D pose estimation and monocular depth estimation, a decoupling module that predicts and optimizes human body mesh parameters. Our method simultaneously predicts 2D joint heatmap and scene depth map, which can jointly extract image feature to drive the 3D reconstruction task. We validate our method on challenging scenes with multiple people and interaction, showing that our method obtain good results both quantitatively and qualitatively. Keywords: 3D body reconstruction · Multi-task feature extractor · Crowded scenes · Pose estimation · Depth estimation

1 Introduction Monocular 3D human reconstruction is a basic but covers cross-cutting difficult fields in computer vision, refers to to detect human poses in images and represent them in digital manner in computer. 3D human reconstruction from images [1–3] or videos has great progress in these years. Compared with reconstruction from multi-view cameras or RGBD cameras, human reconstruction from monocular image or video is more practical and less costly, and is therefore widely used in clothed human reconstruction [4, 5], virtual reality and gaming. By introducing a parametric body model [1, 3, 6, 7] and body prior, the body models of different poses and shape distributed in a high-dimensional space could be mapped to a low-dimensional parameter space, thus a complete triangular mesh model of the human can be obtained by only a set of low-dimensional parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 408–416, 2023. https://doi.org/10.1007/978-981-19-9968-0_49

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However, most of the current researches focus on monocular human reconstruction in single people scenes [8–10], and there are only few studies on human reconstruction in monocular multiple people scenes or in-the-wild scenes. Most existing multiple people reconstruction researches [11, 12] follow a two-stage pipeline that first implement body detect in monocular in-the-wild scenes and then estimate multiple people 3D body meshes independently, which ignored the relative depth of people and human interaction in the scene. Besides, most of the multiple people scenes reconstruction works do not directly estimate from image or video, they rely on a accurate prior input such as cropped human bounding box and silhouettes, and they are not explicitly reason about the relative depth of people. In this paper, we propose a monocular end-to-end 3D human reconstruction paradigm that can simultaneously robustly estimate multiple people 3D pose and shape in monocular scenes, as well as their relative spatial distributions. The contributions of our paradigm are summarized as follows: • We propose a monocular end-to-end paradigm that can directly reconstruct multiple people 3D mesh from monocular in-the-wild scenes. • We present a cascaded network can effectively integrate feature from depth and pose estimation, a decoupled-optimization module to predict and improve body model parameters. • We validate our method on challenging scenes with multiple people and interaction, showing that our method obtain good results both quantitatively and qualitatively.

2 Body Parametric Representation In this work, for 3D human reconstruction, we represent with SMPL [1] parametric model. SMPL is a parametric human model with N vertices that allows us to a few number of shape and pose parameters to generate or adjust 3D body mesh. SMPL provides a differentiable function M (β, 0) that input with estimated pose parameters θ and shape parameters β and then generates a triangulated body mesh M ∈ RN ×3 with N = 6980 vertices: M (β, θ ) = W (TD (β, θ ), J (β), θ, W ) TP (β, θ ) = T + BS (β; S) + BP (θ ; P) where W (·) is linear blend skinning function that rotates body mesh vertices TP (·) around the body keypoints J (β) by the blend shape weights W ∈ RN ×K . The body mesh vertices are then used to calculate the 3D joints J ∈ RNj ×3 with the pretrained linearregressor. These 3D joints  could further projected on the image as 2D keypoints K = (J ) ∈ RNj ×2 , where (·) denotes the camera project model based on the camera parameters.

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3 Human Reconstruction Framework In this Section, we describe our multiple people reconstruction framework(see Fig. 1 for a overview). We describe in detail our proposed baseline network consisting a feature extractor (Sect. 3.1), a decoupling module containing a SMPL parameter regressor (Sect. 3.2) and a parameter refine regressor (Sect. 3.3). Our loss function and implementation details are presented in Sect. 3.4 and Sect. 3.5. Given an RGB image as input, our network final output SMPL parameter map and Camera map. The SMPL parameters are then passed though SMPL module to produce multiple 3D body meshes.

Fig. 1. Network structure diagram of our method.

3.1 Pose-Depth Feature Extractor To drive the 3D reconstruction task, we design a multi-task feature extractor framework efficiently comprising 2D pose estimation and monocular depth estimation. Given the raw input image, our feature extractor pass it through three modules, a CNN modules to extract the monocular scene features F ∈ RCf ×h×w , an off-the-shelf bottom-up pose estimation algorithm [13] to predict the body kepoints position P2d and a depth estimation algorithm [14] to predict the depth map D ∈ R1×hi ×wi . Here, we set the size of output map is h = w = 64 Js = 25, is the numbers of joints. We then generate a joint heatmap H ∈ RJs ×h×w based on 2D joint position P2d and depth map D. The crowded scene feature maps F, improved joints heatmap H and depth map D are stacked along the channel and passed to the post image feature extractor module which contains the residual block and MLP module to extractor the final feature maps F  ∈ RC×h×w . Final feature maps F  extracted from feature extractor has both the crowded scene information, depth information and body joint feature, which can efficiently guide the SMPL Parametric Regressor. 3.2 SMPL Parametric Regressor Given the final features F  from the feature extractor,  the SMPL parameter regressor  respectively regresses camera parameters π = s, tx , ty and SMPL map S ∈ RH×W×142 which contains the pose and shape parameters of the SPML mesh: shape parameters

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β ∈ R10 , pose parameters θ ∈ R6×22 . The shape parameter β ∈ R10 is the principal PCA coefficients of shape space. The pose parameters θ ∈ R6×22 contain the 3D rotation of the 22 body joints in a 6D representation [15]. By decomposing the pose parameters, the first three parameters Rg ∈ R3 of pose θ ∈ R6×22 denotes the body global orientation in the canonical space, the rest of the pose parameters are the relative 3D rotation of joint points with respect to its parent joint in a kinematic  tree. The camera parameters  contains the scale parameter and the translation t = tx , ty of the 3D body mesh, similar to [13], reflect relative translation to the image center on the x and y axis: x = s ∗ vx + tx , y = s ∗ vy + ty

3.3 Parametric Refine Regressor Human poses are high-dimensional and complex and there are a lot impossible pose, such as knee and elbow can only bend in one direction, people cannot bend knees and elbows bending in opposite directions. Thus we can use the human prior to regular SMPL pose parameters. We use a prior VPoser introduced in [3], which learns a normal latent distribution of body pose, to avoid generating poses that are impossible for humans. The multiple 3D body pose parameters obtained by the SMPL Parametric Regressor are then passed through Vposer to obtain the final SMPL pose parameters. With the differentiable function M (β, θ ) provided by SMPL, the shape parameters β ∈ R10 , pose parameters θ ∈ R6×22 are passed through SMPL module to obtain multiple 3D body meshes respectively. The coordinates of final 3D keypoints J mapped from mesh vertices can be obtained by a sparse linear regressor J (·). These 3D keypoints can be projected into the image based on the camera project model:    Jˆ = J T + BS (β; S)

3.4 Loss Functions To supervise our network, our objective loss function for 3D body meshes is showed as follows: LP = ωp Lp + ωs Ls + ωj3d Lj3d + ωpaj3d Lpaj3d + ωpj2d Lpj2d LP is the L2 loss of the pose parameters in the 3 × 3 rotation matrix format. Ls is the L2 loss of the shape parameters. Lj3d is the L2 loss of the 3D joints J obtained from body mesh. Lpj3d is the L2 loss of the 3D joints J after Procrustes alignment. Lpj2d is the L2 loss of the projected 2D joints Jˆ . Lastly, ω denotes the corresponding loss weights. Here, we set our network loss weights as follows: ωj3d = 80, ωpaj3d = 100, ωpj2d = 120, ωp = 30, ωs = 1 to ensure our network loss weights items can efficiently accomplish 3D reconstruction task.

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3.5 Implementation Details We use 3D datasets Human3.6M [16], MuCo-3DHP [17] and three 2D datasets MSCOCO [18], MPII [19] and CrowdPose [20] for training following the standard split protocols. Human3.6M is an indoor dataset where a single person is visible in each frame. MuCo-3DHP is 2D multi-person images and ground truth of 3D labels. MSCOCO, MPII, and CrowdPose is in-the-wild datasets with 2D pose annotations, including multiple frames for each sequence. For our proposed multiple 3D human reconstruction framework training, we use the Adam optimizer, our code is implemented in Pytorch on 1080 Ti GPU. The batch size of our reconstruction framework input is 64. The initial learning rate of our reconstruction network is 10−4 , the learning rate of our network is reduced with factor 5 at 3th and 12th epochs during the first 15 epochs.

4 Experimental Results To objectively and comprehensively measure the performance of our framework, in this part, we qualitatively and quantitatively measure performance of our end-to-end framework. Our intermediate feature from our multi-task feature extractor framework is showed in Fig. 2, which can efficiently guide the SMPL Parametric Regressor in in-the-wild multiple people scenes.

Fig. 2. Pose-Depth intermediate feature from feature extractor.

4.1 Qualitative Comparisons As is showed in Fig. 3, we compared our proposed end-to-end framework with other work. The left column is the original input. The two columns on the right are results of ROMP and the results of our framework. Experimental results qualitatively reflect that our reconstruction framework have better result in in-the-wild multiple people scene than other work, and is more robust in occlusion and depth scenes.

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Fig. 3. Qualitative comparisons. From left to right: Original, ROMP, Ours.

4.2 Quantitative Comparison As is reported in Table1, we quantitatively evaluate our proposed end-to-end framework performance on 3DPW dataset. 3DPW dataset is a single-view multi-person 3D human benchmark with abundant 2D and 3D labels. We implemented MPJPE and PA-MPJPE to evaluate the 3D pose accuracy. For the evaluate on 3D surface error, we also use the PVE. We compare our proposed end-to-end framework with two monocular multiple human reconstruction work [13] and [14], and our method achieves better performance on 3DPW datasets.

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MPJPE↓

PA-MPJPE↓

PVE↑

CRMH [13]

105.9

71.8

28.5

ROMP [14]

87.0

62.0

34.4

Ours

81.6

57.9

36.2

4.3 Ablation Experiment We conducted the ablation experiment by removing feature extractor modules or replacing intermediate representations to verify the effectiveness of our multi-task feature extractor. We present comparative experiment results on the 3DPW follow Protocol 1, which inference without ground truth input. And as is showed in Table2, the image feature conduct as intermediate representations in the first row input with cropped image and extracts image features without any other as guidance. The second row using original image as input and using 2D pose estimation as intermediate representations guidance. The significant difference proves that the our pose-depth feature is beneficial for 3D human reconstruction task in the in-the-wild scenes. Table 2. Ablation on the intermediate representations for parametric regressor. Input feature

MPJPE↓

PA-MPJPE↓

Image feature

113.6

67.3

2D pose

93.4

65.6

Pose-depth feature

81.6

57.9

5 Conclusions We propose an end-to-end 3D human reconstruction paradigm that can directly robust regress multiple people 3D mesh from in-the-wild scenes. At the key of our method is its feature extractor with pose and depth guidance, it extracts robust pose-depth image features and can effectively to drive the 3D reconstruction task. Experimental results showed that our reconstruction framework outperforms previous works both quantitatively and qualitatively in accuracy and robustness. We will try to reconstruct 3D body in more difficult and occluded scenes.

References 1. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multiperson linear model. ACM Trans. Graph. (TOG) 34, 1–16 (2015)

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2. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 561–578. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_34 3. Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10975–10985 (2019) 4. Yu, T., et al.: Doublefusion: real-time capture of human performances with inner body shapes from a single depth sensor. In: Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7287–7296 (2018) 5. Zheng, Z., Yu, T., Liu, Y., Dai, Q.: Pamir: parametric model-conditioned implicit representation for image-based human reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3170–3184 (2021) 6. Osman, A.A.A., Bolkart, T., Black, M.J.: Star: Sparse trained articulated human body regressor. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 598–613. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_36 7. Santesteban, I., Garces, E., Otaduy, M.A., Casas, D.: SoftSMPL: data-driven modeling of nonlinear soft-tissue dynamics for parametric humans. In: Proceedings of the Computer Graphics Forum. Wiley Online Library, vol. 39, pp. 65–75 (2020) 8. Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7122–7131 (2018) 9. Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3D human dynamics from video. In: Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5614–5623 (2019) 10. Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2252–2261 (2019) 11. Jiang, W., Kolotouros, N., Pavlakos, G., Zhou, X., Daniilidis, K.: Coherent reconstruction of multiple humans from a single image. In: Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5579–5588 (2020) 12. Sun, Y., Bao, Q., Liu, W., Fu, Y., Black, M.J., Mei, T.: Monocular, one-stage, regression of multiple 3D people. In: Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11179–11188 (2021) 13. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017) 14. Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12179–12188 (2021) 15. Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5745–5753 (2019) 16. Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1325–1339 (2013) 17. Mehta, D., et al.: Monocular 3d human pose estimation in the wild using improved CNN supervision. In: Proceedings of the 2017 international conference on 3D vision (3DV), pp. 506–516. IEEE (2017)

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Research on the Curriculum System of Electronic Information Engineering for the Certification of Engineering Education Songlin Sun(B) , Jiaqi Zou, Zhilei Ling, Hai Huang, Tiankui Zhang, Caili Guo, and Changchuan Yin Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected]

Abstract. The curriculum system is very important in the professional training program, and it is also an important inspection link in the professional certification of engineering education. For electronic information majors, some common problems will be encountered when setting up and optimizing the curriculum system. This paper takes the electronic information engineering major of Beijing University of Posts and Telecommunications as an example to introduce the cases in the curriculum system. Keywords: Curriculum system · Electronic information engineering · Certification of engineering education

1 Introduction Engineering education certification, as an engineering education quality assurance system which is internationally accepted, becomes an important basis for accomplishing international mutual recognition of engineering education and engineer qualifications [1, 2]. The engineering education certification work in China started in 2006, which is the basis and important part of the reform of the engineer system. In 2016, China has joined the Washington Agreement as a full member. The objectives of engineering education certification are to promote the continuous improvement of the quality assurance system of engineering education in China, promote the innovation of engineering education in China and continue to raise the standard of engineering education; establish a certification system for engineering education that is linked to the engineer system; foster ties between the business and education sectors; and improve the ability of engineering education personnel to adapt to the needs of the industrial development.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 417–421, 2023. https://doi.org/10.1007/978-981-19-9968-0_50

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China Engineering Education Certification Association CEEAA was established in 2015 to be responsible for the certification of Chinese universities. To this end, it formulated the Policy and Procedure of Engineering Education Accreditation, and constantly updated the Interpretation and Use Guide of the General Standards for Engineering Education Accreditation. Established in accordance with the regulations of the Ministry of Education, the full-time ordinary four-year undergraduate majors of ordinary colleges and universities that have graduated for three times and awarded the bachelor’s degree in engineering can apply for certification, but they need to be majors in 20 professional fields, such as machinery, computer, electronic information and electrical engineering, as specified in the corresponding supplementary standards for each major in the Engineering Education Certification Standards. By the end of 2021, a total of 288 ordinary colleges and universities in China had passed the engineering education certification for 1977 majors, involving 24 engineering majors such as machinery and instrument.

2 Introduction of Electronic Information Engineering The electronic information engineering specialty of Beijing University of Posts and Telecommunications (BUPT) passed the certification of engineering education specialty in 2010, which is a specialty with Beijing characteristics; In 2019, it passed the professional certification of engineering education again, and won the national first-class undergraduate major in 2020. From 1955, BUPT has been specialized in radio communication and broadcasting, television, image transmission and processing. It was renamed Electronic Information Engineering in 1997. Electronic Information Engineering, as one of the oldest majors of Beijing University of Posts and Telecommunications, is a key specialty of the university and a characteristic specialty of Beijing. The national key discipline of “Information and Communication Engineering” won the first place in the national academic evaluation organized by the Ministry of Education in 2012 and the fourth round of national academic evaluation in 2016. Facing the national strategy and the development needs of the information and communication, radio and television and network audio-visual industries, this major aims to cultivate technical talents or leading reserve talents in the information and communication, radio and television and network audio-visual industries with innovation and entrepreneurship awareness and lifelong learning ability, based on morality and talent cultivation, with the goal of building a world-class specialty. BUPT enrolls students in communication engineering (major specie enrollment). This major is trained in the first and second year in accordance with the major category of “communication engineering (major specie enrollment)”. The students mainly take general education courses and basic courses. In the fourth semester, determine their respective majors based on the number of volunteers and majors, and began to take professional courses in the third year.

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3 Design Requirements of Curriculum System The professional certification of engineering education requires that the curriculum in the curriculum system can sustain the accomplishment of graduation requirements, and enterprises or industry experts are involved in the curriculum system design. The curriculum system must include the following four parts: Part 1. The general education courses of humanities and social sciences, accounting for more than fifteen precent of the total credits, allow students to weigh and consider various constraints such as economy, environment, law and ethics when engaging in engineering design. Part 2. Mathematics and natural science courses, as an essential part to meet the graduation requirements, and at least fifteen percent of the total credits. The study of mathematics and natural science courses will develop a good disciplinary thinking for subsequent learning and lay the foundation for cultivating good numeracy skills. Part 3. Professional education courses including engineering foundation courses, professional basic courses and professional courses in order to meet the graduation requirements of the major, and more than thirty percent of the total credits. These professional education courses can furthermore reveal the performance of the training about the application capabilities of mathematics and natural science, and promote the development of system design and implementation ability. Part 4. Engineering practice and graduation design (Thesis), and at least twenty percent of the total credits. Establish a perfect practical teaching system, collaborate with corporations, perform practice and training, and foster students’ practical ability and innovation ability. The topic of graduation design (thesis) should be selected in combination with the actual engineering problems of the major, to promote the formation of engineering awareness, cooperation spirit and the ability to comprehensively apply the knowledge learned to solve practical problems. Enterprise or industry experts participate in the guidance and evaluation of graduation design (thesis). In curriculum system setting and optimization, there are four common problems: the curriculum system lacks systematic design, the curriculum system cannot effectively support all graduation requirements, the description of the curriculum objectives in the syllabus is unreasonable, the task of industry and enterprise experts participating in the design and revision of the curriculum system is not clear and their role is not reliable. In view of the above problems, such as embedded systems [3], software engineering [4] and other professional curriculum systems [5, 6] have cases to solve and optimize. This paper also tries to practice in electronic information engineering.

4 The Curriculum System The structure of the curriculum system of the electronic information engineering major in BUPT is shown in Table 1.

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Course category

Main content

Compulsory courses credit

Elective courses credit

15.75

2

6

2

Sports

0.25

0.75

Military theory

2

Mental health

0.5

General education Ideological and Political theory course English

Quality education course

6

Safety education

0

Mathematics and Natural science

Basic course of mathematics and natural science

28

Professional education

Basic computer course

7.5

Engineering foundation 25 course Professional basic courses

7

Professional courses Practice

17

Practice of ideological and political theory course

2

Military training

2

Labor education

2

Classroom practice

9.25

Professional practices

14

Graduation design (thesis)

10

Innovation and Entrepreneurship

3

2

4

In this structure of the curriculum system, the total credits are 168, including 119.75 credits for theory, 41.25 credits for practice and 7 credits for Innovation and entrepreneurship education. In this curriculum system, the four necessary parts specified by the professional certification of engineering education are included, and the corresponding requirements for credit ratio are met. The 12 indicators required for graduation are supported by all

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courses, including engineering knowledge, problem analysis, design/development solutions, research, use of modern tools, engineering and society, environment and sustainable development, professional norms, individuals and teams, communication, project management and lifelong learning. According to the curriculum system, the major has set up basic courses, specialized basic courses and specialized courses, and carefully divided the 12 indicators supported by each course to form a curriculum matrix, thus completing the design of the entire curriculum system.

5 Conclusion According to the requirements of engineering education certification, the electronic information engineering specialty of BUPT has set up a reasonable curriculum system and optimized it for common problems. Through practice, it has achieved very good results.

References 1. GuanBo, F., Yue, J.: Innovation of software engineering course with the background of engineering education professional certification. In: 2021 16th International Conference on Computer Science & Education (ICCSE), pp. 628–631 (2021). https://doi.org/10.1109/ICCSE5 1940.2021.9569509 2. Chen, Y., Liu, Z.: Teaching reform of engineering graphics education in terms of engineering education professional certification in Guangzhou, China. In: 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 596–601 (2018). https://doi.org/10.1109/TALE.2018.8615296 3. Xiaojuan, L., Yong, G., Huimei, Y.: Curriculum development and progressive engineering practice design in embed system education. In: IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications 2008, 228–232 (2008). https://doi.org/ 10.1109/MESA.2008.4735752 4. Chen, X., Hu, F., Lai, J., Tao, C., Shin, H.: Research on engineering certification-oriented course design for software engineering under the background of new engineering. In: 2021 10th International Conference on Educational and Information Technology (ICEIT), pp. 101–105 (2021). https://doi.org/10.1109/ICEIT51700.2021.9375577 5. Macias-Guarasa, J., Montero, J.M., San-Segundo, R., Araujo, A., Nieto-Taladriz, O.: A projectbased learning approach to design electronic systems curricula. IEEE Trans. Educ. 49(3), 389–397 (2006). https://doi.org/10.1109/TE.2006.879784 6. Asef, P., Kalyvas, C.: Computer-aided teaching using animations for engineering curricula: a case study for automotive engineering modules. IEEE Trans. Educ. 65(2), 141–149 (2022). https://doi.org/10.1109/TE.2021.3100471

Multimedia and Communication Networks

An S Band Antenna Using Meta-surface for Satellite TT & C Test Xiangwei Ning1(B) , Fanhui Zhou2 , Liang An1 , Shuo Feng1 , Pengfei Zhao1 , and Aixin Chen3 1 Institute of Remote Sensing Satellite, Beijing, China

[email protected]

2 Institute of Spacecraft System Engineering, Beijing, China 3 School of Electronic and Information Engineering, Beihang University, Beijing, China

Abstract. This paper presents a novel S band antenna using Meta-surface. The working frequency band of the antenna is from 2.0 GHz to 2.4 GHz, which has covered the whole Satellite TT&C S band. And the antenna’s linear polarization mode can receive both left-handed circularly polarized and right-handed circularly polarized signals from satellites. In addition, the wide beam characteristics make it convenient to use, its 3 dB beam width can reach 80°. Furthermore, the antenna has low profile and small volume and the overall dimension is only 65 mm * 65 mm * 10 mm. These characteristics make the antenna have more leading advantages in satellite testing. Keywords: Satellite TT&C S band · Meta-surface · Linear polarization · Wide beam

1 Introduction Telemetry, tracking, and command (TT & C) system is a key subsystem of satellites to connect with ground station [1, 2]. In order to verify the functionality and reliability of TT & C system in ground test period, we design an antenna using Meta-surface as part of the ground verification system. At present, S band is the most common frequency band for TT & C, so the antenna we designed aims at S band [3]. In current test systems, most S band antennas are spiral antennas, whose volume are large and profile are high [4, 5]. This kind of antenna is not easy to carry and integrate. In addition, spiral antenna is single circular polarized without generality. In order to overcome these disadvantages, we design a novel antenna. Meta-surface is a kind of metamaterial whose effective thickness is much less than the transmission wave’s wavelength [6, 7]. It has the benefits of metamaterial such as enhancing the beam intensity and expanding frequency bandwidth. The combination of Meta-surface and antenna design can improve some characteristics of the original antenna. So we introduce it into our antenna design. The antenna proposed by this paper is improved microstrip antenna using metasurface. It not only has the advantages of microstrip antenna such as small volume, low profile and linear polarization, but also overcomes the traditional microstrip antenna’s narrow frequency band and small power capacity by using meta-surface. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 425–429, 2023. https://doi.org/10.1007/978-981-19-9968-0_51

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2 Design Structure and Mentality of the Antenna The antenna consists of two parts: microstrip part and meta-surface part. Figure 1 has shown the overall framework. The microstrip part is composed by two layers of metal and one layer of medium. We choose Rogers RT/duroid 5880 (tm) as the medium material for the low transmission loss. The relative permittivity of this medium is 2.2 and the loss tangent is 0.0009. And the novel meta-surface has a layer of metal and a layer of medium. The material of medium is Rogers RO4003 (tm) whose loss tangent is 0.0027 and relative permittivity is 3.55. The microwave transmits between the metal layer of the meta-surface and the upper metal layer of microstrip. After many times of reflecting and superposition, the pattern and resonance of the proposed antenna are formed. The characteristics and adventures of the antenna is mainly decided by this structure.

Fig. 1. The structure of the antenna. (a) side view. (b) 3D view. h1 = 0.762 mm, h2 = 6 mm, h3 = 3.2 mm.

The structure of microstrip antenna is shown in Fig. 2. The upper metal layer is feed plane and the lower is ground plane. In order to expand bandwidth and reduce volume, we design slotting on the feed plane. The feeding way is back-fed and the feeding point is (−16.4, 15) mm on the ground plane. The feeding plane can not only generate microwave, but also play a reflection role in the process of microwave propagation. The superposition of electromagnetic fields in the air space between the meta-surface and the feed plane increases the resonant point of microstrip antenna and constitutes wide beam pattern.

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Fig. 2. The structure of the microstrip antenna. (a) ground plane. (b) feed plane. A = b = 65 mm, a1 = 34.8 mm, b1 = 48.4 mm, c = d = 5 mm, e = 2 mm, f = 1 mm.

The structure of meta-surface has be shown in Fig. 3. The 2 × 2 square metal array elements form the metal layer of the meta-surface. In order to increase resonance points near the main resonance point of the microstrip antenna, we design slots on the metal array elements. The extra slots increase the wave penetration points. In addition, the proposed meta-surface can help to expand beam and realize wide beam.

Fig. 3. The structure of the MS. L = 30 mm, g = 2 mm, c1 = 8 mm, d1 = 6 mm.

The design and size of the antenna are optimized by simulation software to achieve the desired antenna characteristics.

3 The Characteristics and Applications of the Antenna The result of the antenna is simulated. The antenna’s 3D pattern has been shown by Fig. 4. As can be seen from the figure, flatness of main beam lobe is good.

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Fig. 4. The 3D pattern of the antenna at 2.2 GHz.

The 3 dB beam width at central frequency point can reach 81° as shown in Fig. 5 (a). The pattern’s wide beam and smooth have been verified by simulation in the whole working band. The axis ratio of main radiation direction is shown by Fig. 5 (b). The minimum axis ratio is 7.14 dB in the working frequency band which is more than 3 dB. So the proposed antenna is linear polarization in the whole working frequency band.

Fig. 5. The. (a) The x-z plane’s pattern of the antenna at 2.2 GHz. (b) The axis ratio of main radiation direction.

The antenna’s working frequency is from 2.0 GHz to 2.4 GHz which contains the whole satellite’s S band TT&C frequency point. The return loss has been shown by Fig. 6. These characteristics make the antenna have advantages of application on ground test period. Only one antenna can match all antennas of S band on satellites and it is convenient to direct and carry for outfield experiment.

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Fig. 6. The return loss of the antenna.

4 Conclusions This paper proposes an S band antenna using meta-surface for satellite TT&C test. According to user’s requirements, the antenna’s working frequency band is 2.0−2.4 GHz. Main beam width of pattern is wide and the radiation range is large. In addition, the antenna is linear polarization. These characteristics are suitable for the satellite TT&C’s wireless test. And the antenna’s small size and low profile make it portable and convenient to use.

References 1. Mishurov, A.V., Panko, S.P., Khnykin, A.V.: Standards for the design of tracking, telemetry and command subsystem of spacecraft. In: 2017 International Siberian Conference on Control and Communications, pp. 1–4 (2017) 2. Yao, Z. -Y., Wu, X. –Y., Wu, X. –Y.: A SysML description for TT&C system. In: Global Reliability and Prognostics and Health Management, pp. 1–4 (2021) 3. Mahardika, C., Nugroho, B.S., Syihabuddin, B., Prasetyo, A.D., Nurmantris, D.A.: Modified wilkinson power divider 1 to 4 at S band as the part of smart antenna for satellite tracking, telemetry, and command subsystem. In: 2016 International Conference on Control, Electronics, Renewable Energy and Communications, pp. 70–73 (2016) 4. Choi, E., Lee, J.W., Lee, T., Lee, W.: Circularly polarized S band satellite antenna with parasitic elements and its arrays. In: IEEE Antennas and Wireless Propagation Letters, vol. 13, pp. 1689– 1692 (2014) 5. Thunyakaset, N., Ratanacharoen, S., Namsang, A., Lerdwanittip, R., Chomtong, R., Akkaraekthalin, P.: Design of S band cubesat antenna. In: 2021 9th International Electrical Engineering Congress (iEECON), pp. 543–546 (2021) 6. Gupta, G., Harish, A.R.: Wideband antenna using cavity backed non-uniform double layered meta-surface. In: 2017 IEEE International Conference on Antenna Innovations & Modern Technologies for Ground, Aircraft and Satellite Applications (iAIM), pp. 1–5 (2017) 7. Chen, J., Huang, S., Cao, J., Wu, Q., Tang, X.: All polarization gain enhancing lens based on meta-surface. In: 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–4 (2019)

Design and Implementation of a Test and Verification System for Spaceborne Synthetic Aperture Radar An Liang, Liang Jian(B) , Yang Li, Hao Zhiya, and Zhongjiang Yu Institude of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China [email protected]

Abstract. For the requirement of ground test and verification of SAR satellite payload, a ground test and verification system of SAR satellite is proposed. Based on the completion of the test system scheme design, the design and implementation methods of echo simulator and quick-look system are presented. The echo simulator achieves echo generation in real time by a one-dimensional frequency domain algorithm, and the quick-look system uses GPU to obtain high computational performance. The article proposes a SAR imaging algorithm for TOPS mode. The system has been used to complete the ground test of several SAR satellites in China. The results show that the system can meet the ground test verification requirements of SAR satellite, and the ground test results match well with the on-orbit test results. The application of the system guarantees the successful launch and stable operation of the SAR satellite. Keywords: SAR · Test verification · Echo simulator · Quick-look system

1 Introduction As a kind of active microwave imaging remote sensor, Synthetic Aperture Radar (SAR) achieves high range resolution by transmitting LFM signal and pulse compression technology, and the azimuth high resolution by synthetic aperture technology. Since the first SAR satellite SEASAT was launched by the United States in 1978, satellite-based SAR has gradually become a research hotspot in the field of earth observation because of its ability to achieve all-day, all-weather and wide-area observation. Many countries have successively deployed their own SAR satellite and achieved the renewal of them, and SAR satellites have played an important role in national economic construction and national security. SAR satellites play an important role in national economic construction and national security. With the gradual improvement of SAR satellite performance, ground test verification is particularly important to ensure the quality of SAR satellites. The traditional ground test verification method of SAR satellites is the open-loop test method based on signal characterization, which can realize the evaluation of channel performance through internal calibration data analysis. The traditional method is insufficient to verify the cooperative working capability of different subsystems of the whole satellite. In © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 430–436, 2023. https://doi.org/10.1007/978-981-19-9968-0_52

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this paper, a SAR satellite closed-loop test verification system based on on-orbit state equivalent simulation is designed to solve the difficult problem of ground equivalent verification through the simulation of on-orbit state. At the same time, a SAR payload closed-loop imaging link is established through echo simulation and quick-look system to realize the quantitative evaluation of SAR imaging quality and verify the effectiveness of ground processing method.

2 System Design The overall scheme of the SAR satellite closed-loop validation system is shown in Fig. 1, which establishes the on-orbit simulation state through the ground support system.

Fig. 1. SAR satellite real-time closed-loop test verification system

(1) The flight simulation system simulates the satellite dynamics through ground equipment, completes the simulation of various sensors and actuators. Then a closed-loop system was established with the on-board control subsystem to simulate the satellite flight status on orbit. The flight simulation system provides real-time information such as attitude and orbit for the real-time echo simulator, and at the same time provides the data transmission antenna control strategy for the data transmission subsystem to realize the tracking of the ground station. (2) The master control system completes the test system management and establishes the Telemetry & Telecontrol link with the satellite. (3) The GPS simulation system realizes the synchronization between the GPS system and the satellite time system through high dynamic navigation signal simulation, and provides the echo simulator with pulse per second and GPS orbit data for echo generation. (4) The power supply simulation system provides dynamic power input for the satellite. (5) The echo simulator receives the transmitted signal from the SAR, modulates the transmitted signal according to the satellite’s flight attitude, orbit information, scene position and scattering characteristics to generate an echo. (6) The data transmission subsystem receives the remote sensing data and completes processing such as demodulation, descrambling, decoding, de-formatting, and decompression. (7) The quick-look system receives the imaging data and completes the processing of auxiliary data analysis, SAR imaging and imaging quality evaluation.

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3 Design of Echo Simulator 3.1 Hardware Design The echo simulator consists of RF subsystem, isolation circuit and echo generation subsystem. The principle of hardware system is shown in Fig. 2.

Fig. 2. Schematic diagram of the echo simulator hardware composition

The RF subsystem realizes the transceiver of RF signal. Firstly, the radar transmitting signal is received and down-converted to the baseband, and then the baseband signal is sent to the echo generation subsystem. At the same time, the echo signal generated by the echo generation component system is received for modulation and up-conversion, and then the simulated signal is radiated by the antenna. The isolation circuit realizes the isolation of RF signal and baseband signal. The echo generation subsystem modulates the received transmitting signal according to the scene characteristics, orbit and attitude information to complete the echo generation, and then sends it to the RF subsystem based on the synchronous trigger signal. 3.2 Software Design The echo simulator software flow is shown in Fig. 3. The echo simulator receives and samples the transmitted signal based on the transmitted pulse signal of SAR subsystem. At the same time, the echo calculation module obtains the scene impact response at each time according to the orbital attitude information and the position and characteristics of the scene. Finally, the scene impulse response and the radar baseband transmitting signal are processed by convolution modulation and up-conversion to obtain the RF echo signal and realize the online real-time generation of echo. One-dimensional frequency domain algorithm is used to calculate the scene impact response, which has good performance in accuracy and efficiency. In the radar beam irradiation area, the scattering points with the same slant range to the antenna phase center will be superimposed together. The imaging geometry of one-dimensional frequency domain algorithm is shown in Fig. 4. The target scene is divided into several equal range rings. The slant range of each scattering point in the same equal range ring is replaced by the central of equal range ring Rm , The number of equal range rings is M . In the the

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echo of the i-th equal range ring is written as        N   2 t − 2Rim c 4π Rn (ta ) sri (t, ta ) = exp jπ kr t − 2Rim c σn rect exp −j Tp λ n=1

(1) where σn is backscatter coefficient, t is the range time, ta is azimuth time, N is the number of scattering points in the i-th equal range, Rim is the central slant range of the i-th equal range ring, c is the speed of light, Tp is the pulse width, λ is the wavelength and kr is the chirp modulation rate.

Echo Signal

Fig. 3. Flow chart of echo simulation

Fig. 4. The diagram of equal range ring

The equivalent backscatter coefficient of i-th equal range ring is described as σi =

N  n=1



4π Rim σn exp −j λ

 (2)

Then the echo signal of target scene is written as      M   2 t − 2Rim c 2R im sr (t, ta ) = σi rect exp jπ kr t − c Tp i=1



t = rect Tp



M

 

2 exp jπ kr t ⊗ σi δ t − 2Rim c

(3)

i=1

where ⊗ represents the convolution, In digital signal processing, the fast convolution can be performed by FFT.

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4 Design of Quick-Look System 4.1 Hardware Design The hardware composition of the quick-look system is shown in Fig. 5, including the raw data record server, GPU processor, graphics workstation. Each server and workstation are interconnected through the gigabit network. The raw data record server receives the raw data in real time, and distributes the raw data to the GPU processor according to the azimuth framing rule. GPU processor completes BAQ decompression and real-time imaging. The graphics workstation completes the real-time quantification of image data and the display of image data and auxiliary data. Then the auxiliary data interpretation and image quality evaluation were completed by the system.

Fig. 5. The hardware composition of the quick-look system

4.2 Software Design The composition of the quick-look system software is shown in Fig. 6, which is mainly composed of data receiving software, imaging processing software, display software and centralized control software. The data receiving software realizes the receiving, recording and preprocessing of echo data, and then transmits the echo data to the GPU for parallel processing in real time. The imaging processing software runs in the GPU to complete the echo data decompression and focused imaging. The parallel processing design is used to accelerate the operation. The display software shows the imaging results and auxiliary data, and can realize the evaluation of SAR imaging quality. The centralized control software completes the state monitoring of each server node of the quick-look system and the dynamic allocation of computing resources between nodes. It also can provide user interface to realize the unified configuration of the quick-look system.

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Quick-look System

Data Receiving Software

Imaging Processing Software

Display Software

Centralized Control Software

Cluster Management

Equipment Monitoring

Mission Control

Image Quality Evaluation

Read-time Display

Parallel Acceleration

Imaging Processing

Decompression

Data Forwarding

Data Pre-processing

Data Recording

Data Receiving

Fig. 6. The composition of the quick-look system software

5 Application The system has been applied to the ground test of several SAR satellites in China and has achieved excellent application performance. The application of the system ensures the successful launch and stable operation of the satellite. Figure 7 shows the imaging results of the scene target and the point target. The evaluation of the imaging quality of the point target shows that the test results meet the design requirements. The successful application of the system not only effectively guarantees the quality of SAR satellite development, but also provides an important data validation basis for the construction of satellite ground application system.

Fig. 7. The imaging results of the scene target and the point target

6 Conclusion In this paper, a real-time closed-loop test and verification system for spaceborne synthetic aperture radar is designed and implemented. Based on the overall scheme design of the system, the hardware construction method and software design process are described for echo simulator and quick-look system. The system has achieved good application effect in the process of satellite development, which can realize the closed-loop verification of SAR satellite imaging mission. The system also provide important technical support for satellite on-orbit application.

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References 1. Cumming, I.G., Wong, F.H.: Digital processing of synthetic aperture radar data: algorithms and implementation. Boston, Artech House 1(3), 108–110 (2005) 2. Zhang, S., Chen, J.: A echo simulation algorithm for natural scene. In: 2008 IEEE Radar Conference, pp. 464–468. IEEE (2008) 3. Tao, M., Zhang, X., Dong, F.: High efficiency integrated test processing technology and system for high resolution SAR satellite. In: 2021 China International SAR Symposium, pp. 58–62. IEEE (2021) 4. Gao, L., Yang, J.: A pre-processing and storage unit of an on-board space-borne SAR Quicklook system. In: 2009 IET International Radar Conference. IET, pp. 89–92 (2009) 5. Yao, X., Zhang, F., Sun, X.: Comparison of distributed GPU computing frameworks for SAR raw data simulation. In: 2017 IEEE International Geoscience and Remote Sensing Symposium, pp. 2144–2147. IEEE (2017)

An Optimized Scheme for Telecommand Transmission Liu Bo(B) , Weining Hao, Hongguang Wang, and Weisong Jia Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected]

Abstract. The communication satellite based on PCM telecommand system needs to be transmitted multiple telecommands continuously before the solar sail deploys to complete the gain gear setting of solid-state power amplifier or traveling-wave tube amplifier. After a telecommand is sent by the ground, it is judged by telemetry and the next telecommand is automatically sent after the judgment is correct. This is the scheme adopted in the past. The whole transmission process has a long cycle, which has a certain impact on the execution of subsequent tasks. To solve this problem, this paper proposes an optimized scheme for telecommand transmission. It can shorten the transmission cycle of telecommand and provide more time margin for subsequent tasks. The scheme proposed in this paper has been applied in many communication satellites. It can provide a reference for the future satellites using PCM telecommand system. Keywords: PCM telecommand · Telecommand transmission · Optimized scheme

1 Introduction The solar sail is an important part of the satellite energy system. The successful deployment of the solar sail is the key to the success of the satellite launch [1]. Before the north and south solar sails deploy, the –Z direction telemetry antenna will be connected first. It includes setting the gain gear of solid-state power amplifier or traveling-wave tube amplifier, and the setting command needs to be transmitted several times continuously. The traditional scheme is to judge the correctness of telecommand through telemetry after each telecommand is transmitted and transmit the next telecommand after the judgment is completed then. It takes a long time to transmit the whole set of gain gear setting telecommands in this way. As the telecommand sequence on the launch day, especially the operation telecommands for the solar sail deployment are strict to the time. Therefore, it will have a certain impact on the solar sail deployment and subsequent operations if the gain gear setting waste too much time. Based on the interface design of telecommand, an optimized scheme for telecommand transmission is proposed in this paper. It can greatly shorten the transmission cycle of the whole set of gain gear setting telecommands and provide more time for the solar sail deployment.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 437–442, 2023. https://doi.org/10.1007/978-981-19-9968-0_53

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2 Design of Telecommand Interface Telecommand and telemetry are the basic means of satellite operation control and monitoring on the ground after satellite entering orbit [2]. At present, the telecommand systems used by satellites in orbit and under research mainly include PCM telecommand system [3] and CCSDS layered telecommand system [4]. PCM telecommand is a traditional telecommand system, which has been applied in spacecraft engineering in China for decades. It is a very mature technology [5]. The transponder demodulates the phase of the uplink telecommand signal and provides telecommand PSK signals and ranging baseband signals output. After the signals are sent to the demodulation decoder, they are selected and identified by the input signal identification control circuit as telecommand signals, and sent to the sub carrier frequency demodulator. After demodulating the telecommand PSK signals, PCM signal and bit synchronization signal are generated and sent to the decoder. After all demodulation and decoding are completed, it is sent to the data processor to generate execution pulses and send them to relevant equipment [6]. In the communication satellite based on PCM telecommand system, the main equipments for receiving and processing the telecommands are TCU(Telecommand Unit) and CTU(Central Terminal Unit). After receiving the telecommands, TCU will transmit them to CTU. The telecommands are transmitted to the corresponding users after them pass validity verification of CTU. Between TCU and CTU, the process of transmitting and receiving each telecommand is as follows: 1) The beginning of each telecommand is a “door opening command”. The “door opening command” ends with passwords. After that, the PCM data stream and bit synchronization signals are transmitted. 2) When transmitting the center of last bit of the password, TCU transmits a start level to CTU. 3) CTU starts to receive telecommand data when it receives the start level. The starting segment of the data is “telecommand frame information setting”, and CTU judges the end time of the telecommand according to the content of it. 4) CTU transmits an end pulse to TCU after the end time of the telecommand (The pulse width from the end pulse to the rising edge of start level should be greater than 2.5 s). 5) TCU closes output gate of the PCM data stream and bit synchronization signal after receiving the end pulse. CTU receives telecommand data by 16 bits. The significant bit of telecommand data is an integer multiple of 16 bits. The PCM stream transmitted by TCU is in phase with the bit synchronization. CTU inverts the bit synchronization and shifts it on the rising edge of the bit synchronization. The data transmission sequence is high-order transmission first and low-order transmission later. Timing sequence diagram of this interface is shown in Fig. 1.

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Fig. 1. Timing sequence diagram between TCU and CTU

Fig. 2. Structure diagram of CTU receiving PCM data

2.1 Hardware Design The structure diagram of CTU receiving PCM data is shown in Fig. 2. The bit synchronization connects to CLK1 of a D-type flip-flop U1 after it passes through an inverter. The start level connects to D1 of U1. After passing through an AND gate, the output Q1 of U1 and the bit synchronization are used as CLK2 of a synchronous presettable 4-Bit counter. The presetting value of counter is “0000”and increases 1 on the rising edge of the CLK2. After the count reaches 15, the TC output changes from low level to high level. After the TC passes through an inverter, it connects to CLK3 of D-type flip-flop U3. The Q3* of U3 outputs low-level telecommand interrupt to CPU. Passing through the same inverter, the TC output also connects to CLK4 of a hexadecimal D-type flip-flop U5 composed of 2 octal D-type flip-flops. The PCM serial data is converted into 16-bit parallel data through a 16-bit shift register composed of two 8-bit shift registers. The parallel data is latched on the rising edge of CLK4. After receiving the telecommand interrupt, CPU reads the 16-bit data.

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Fig. 3. Structure diagram of CTU transmitting end pulse

Structure diagram of CTU transmitting end pulse is shown in Fig. 3. The address A16-A14 is served as address inputs of a 3–8 decoder U6. When A16A15A14 are equal to “100”, Y4 outputs low level. Y4 connects to CLK5 of D-type flip-flop U7. As the end pulse, Q7 of U7 outputs high level at this time and Q7* outputs low level. Since Q7* has been outputting high level before, the voltage of the capacitor C is high. After Q7* becomes low, C discharges through resistance R. After RC time, the voltage of the capacitor becomes low and CLR of U7 is enabled. U7 is reset and Q7 goes low. Therefore, the pulse width of Q7 outputting high level is 100 ± 20 ms. 2.2 Software Design CTU adopts the interrupt receiving method for telecommand data. An interrupt is generated and transmitted to CPU when every 16-bit data is received. The interrupt is valid at low level. When software reads the telecommand data by word, the interrupt flag is automatically cleared. After receiving the data completely, CTU calculates the time from the rising edge of the start level to completion of the data reception. If this process is greater than 2.5 s, the end pulse will be transmitted. Otherwise, software will transmit the end pulse after 2.5 s.

3 Traditional Scheme for Telecommand Transmission The telecommands on the launch day are arranged in advance. The prepared telecommand sequences are transmitted in chronological order by ground with the time of satellite-rocket separation as T0. Every time a telecommand is injected, CTU will increase “telecommand injection count” by one after receiving the telecommand data completely. And CTU will calculate the time from the rising edge of the start level to completion of the data reception. According to the telecommand format, the gain gear setting telecommand consists of three parts: main header, source data and checksum. After being split, the telecommand length is 26 bytes in total. The bit synchronization

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rate between TCU and CTU is 1000 bps. So, it will take 0.208 s for CTU to receive this telecommand from TCU. Because the receiving time is less than 2.5 s, CTU times another 2.5 s and transmits the end pulse after 2.708 s, and increases the “end pulse count” by one. The “telecommand injection count” and the “end pulse count” are transmitted to ground through telemetry. The ground will automatically transmit the next telecommand after monitoring that the “telecommand injection count” and the “end pulse count” are both increased by one. The “telecommand injection count” and the “end pulse count” are transmitted through telemetry every 8 s. In other words, the maximum time from transmitting a telecommand to receiving the updated telemetry on the ground is 2.708 + 8 = 10.708 s, about 11 s. The whole process of transmitting 7 to 10 telecommands will take about 80–110 s. The telecommand sequence on the launch day is very compact. The gain gear setting telecommands are followed closely by relevant telecommands of the solar sail deployment. The execution interval between the two sets of telecommands does not exceed 120 s. If the transmitting cycle of the gain gear setting telecommand is too long, normal transmission of the solar sail deployment telecommands will be affected. It brings certain danger to the smooth implementation of the task and the safety of the whole satellite.

4 Optimized Scheme for Telecommand Transmission According to the interface design of the telecommand, after the data is received completely, if CPU receives a telecommand interrupt again, it will go to the interrupt processing program to continue to receive the new telecommand data before the end pulse is transmitted. Therefore, the scheme for transmitting telecommand is optimized in order to shorten cycle of transmitting the whole set of gain gear setting telecommands. After optimization, the gain gear setting telecommand is set to be transmitted once every second. The telemetry judgment is not carried out until all telecommands are transmitted. The “telecommand injection count” will be increased by one when a telecommand is received by CTU completely. So, the “telecommand injection count” should be equal to the count of telecommands transmitted by ground. Due to the short transmitting interval, CTU has received an interrupt request of the next telecommand before the end pulse of previous telecommand has been transmitted. The end pulse is not sent out until all telecommands are correctly received. So, the “end pulse count” is only increased once. The whole process of transmitting 7–10 telecommands by the optimized scheme does not exceed 20 s. The following table compares the two telecommand transmission schemes, as shown in Table 1. Through comparison, the optimized scheme proposed in this paper is significantly better than the traditional scheme, and the telecommand transmission efficiency is improved by more than 70%.

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L. Bo et. al. Table 1. Comparison of two telecommand transmission schemes

Telecommand transmission scheme

Telecommand transmission interval

Judgment method of Total time for 7–10 telecommand telecommands correctness transmission

Traditional scheme

8s

Judging after each transmission of telecommand

80–110 s

Optimized scheme

1s

Judging after transmission of all telecommands

20 s

5 Conclusion Based on hardware and software design of telecommand interface, an optimized scheme of telecommand transmission is proposed in this paper. Compared with the traditional scheme, the optimized scheme can greatly shorten the transmitting time of the whole set of telecommands which need to be transmitted continuously many times. This plan has been applied in many communication satellites and achieved good results.

References 1. Winston, P.D.: Parallel power extraction technique for maximizing the output of solar PV array. Sol. Energy 213(3), 102–117 (2021) 2. He, X., Zhang, M.: Application method of telecommand and telemetry packet utilization standard in spacecraft. Spacecraft Eng. 21(3), 54–60 (2012) 3. Commission of Science, Technology and Industry for National Defense. GJB 1198.1A 2004 satellite telemetry and telecommand and data system—PCM telecommanding. Beijing: Commission of Science, Technology and Industry for National Defense (2004). (in Chinese) 4. Commission of Science, Technology and Industry for National Defense. GJB 1198.7A 2004 satellite telemetry and telecommand and data system—packet telecommanding. Beijing: Commission of Science, Technology and Industry for National Defense (2004). (in Chinese) 5. Tan, W., Gu, Y.: Space data system. China Science and Technology Press, Beijing (2008). (in Chinese) 6. Cai, Y., Ma, W., Li, H., Wang, J.: Interference research of telecommand to communication signal. Foreign Electron. Measur. Technol. 34(10), 45–53 (2015)

Transmission Efficiency Improvement of Ka-Band VCM Satellite-to-Ground Data Transmission System of LEO Remote Sensing Satellite Based on DVB-S2X Zhongguo Wang(B)

and Dabao Wang

Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. Based on the X-band variable coding and modulation (VCM) verified by Gaofen-7 satellite in orbit and Chinese satellite-to-ground Ka-band data transmission technology of constant coding and modulation (CCM), and taking the VCM transmission efficiency factor index combined with the effective data rate and link availability as the comparison object, the transmission efficiency of Ka-band VCM satellite-to-ground data transmission system based on DVB-S2X and DVB-S2 is analyzed quantitatively. For Ka-band VCM satellite-to-ground transmission system based on DVB-S2X, the simulation results show that the transmission efficiency is improved by more than 16% and up to 26% compared with DVB-S2, because 256APSK and other high-order modulation modes which are added in DVB-S2X can provide higher spectral efficiency, and they are used in a large proportion of time. This can provide a new and effective solution to the problem of massive data balance caused by the continuous increase of remote sensing data. Keywords: LEO remote sensing satellite · Satellite-to-ground transmission efficiency · Variable coding and Modulation · Ka-band · DVB-S2X

1 Introduction Early LEO remote sensing satellites usually use CCM scheme to transmit data from satellite to ground in the X-band (8025–8400 MHz) with a bandwidth of 375 MHz. They do not take advantage of the favorable conditions of reducing free space loss with the increase of elevation, and result in a certain waste of link resources. Chinese Gaofen7 satellite adopts VCM satellite-to-ground transmission system based on DVB-S2 [1] in X-band, which improves the transmission efficiency by 20% on average only by utilizing the free space loss change [2]. With the continuous improvement of the image resolution of low-Earth-orbit (LEO) remote sensing satellite, the transmission ability of satellite-to-ground transmission system is required to be higher and higher [2]. The Ka-band (25.5 − 27 GHz) allocated © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 443–451, 2023. https://doi.org/10.1007/978-981-19-9968-0_54

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by International Telecommunication Union (ITU) to LEO remote sensing satellite has a bandwidth of 1.5 GHz, which is four times that of X-band. It can support greater downlink data rate, and has realized CCM transmission of effective data rate of 1.25 Gbit/s for single channel and 5 Gbit/s for four Channels [3]. Considering that the radio frequency (RF) signal at Ka-band is much more affected by the weather than that in X-band, if the changes of both atmospheric attenuation and free space loss with elevation are considered at the same time in the Ka-band VCM satellite-to-ground transmission system, greater transmission efficiency can be obtained in theory. Besides the four modulation constellations of DVB-S2 (32APSK, 16APSK, 8PSK and QPSK), DVB-S2X [4] also provides three higher-order modulation constellations with higher spectral efficiency (64APSK, 128APSK and 256APSK). If they are applied to Ka-band VCM satellite-to-ground transmission system, it is expected that the transmission efficiency should be further improved. Therefore, this paper studies the application efficiency of VCM technology at Ka-band LEO remote sensing satellites, and focuses on the quantitative comparative analysis of the transmission efficiency improvement caused by the new higher-order modulation and coding mode of DVB-S2X compared with DVB-S2.

2 Simulation Parameters 2.1 Link Parameters In order to fully cover the distribution characteristics of different elevation of each ground station, this paper takes the orbit of Gaofen-7 satellite [5] as an example. Table 1. System parameters of satellite-to-ground transmission link at Ka-band Parameter

Value

Orbit

Sun-synchronous regressive circular orbit

Satellite altitude

505.984 km

Orbit inclination

97.421°

Carrier frequency

26.65 GHz

Roll-off factor

0.35

Polarization

Circular

Symbol rate

500 Msys/s

Equivalent isotopically radiated power (EIRP)

48 dBW

Loss of pointing error and polarization mismatch

1.5 dB

Ground station G/T

40.0 dB/K

System margin

3.0 dB

The parameters of satellite-to-ground transmission link shown in Table 1 are selected. The simulation duration is 59 days (corresponding to an orbital regression cycle). During this period, the satellite runs 897 cycles. The number of data transmission arcs of

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Beijing station and Sanya station is 240 and 189 respectively, and the duration of data transmission arcs is 1742.82 min and 1354.12 min respectively. 2.2 Modulation and Coding Mode Selection DVB-S2 supports 11 kinds of channel coding modes (coding rate from 1/4 to 9/10), and 4 kinds of modulation constellations (32APSK, 16APSK, 8PSK and QPSK), and 28 kinds of modulation and coding modes are formed through combination. Ideally, the span of symbol signal-to-noise ratio E S /N 0 demodulation threshold can reach up to 18.4 dB (from −2.35 dB to 16.05 dB). For Ka-band VCM system with channel data rate up to Gbps, it is more suitable to select normal forward error correction (FEC) frame whose length is 64800 bits recommended by the standard. For normal FEC frames, DVB-S2X adds 38 kinds of modulation and coding modes such as 64APSK 7/9, 128APSK 3/4 Table 2. Characteristic of modulation and coding mode combination MODCOD

Canonical mode name

System spectral efficiency (bit/s/Hz)

Demodulation threshold E S /N 0 (dB)

Link availability% Beijing

Sanya

1

QPSK 1/4

0.490243

−1.05

99.41

94.46

3

QPSK 2/5

0.789412

0.70

99.28

93.85

4

QPSK 1/2

0.988858

1.70

99.18

93.48

5

QPSK 3/5

1.188304

2.93

99.04

92.97

7

QPSK 3/4

1.487473

4.53

98.91

92.24

9

QPSK 5/6

1.654663

5.68

98.83

91.66

12

8PSK 3/5

1.779991

6.70

98.75

91.11

14

8PSK 3/4

2.228124

8.81

98.56

89.83

15

8PSK 5/6

2.478562

10.20

98.41

88.95

19

16APSK 3/4

2.966728

12.48

98.13

87.31

20

16APSK 4/5

3.165623

13.56

97.98

86.44

22

16APSK 8/9

3.523143

16.13

97.53

84.07

25

32APSK 4/5

3.951571

18.36

97.05

81.64

27

32APSK 8/9

4.397854

21.25

96.15

77.28

190

64APSK 7/9

4.603122

22.72

95.55

74.35

198

64APSK 5/6

4.936639

23.95

94.97

71.58

200

128APSK 3/4

5.163248

26.18

93.28

63.65

202

128APSK 7/9

5.355556

27.63

91.80

56.50

214

256APSK 3/4

5.900855

29.17

89.69

0.00

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and 256APSK 3/4. Compared with the original modulation and coding modes of DVBS2, DVB-S2X can realize finer step adjustment of E S /N 0 demodulation threshold and improve spectral efficiency through higher-order modulation constellations. To avoid switching of modulation and coding mode frequently and reduce the complexity of both simulation analysis and system implementation appropriately, comprehensively considering the demodulation loss and the amplifier power output back-off [6, 7], and only retaining the combination with increasing spectral efficiency and the difference of demodulation threshold between adjacent modes should not be less than 1 dB, 19 kinds of modulation and coding modes are selected. The corresponding set of mode code identifier (MODCOD) is {1 3 4 5 7 9 12 14 15 19 20 22 25 27 190 198 200 202 214}. Among them, the first 14 MODCODs belong to DVB-S2 and the last 5 MODCODs belong to DVB-S2X. From Table 2 it can be seen that the span of E S /N 0 demodulation threshold in this combination can reach up to 30 dB, which could better adapt to Ka-band satellite-to-ground transmission link.

3 Simulation and Evaluation 3.1 Definition of Transmission Efficiency Factor For a ground station of LEO remote sensing satellites, the transmission efficiency factor (TEF) could be defined as “the product of the link availability and the average effective data rate”, which clarifies the weighted average effect of link availability on the effective data rate [8]. The VCM TEF could be defined as below [8].  N  tend (i) R (t)dt b i=1 tstart (i) (1) E = AL · N i=1 [tend (i) − tstart (i)] In Eq. (1), AL is the link availability, N is s the number of data transmission arcs in an orbital regression cycle, tstart (i) is the start time but tend (i) is the end time of the i-th data transmission arc, and Rb (t) is the effective data rate at time t (Mbit/s) [8]. In engineering realization and simulation analysis, the integral in Eq. (1) should change to piecewise cumulative summation. 3.2 Link Availability Based on the ITU P Series recommendations [9–11], the atmospheric attenuation parameters including gaseous attenuation, rain attenuation, scintillation and cloud attenuation are simulated and computed. Combined with the parameters in Chapter 2, the link availability of Beijing station with moderate rainfall and Sanya station with abundant rainfall under the condition of receiving at the lowest elevation of 5° is shown in Table 2. It can be seen that: (1) The link availability of Beijing station is good. Even if the highest-order modulation mode 256APSK is adopted, the link availability can reach nearly 90%. (2) The link availability of Sanya station is relatively bad. Even if the lowest-order modulation mode QPSK is adopted, the link availability can only reach 91%.

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3.3 Simulation Results of Transmission Efficiency Factor For one specific link availability, the atmospheric attenuation and free space loss corresponding to the minimum 5° elevation are the largest, and the minimum MODCOD with the minimum requirement for E S /N 0 demodulation threshold can be selected. As the elevation increases, the receiving SNR increases gradually, and the MODCOD could gradually switch to a larger one to obtain greater spectral efficiency, which can improve the satellite-to-ground transmission efficiency. Considering the dynamic channel attenuation characteristics and the amount of simulation calculation, the sampling interval in the transmission process is set as 1 s. Table 3 shows the quantitative analysis results. The results for initial minimum MODCOD of 27 (DVB-S2) and 214 (DVB-S2X) are not given in the table, because there is only one MODCOD in the VCM system at this time (always used from the minimum elevation of 5°), and it will degenerate to CCM system. In order to show the change rule of VCM TEF more intuitively, the VCM TEF in Table 3 is drawn on a diagram, as shown in Fig. 1. With the increase of the initial minimum MODCOD, it can be seen from Table 3 and Fig. 1 that: Table 3. VCM TEF (Mbit/s) Initial minimum MODCOD

Canonical mode name

Beijing

Sanya

DVB-S2

DVB-S2X

DVB-S2

DVB-S2X

1

QPSK 1/4

1762.69

2054.90

1708.40

2017.30

3

QPSK 2/5

1813.74

2125.45

1740.25

2061.74

4

QPSK 1/2

1841.00

2164.35

1755.99

2084.63

5

QPSK 3/5

1871.69

2209.84

1773.46

2111.41

7

QPSK 3/4

1909.63

2264.80

1794.02

2144.08

9

QPSK 5/6

1934.54

2301.95

1805.65

2164.56

12

8PSK 3/5

1953.97

2332.78

1814.49

2181.20

14

8PSK 3/4

1996.40

2400.02

1828.61

2211.90

15

8PSK 5/6

2019.60

2440.35

1831.96

2226.58

19

16APSK 3/4

2055.47

2506.28

1832.71

2246.00

20

16APSK 4/5

2062.37

2528.23

1822.33

2244.55

22

16APSK 8/9

2086.58

2590.41

1798.51

2241.62

25

32APSK 4/5

2090.13

2629.69

1761.66

2222.48

27

32APSK 8/9

−

2683.71

−

2161.93

190

64APSK 7/9

−

2697.31

−

2102.52

198

64APSK 5/6

−

2693.97

−

2035.52

200

128APSK 3/4

−

2683.81

−

1831.62

202

128APSK 7/9

−

2658.53

−

1637.41

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Z. Wang and D. Wang

1) For Beijing station, the VCM TEF based on DVB-S2 increases monotonically, but the VCM TEF based on DVB-S2X increases first and then decreases, and reaches maximum when the initial minimum MODCOD is 190. 2) For Sanya station, the VCM TEF based on DVB-S2X and DVB-S2 increase first and then decrease, and reach maximum when the initial minimum MODCOD is 19. If the design criterion of maximizing transmission efficiency is followed, this method can be used to find the best working point. 2800 Beijing (DVB-S2) Sanya (DVB-S2) Beijing (DVB-S2X) Sanya (DVB-S2X)

VCM TEF/(Mbit/s)

2600

2400

2200

2000

1800

1600

1

3

4

5

7

9

12

14 15 19 20 22 Minimum MODCOD

25

27

190 198 200 202

Fig. 1. VCM transmission efficiency factor

3.4 Transmission Efficiency Improvement When the initial minimum MODCOD is the same, the increasing percentage values of VCM TEF based on DVB-S2X compared with VCM TEF based on DVB-S2 are shown in Table 4. It can be seen that: (1) For any ground station, with the increase of the initial minimum MODCOD, the percentage of transmission efficiency improvement increases. (2) For any initial minimum MODCOD, the percentage of transmission efficiency improvement of Sanya station is slightly greater than that of Beijing station. (3) Since the MODCODs with higher spectral efficiency are used, the transmission efficiency adopting DVB-S2X can be improved by no less than 16.58%. Taking the initial minimum MODCOD of 1 as an example, the usage time percentage of different MODCOD in a regression cycle is shown in Fig. 2. It can be seen that whether in DVB-S2 system or DVB-S2X system:

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(1) For a specific ground station, the usage time percentage of any MODCOD less than 27 (13 kinds in total) is the same, and the difference is only in the part of MODCOD 27, 190, 198, 200, 202 and 214. It is because that the initial elevations of any of the first 13 MODCODs are the same in both systems. (2) The usage time percentage of the maximum MODCOD is the largest, and is much bigger than that of other MODCOD. It is because that the impact of atmospheric attenuation is not large in most cases, and this part of margin can be used to improve the transmission efficiency. (3) Compared with Beijing station, only the usage time percentage of the largest MODCOD for Sanya station is larger, while the usage time percentage of other MODCODs is smaller. It is because that Sanya station with abundant rainfall is more vulnerable to atmospheric influence, but its atmospheric attenuation changes faster when the elevation increases.

Table 4. Transmission efficiency improvement proportion (DVB-S2X vs DVB-S2) (%) Initial minimum Canonical mode Beijing Sanya MODCOD name 1

QPSK 1/4

16.58

18.08

3

QPSK 2/5

17.19

18.47

4

QPSK 1/2

17.56

18.71

5

QPSK 3/5

18.07

19.06

7

QPSK 3/4

18.60

19.51

9

QPSK 5/6

18.99

19.88

12

8PSK 3/5

19.39

20.21

14

8PSK 3/4

20.22

20.96

15

8PSK 5/6

20.83

21.54

19

16APSK 3/4

21.93

22.55

20

16APSK 4/5

22.59

23.17

22

16APSK 8/9

24.15

24.64

25

32APSK 4/5

25.81

26.16

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Z. Wang and D. Wang 60 Beijing (DVB-S2) Beijing (DVB-S2X) Sanya (DVB-S2) Sanya (DVB-S2X)

Usage time percentage/%

50 40 30 20 10 0

1

4

3

5

7

9

12

14

15 19 20 MODCOD

22

25

27

190 198 200 202 214

Fig. 2. Usage time percentage of any MODCOD in VCM system

4 Conclusions According to the massive data transmission requirements of LEO remote sensing satellites, using the modulation coding mode combination recommended by DVB-S2X and DVB-S2 standards, the transmission efficiency analysis of Ka-band VCM satellite-toground transmission system is carried out, mainly focusing on the research and simulation analysis of the transmission efficiency improvement caused by the new higher-order modulation coding mode of DVB-S2X compared with DVB-S2. Through the simulation analysis of 19 kinds of modulation and coding modes with increasing spectral efficiency and the difference of E S /N 0 demodulation threshold between adjacent modes not less than 1 dB, it is shown that compared with the VCM satellite-to-ground transmission system based on DVB-S2, the transmission efficiency of the VCM satellite-to-ground transmission system based on DVB-S2X is improved by no less than 16%, and the improvement effect is remarkable. For the follow-up project, the DVB-S2X standard can be introduced into the high-resolution LEO remote sensing satellite, and the on-board design and ground station upgrading can be carried out simultaneously, so as to support the satellite-to-ground transmission of remote sensing image data of a larger area and further improve the application value of the satellite.

References 1. ETSI EN 302 307–1 V1.4.1 Digital Video Broadcasting (DVB); Second generation framing structure, channel coding and modulation systems for broadcasting, interactive service, news gathering and other broadcast satellite applications, Part1: DVB-S2. ETSI, Sophia-Antipolis (2014) 2. Zhang, S., Cao, H., Zhang, X., et al.: Research on application of variable coding modulation system based on digital video broadcasting - satellite - 2nd generation in data transmission of near-earth remote sensing satellite. Int. J. Satell. Commun. Network. 38, 437–449 (2020)

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3. Lu, F., Jiang, Y., Wang, R., et al.: System demonstrations of Ka-band 5-Gbit/s data transmission for satellite applications. Int. J. Satell. Commun. Network. 40, 204–217 (2021) 4. ETSI EN 302 307–2 V1.3.1 Digital Video Broadcasting (DVB); Second generation framing structure, channel coding and modulation systems for broadcasting, interactive service, news gathering and other broadcast satellite applications, Part2: DVB-S2 Extensions (DVB-S2X). ETSI, Sophia-Antipolis (2021) 5. Cao, H., Zhang, X., Zhao, C., et al.: System design and key technologies of the GF-7 satellite. Chin. Space Sci. Technol. 40(5), 1–9 (2020) 6. CCSDS 130.12-G-1 CCSDS protocols over DVB-S2—summary of definition, implementation, and performance. CCSDS, Washington D.C. (2016) 7. CCSDS 131.31-O-1 CCSDS space link protocols over ETSI DVB-S2X standard. CCSDS, Washington D.C. (2021) 8. Wang, Z., Lu, F., Wang, D., et al.: A transmission efficiency evaluation method of adaptive coding modulation for Ka-band data-transmission of LEO EO satellites. Sensors 22, 5423 (2022) 9. ITU-R P.618–13 Propagation data and prediction methods required for the design of earthspace telecommunication systems. ITU, Geneva (2017) 10. ITU-R P.676–12 Attenuation by atmospheric gases and related effects. ITU, Geneva (2019) 11. ITU-R P.840–8 Attenuation due to clouds and fog. ITU, Geneva (2019)

Anti-interference Control for On-Orbit Servicing Spacecraft Formation Under Communication Resource Limitation Tao Wang1,2 , Yingchun Zhang2 , Heli Gao1(B) , Jie liu1 , and Hongchen Jiao1 1 China Academy of Space Technology, Beijing 100094, China

[email protected] 2 School of Astronautics, Harbin Institute of Technology, Harbin 150001, China

Abstract. Aiming at formation configuration keeping control problem between on-orbit servicing spacecraft and runaway spacecraft, this paper establishes a formation system model under communication resource limitation and uncertainty brought by complex formation system. An event-triggered control strategy which is based on extended state observer (ESO) is adopted to diminish the connection between the sensor and the controller after the design of ESO and analysis of event triggering mechanism. The structure of the event-triggered adaptive controller grounded on extended state observer is shown. The firmness of the closed-loop system is testified by Lyapunov function. The results of simulation tests show that proposed control method designed in this paper can realize the six degree-of-freedom (6-DOF) control for on-orbit servicing spacecraft formation under communication resource limitation, and the introduction of ESO can effectively improve the system’s capability to suppress external interference. The time interval of event-triggered indicating that the event triggering mechanism designed can avoid Zeno phenomenon. Keywords: Extended State Observer (ESO) · Event-triggered on-orbit servicing · Spacecraft formation · Six-degree-of-freedom control (6-DOF)

1 Introduction In recent years, in view of the great strategic, commercial and technical potential of on-orbit maintenance, on-orbit servicing spacecraft system has received great attention by various countries [1]. At the same time, Networked Control System represented by event-triggered control has been paid more and more attention. Some scholars have applied event trigger control to spacecraft attitude control for the limited communication resources case [2-5]. To effectively diminish the communication burden of the system, resulting from complex task of the formation system and a large amount of interaction information among formation members, and guarantee the performance of control system, many scholars have carried out control research on formation system based on event trigger [6-8]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 452–459, 2023. https://doi.org/10.1007/978-981-19-9968-0_55

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Therefore, in recent years, more scholars have dedicated on integrated modeling and control method for attitude and orbit coupling [9, 10]. At the same time, uncertain factors such as model error and external disturbance put forward higher requirements on anti-interference capability of formation system. Some scholars improved the antiinterference capability by the observer [11, 12]. Focusing on the multiple on-orbit servicing spacecrafts and runaway spacecraft formation system under communication resource limitation, the paper introduces a kind of event-triggered adaptive control method based on ESO which is integrated the ESO, event trigger and LQR control. The proposed method implements the anti-interference control with 6-DOF for on-orbit servicing spacecraft. Simulation results demonstrate the effectiveness of the proposed method.

2 System Model Considering a close system formed by two servicing spacecrafts and another runaway,  T  T let the state variable be q η = q1 · · · q12 · · · η1 · · · η12 and the control variable be T  uc = Fu1 Fu2 τ u1 τ u2 , in which q1 and q2 are the radius vector from the formation centroid to the servicing spacecraft centroid respectively, q3 is the angle between q1 and q2 ; q4 , q5 and q6 are the orientation variable of the formation configuration in the orbital coordinate system, q7 , q8 and q9 are attitude angles of the servicing spacecraft 1, q10 , q11 and q12 are attitude angles of servicing spacecraft 2; η1,2,3 is the gradient of q1,2,3 , η4,5,6 is the angular velocity of formation coordinate system against to inertial system, η7,8,9 is attitude angular velocity of spacecraft 1, η10,11,12 is the attitude angular velocity of spacecraft 2; Fu1,2 is the control force of servicing spacecraft 1 and 2, τ u1,2 is the control torque of servicing spacecraft 1 and 2. The dynamic model of the system is established referring [9] and [10] and let q, η be the nominal state, uc be the nominal control quantity, and the deviation of the nominal state is respectively defined as δq, δη, δuc , then



q = q + δq, η = η + δη, uc = uc + δuc

(1)

   δq δ q˙ + B(q, η, uc )δuc = A(q, η, uc ) δη δ η˙

(2)

3 Extended State Observer Design Considering the established 6-DOF dynamic model of multi-spacecraft formation as a first-order system and taking the linearized Eq. (2) as a known part of the model, this system can be represented as  x˙ = A(q, η, uc )x + Ad x + [B + Bd ]ρuc + d (3) y=x

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T  Here, A(q, η, uc ) and B = 012×12 B(q, η, uc ) are the a known part of the model, Ad and Bd are the unknown part of the model, d is the external unknown disturbance, and ρ is the health indicator of the actuators with 0 ≤ ρi ≤ 1. Define the unknown disturbance as a new state d = Ad x + Bd ρuc + d + B(ρ − I)uc with which the Eq. (3) can be rewritten as:  x˙ = A(q, η, uc )x + Buc + d y=x

(4)

(5)

The control matrix is B = [ 012×12 B12×12 ]T24×12 , combining with Eqs. (14) and (15). It can be seen that the control variable does not have a direct effect on the state q, but is related to the state variable η. To tackle the non-matching control problem, an extended state observer for the state variable η is established, and the variable q is directly feedback without estimation. Based on Eqs. (3) and (4), the local second-order extended state observer is designed as follows ⎧ e = z1 − x1η ⎪ ⎪ ⎪ ⎨ z˙ 1 = dˆ + Buc − β01 fal(e, κ1 , δ) (6) ⎪ ⎪ ⎪ ⎩ ˆ˙ d = −β02 fal(e, κ2 , δ) Here, e is the observation error of extended state observer on system state. x1η is the ˆ are the system output, in which z1 is the estimation of the state system input. z1 and d(t) ˆ variable η and d(t) is the prediction of uncertain state vector d(t). β01 and β02 is the ESO coefficient to be set. f (e, κi , δ) is a linear and continuous power function near the origin, defined as  e/δ 1−κi , e ≤ δ (7) fal(e, κi , δ) = κ i e sgne , e > δ in which 0 < κi < 1, δ > 0, and κi = 1/2i . The real-time control strategy after introducing the extended state observer is −1 ˆ

uc (t) = KX(t) − B

d(t)

(8)

Here, K is the derived feedback control matrix based on LQR.

4 Event-Triggered Adaptive Control Base on ESO To tackle the communication between sensor and controller, an event-triggered control strategy is adopted, and the following event-triggered conditions are designed e(t) = x(tk ) − x(t) ||e(t)|| ≤ σ1 ||x(t)|| + σ2

(9)

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Here, the σ1 > 0 and σ2 > 0 are the coefficients of event-triggered condition. Considering the error term brought by the event trigger mechanism, formula (8) is rewritten as follows u(t) = δu(t) − B δu(t) = Kx(tk )

−1 ˆ

d(tk ) − us (t), t ∈ [tk , tk+1 )

(10)

Here, us (t) = −a × sign(BT Px(tk )) (a > 0) is the compensation term. Thus, formula (5) can be expressed as ˆ +d x˙ = A(q, η, uc )x + BKx(t) + BKex (t) + ed (t) − Bαsign(BT Px(tk )) − d(t) (11) ˆ ) − d(t). ˆ in which ed (t) = d(t k The structure of event-triggered adaptive controller grounded on extended state observer is given in Fig. 1.

Fig. 1. Configuration of Event-Triggered Adaptive Control based on ESO

Theorem 1: For the system (5), control strategy (9), and event trigger mechanism (10), if σ1 , σ2 , and α satisfy conditions as follow, and with β > 0, the closed loop system is stable. σ1 ≤ λmin (Q) α=

σ2 +d +β 2

(12) (13)

Proof: Considering a Lyapunov function as follow V(t) = xT (t)Px(t)

(14)

The derivative of V(t) can be expressed as ˙ V(t) = −xT (t)Qx(t) − δuT (t)Rδu(t) + 2xT (t)PBKex (t) +2xT (t)BPed (t) − 2xT (t)BPαsign(BT Px(tk )) ˆ + 2xT (t)Pd −2xT (t)P d(t)

(15)

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ˆ − d) = 0, Eq. (14) can be transformed into As lim (d(t) t→∞

˙ V(t) ≤ −xT (t)Qx(t) + σ1 ||x(t)|| + σ2 + 2xT (t)BPed (t)+ −2xT (t)PBαsign(BT Px(tk ))

(16)

Setting s(t) = BT Px(t), for ||s(t) − s(tk )|| = ||BT P||||e(t)|| ≤ σ1 ||x(t)|| + σ2 =

(17)

Thus, the domains can be demonstrated as follow Domain1 : {x(t)|sj (t) < 0, sj (tk ) > 0, sj (t) > sj (tk ) − } Domain2 : {x(t)|sj (t) > 0, sj (tk ) < 0, sj (t) < sj (tk ) + }

(18)

with which it can be concluded that  = {x(t)|||sj (t)|2 + |sj (tk )|2 ≤ }

(19)

As sign(sj (t)) = sign(sj (tk )) is established outside the , the derivative of V(t) can be represented as ˙ V(t) ≤ −xT (t)Qx(t) + σ1 ||x(t)|| + σ2 + 2d ||s(t)|| − 2α||s(t)||

(20)

Further, based on Eqs. (12) and (13), it can be got that ˙ V(t) ≤ −β||s(t)||

(21)

˙ It is clear that V(t) < 0, for s(t) outside the . Therefore, the system state will get into  in a limited time, and the closed-loop system is bounded and stable.

5 Simulation and Analysis Assuming that the masses of servicing spacecraft 1 and 2 are 300 kg, the three-axis main inertia is 100 kgm2 , and the mass of the malfunctioning spacecraft is 2000 kg. The three spacecrafts form an isosceles triangle in which the runaway spacecraft is located as the vertex. The nominal state of the desired configuration is: q1 = 20m, q2 = 50m, q3 = 120◦ , q4 = 0◦ , q5 = 0◦ , q6 = −30◦ and q7,8,9,10,11,12 = 0◦ .Set the initial deviation of each item as: q1 = 2m, q2 = −2m, q3 = −3◦ , q4,5,6 = 0, q7 = 5◦ , q8 = −4◦ , q9 = −3◦ , q10 = −5◦ , q11 = −4◦ , q12 = 3◦ , η1 = 0.05m/s, η2 = −0.05m/s, η3 = −0.05◦ /s, η4,5,6 = 0, η7 = −0.05◦ /s, η8 = −0.04◦ /s, η9 = 0.04◦ /s, η10 = −0.03◦ /s, η11 = 0.04◦ /s η12 = −0.04◦ /s, and ρi = 0.9 + 0.05 sin(t).

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Set the process noise and the controller parameters as follows: ⎧     ⎪ Fd = 2 2 2 × 10−2 sin(nt)N + 1 1 1 × 10−2 cos(nt) N ⎪ ⎨ 1     Fd2 = 2 2 −2 × 10−2 sin(nt)N + 1 1 −1 × 10−2 cos(nt) N ⎪ ⎪    ⎩ d  F3 = 2 −2 2 × 10−2 sin(nt) N + 1 −1 −1 × 10−2 cos(nt) N  d     τ 1 = 2 −2 2 × 10−4 sin(nt) N · m + 1 −1 1 × 10−4 cos(nt) N · m     τ d2 = 2 2 −2 × 10−4 sin(nt) N · m + 1 1 −1 × 10−4 cos(nt) N · m Q = diag[80, 80, 80, 10, 10, 10, 80, 80, 80, 80, 80, 80, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] R = diag[50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50] κ1 = 0.5, κ2 = 0.25, β01 = 3000, β02 = 30000 δ = 1 × 10−6 , ||e(t)|| ≤ 0.02||x(t)|| + 0.001. The results of simulation tests are illustrated in Figs. 2, 3, 4, and 5. The interference can be well suppressed and the proposed algorithm is effective. After introducing ESO, the proposed method can overcome and compensate the external interference better, with higher control precision and faster convergence time.

Fig. 2. Relative distance curve

Fig. 3. Relative configuration angle curve

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Fig. 4. Azimuth change curve

Fig. 5. Relative attitude angle of spacecraft 1 and 2

Figure 6 shows that any two event triggering time intervals are greater than 0, indicating that the designed event triggering method can eliminate Zeno phenomenon and is effective.

Fig. 6. Time interval of event-triggered

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6 Conclusion An ESO is designed forced on the 6-DOF control challenge of on-orbit servicing spacecraft formation with limited communication resources, and achieves the estimation of unknown disturbances effectively. An event-triggered adaptive controller is designed grounded on the estimation of state and disturbance. Finally, the validity of the proposed method is testified by simulation data. The controller with ESO can achieve better performance.

References 1. Flores-Abad, A., Ma, O., Pham, K., Ulrich, S.: A review of space robotics technologies for on-orbit servicing. Prog. Aerosp. Sci. 68, 1–26 (2014) 2. Xu, S., Wei, Z., Huang, Z., Wen, H., Jin, D.: Fuzzy-logic-based robust attitude control of networked spacecraft via event-triggered mechanism. IEEE Trans. Aerosp. Electron. Syst. 57(1), 206–226 (2020) 3. Wu, B., Shen, Q., Cao, X.: Event-triggered attitude control of spacecraft. Adv. Space Res. 61(3), 927–934 (2018) 4. Liu, Y., Jiang, B., Lu, J., Cao, J., Lu, G.: Event-triggered sliding mode control for attitude stabilization of a rigid spacecraft. IEEE Trans. Systems, Man, Cybernetics: Syst. 50(9), 3290– 3299 (2018) 5. Zhang, C., Wang, J., Zhang, D., Shao, X.: Learning observer based and event-triggered control to spacecraft against actuator faults. Aerosp. Sci. Technol. 78, 522–530 (2018) 6. Su, B., Wang, H., Wang, Y., Gao, J.: Fixed-time formation of AUVs with disturbance via event-triggered control. Int. J. Control Autom. Syst. 19(4), 1505–1518 (2021) 7. Weng, S., Yue, D.: Distributed event-triggered cooperative attitude control of multiple rigid bodies with leader-follower architecture. Int. J. Syst. Sci. 47(3), 631–643 (2016) 8. Qi, J.R., Liao, H., Xu, Y.F.: Event-triggered attitude-tracking control for a cableless noncontact close-proximity formation satellite 9, 138 (2022) 9. Huang, H., Zhu, Y.W., Yang, L.P., Zhang, Y.W.: Stability and shape analysis of relative equilibrium for three-spacecraft electromagnetic formation. Acta Astronaut. 94(1), 116–131 (2014) 10. Huang, H., Yang, L., Zhu, Y., Zhang, Y.: Dynamics and relative equilibrium of spacecraft formation with non-contacting internal forces. Proceedings of the Institution of Mechanical Engineers, Part G: J.of Aerospace Eng. 228(7), 1171–1182 (2014) 11. Chen, G.J., Chang, L., Yang, X.B., Yang, C.L., Li, Y.B.: Anti-disturbance and fault-tolerant control of dual-satellite formation configuration maintenance. Opt. Precis. Eng. 29(3), 605– 615 (2021) 12. Zhang, J., Biggs, J.D., Ye, D., Sun, Z.: Extended-state-observer-based event-triggered orbitattitude tracking for low-thrust spacecraft. IEEE Trans. Aerosp. Electron. Syst. 56(4), 2872– 2883 (2019)

FPGA-Based High-Speed Remote Sensing Satellite Image Data Transmission System Yifeng He(B) , Peng Sun, Qian Liu, Hao Zhang, and Dayang Zhao Xi’an Institute of Space Radio Technology, Xi’an 710100, China [email protected]

Abstract. At present, with the increasing number of remote sensing satellite systems established in China, a large amount of remote sensing satellite image data has been obtained. Based on FPGA, this paper studies the transmission of highspeed remote sensing satellite image data, studies the image characteristics, image preprocessing methods and image detection principles of remote sensing satellite images, and analyzes various methods of image preprocessing. For different targets and different image backgrounds, the specific processing algorithms are also different. Only by flexibly using the basic theory of image processing can a good image processing effect be achieved. Among them, the fast connected region labeling algorithm designed and implemented can realize the merging of equivalent pairs while scanning. The results show that the system can reliably implement the algorithm in this paper, complete the processing of image data, and meet the requirements of the project. Keywords: FPGA · High-speed remote sensing · Satellite imagery · Data transmission

1 Introduction Today, the world, remote sensing technology is evolving. With the rapid development of science and technology in recent years, the aerospace remote sensing technology is not only applied to the military industry, and it can be seen in the civil industry [1, 2]. These satellite remote sensing data products are widely used in agriculture, forestry resource surveys, crop estimation, environmental monitoring, geological filling, surveying, coal, oil and gas, etc. And high-voltage line lines, reservoir site selection and reservoir area ecological environment dynamic monitoring; fire-free fire zone dynamic monitoring, desert to find water, and renewal of national resource environment databases and establishment of road traffic databases. There are many research on FPGA and telling remote sensing image transmission, where Yasumura proposes a high-speed PMSM drive system (DTC) implemented in FPGA [3]. The running feature of the recommended system using the PMSM with a rated speed of 42,000 min-1 is displayed. Nascimento proposes hardware / software implementation in on-site programmable gate arrays on a single system on which to compress sensing [4]. The architecture operates compressed sensitive algorithm on a © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 460–470, 2023. https://doi.org/10.1007/978-981-19-9968-0_56

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single compression / infrared imaging spectrometer sensor image within 0.35 s, which has 512 lines, 614 samples, and 224 bands. Since the remote sensing satellite image has a large number of noise, complex marine background and cloud layers such as the interference such as threshold-based detection methods, there is a high default. And the detection algorithm based on the trailing also has high algorithm, and is limited. Based on the analysis of the target detection results of the marine remote sensing satellite image ship, this paper improves the existing classic ship target detection algorithm based on a specific image background and ship target characteristics, and verifies the entire algorithm through computer universal software. Finally, FPGA is completed. Sliced system design. The image characteristics of the remote sensing satellite image have been studied, and the hardware and software system solution based on FPGA-based target detection algorithm is proposed. Tested the FPGAbased chip system. The results indicate that the system can be more reliable to implement the algorithm to complete the processing of image data.

2 Design and Research of High-Speed Remote Sensing Satellite Image Data Transmission System Based on Fpga 2.1 Image Features Whether it is an optical remote sensing satellite image or a radar satellite image, the features of objects in the image can be summarized into several aspects such as gray level, shape, texture, size, etc. [5, 6]. (1) Grayscale The grayscale of the image reflects the reflection intensity of the satellite imaging device on the ground objects. Strong reflections from objects in the image appear brighter on the image, while weak reflections appear darker on the image. The magnitude of the reflection intensity depends on factors such as the side view angle, direction, and orientation of the satellite imaging device. For example, calm seas appear as dark areas on a grayscale image, while clouds and ships appear as bright areas. (2) Shape Shape refers to the natural form that an object exhibits. If the target object in the image can show its unique morphological features, it is helpful to identify it on the image. For example, rivers on land appear as slender strips; land and sea meet as irregular edges; harbors appear as jagged; for medium-resolution images, ships in the ocean appear as elongated rectangles and many more. (3) Texture At present, there is no formal and unified definition of image texture. It is usually understood as an image feature that reflects the spatial distribution of pixel grayscale properties, which is generally expressed as a local irregularity but a macroscopically regular feature. Remote sensing satellite image textures are generally classified into macro, medium and subtle.

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(4) Dimensions Size is generally defined as the expression of characteristics such as length, width and area of an object. The size of the target is closely related to the system resolution of remote sensing satellites. For example, for a medium-high-resolution image, the upper area of the target, that is, the number of pixels, will be larger, and its shape and other characteristics will be obvious. For low-resolution images, the same target appears to be smaller in size, and may be composed of only one point or several points, making it difficult to distinguish the target. 2.2 The Principle of Ship Target Detection In the background of various sea conditions, how to find an effective algorithm to identify and extract ship targets has always been the goal that people pursue. By analyzing the numerous achievements of ship target detection in the marine background of optical satellite images and SAR images, it can be found that the ship target detection of marine satellite images has a similar process, which can be roughly divided into the following three stages: (1) Image Preprocessing Stage. The purpose of image preprocessing is to eliminate the interference of various factors in the image. While improving the image quality, it also makes the ship target in the image have a higher contrast or more obvious texture features compared with the background, which provides a good basis for the subsequent ship target detection good conditions. Commonly used preprocessing methods include morphological processing, filtering processing, and sharpening processing. (2) Ship Target Segmentation Stage. Segmenting the ship and the background is the core link to achieve successful ship target detection. At present, there are two main segmentation methods: one is texture segmentation; the other is threshold segmentation [7, 8]. The texture segmentation method is based on the texture features of the ship, and adopts the feature calculation based on the operator to achieve the segmentation of the ship target. Threshold segmentation is essentially a threshold value, and selecting an appropriate threshold value to separate the target and the background is region segmentation. (3) Feature-based Ship Target Extraction Stage. At this stage, it depends on the statistics of the characteristic parameters of the ship target, such as the shape and size of the ship, as well as the ship’s wake, heading and other parameters are important reference elements for ship extraction. In addition, some scholars have realized the identification and extraction of ship targets based on the spatial coordinate parameter characteristics of military ship formations. Relying on the geometric features and ground features of ships in remote sensing satellite images, the research on ship detection methods in marine remote sensing satellite images by domestic and foreign scholars mainly develops into two directions: one is to directly segment and detect ship targets based on ship features. The second method is to indirectly detect the ship target by detecting the wake of the ship.

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2.3 Selection Principle of Characteristic Parameters It is very important for the detection algorithm of this paper to extract the ship target through the feature parameters. The proper selection of the feature parameters directly determines the quality of the image target identification effect. At present, researchers have the following consensus on the principle of feature parameter selection: (1) Robustness, that is, the feature parameters should be insensitive to noise in the image; (2) Separability, that is, the characteristic parameters can show the fundamental difference between the ship target and the false alarm target; (3) Independence, that is, the characteristic parameters should avoid the repetition of the characteristics of the ship target and the false alarm target, and there is no correlation between them; (4) Simplicity, that is, the characteristic parameters adopted should make the whole detection algorithm simple and efficient as much as possible, so that the detection algorithm has better performance indicators. 2.4 Remote Sensing Image Compression Algorithm (1) One-dimensional DCT algorithm The DCT of a one-dimensional sequence of length N {x(n): n = 0, l,…, N−l} is defined as:  N −1 2  2kπ (2n + 1) ] k = 0, 1, · · · , N − 1 (1) x(n) · cos[ X (k) = α(k) N 4N n=0

The one-dimensional inverse DcT transform (IDcT) is:  x(n) =

N −1 2  2kπ (2n + 1) x(n) · cos[ ] k = 0, 1, · · · , N − 1 N 4N

(2)

n=0

Among them, α(k) is the orthogonalization factor, which is introduced to ensure √ the canonical orthogonality of the transform basis α(k) = 1/ 2, when k = 0, otherwise α(k) = 1. (2) Two-dimensional DCT algorithm The 2-dimensional DCT method used in practice, because the image space is 2dimensional, is the same as the starting point of the one-dimensional transformation, and finding the DcT orthogonal transformation matrix is the main direction. Defining the 2D-DcT of two-dimensional image data {x(n, m): n = O, 1,…, N-I; m = 0, l,…, M-l} is defined as:  X (k, l) = α(k) · α(l)

 N −1 M −1  2   kπ (2n + 1) lπ (2m + 1) x(x, m) · cos[ ] · cos[ ] MN 2N 2M n=0 m=0

Among them, seven = o’1…, IV one l, = 0, 1,…, M−l;

(3)

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The two-dimensional IDcT is:  x(n, m) =

 N −1 M −1  lπ (2m + 1) kπ (2n + 1) 2   ] cos[ ] a(k) · a(l) · X (k, l) · cos[ MN 2N 2M

(4)

n=0 m=0

Among them, the definitions of α(k), α(l)a(1) are the same as those in the one-dimensional DcT transform, n = 0, l…, N−l, M = 0, l…, M−l;

3 Experimental Research on High Speed Remote Sensing Satellite Image Data Transmission System Based on FPGA 3.1 FPGA Design Ideas (1) Pipeline design In FPGA design, pipeline design is the most commonly used and the most important design idea. Pipeline design reflects the biggest difference between software design and hardware design. In software design, data flow is generally processed in a serial manner, while in hardware development, data flow is generally pipelined according to the rhythm of the clock, which is also the embodiment of parallelism. Through the design of the assembly line, each part can perform its own duties and work without interruption, which greatly improves the work efficiency and processing speed to a certain extent. (2) Ping-pong operation Ping-pong operation is a commonly used method in FPGA development. The main idea is to use the intermediate cache module to complete the alternate processing of data, in a sense, it can also be regarded as another form of pipeline technology. The characteristics of this method of ping-pong operation are: through the mutual switching and storage between “data buffer module 1” and “data buffer module 2”, seamless pipeline processing of data flow can be realized, which is an important technology to realize pipeline structure and can effectively Improve system throughput. (3) Serial-to-parallel conversion As a commonly used method for FPGA, it reflects the exchange of area and speed. According to the data volume and timing requirements, it is implemented by simple registers or dual-port RAM. In the case of a small amount of data, the serial-to-parallel conversion can be completed through a register. In the case of a large and complex data amount, a state machine and a dual-port RAM can be used to complete the serial-to-parallel conversion. If there are no special requirements, when designing serial-to-parallel conversion, attention should be paid to the synchronous design of timing.

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3.2 FPGA Engineering Development Process The FPGA development process refers to the process in which users use EDA software and programming tools to develop FPGA chips. The main development steps of satellite image compression system include: circuit design, design input, function simulation, chip programming and debugging. (1) Circuit design: Select the most reasonable design scheme and chip model mainly by weighing the system indicators, working speed, available resources, cost and other issues. (2) Design input: The input and output interface of the system has been introduced in the third chapter. The software development part of the image compression system uses Verilog HDL language. (3) Function simulation: As a logical function verification link without time delay, each function of the control module and the data scheduling module in the compression system must be implemented in the function simulation module. In this topic, functional simulation mainly completes the following tasks: whether the system can output the correct control signal waveform according to the established state machine program; whether the system can realize the color space conversion of the source image data in the FPGA; In the case of feedback (excitation and feedback actions are provided by Testbench), jump to the correct state and output the response waveform. (4) Chip programming and debugging: Use the chipscope tool that comes with the ISE software to sample the pin levels in the chip with trigger conditions. The key nodes that need to be verified are: after the program is downloaded to the FPGA chip and the initialization waveform is output to the ADV212 chip, whether the interrupt signal irq_n of each ADV212 chip can be pulled low, and whether the firmware ID (0x0000FF82) can be read immediately; whether the compression chip is in The read and write request signal appears at the correct time, whether the code stream block end flag signal lcode can be pulled high; whether the 4-way chip can work normally according to the designed process. 3.3 Cloud Detection Technology in Remote Sensing Cloud Images Due to the existence of cloud areas, the utilization of remote sensing satellite images is greatly reduced. Cloud areas occupy a very important position in remote sensing images, and the detection and classification technologies for cloud areas are constantly updated and iterative. Cloud detection technology is the premise of subsequent remote sensing cloud image processing, and its detection accuracy has a huge impact on the subsequent image processing performance, and has become the primary problem to be solved in remote sensing cloud image processing.

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Currently commonly used cloud detection technologies can be divided into many categories: (1) Cloud detection algorithm based on image spectral feature threshold, which is widely used in cloud detection algorithms. The same pixel has different spectral characteristics, and the core idea of this method is to rely on this characteristic for cloud detection. In the earliest period, there were often problems of fewer spectral bands and wider spectral bands, which often affected the effect of this cloud detection method, such as the ICLAVR method. (2) Cloud detection algorithm based on the spatial characteristics and texture characteristics of cloud areas, this method is also a kind of threshold method, and its main judgment basis is the spatial characteristics of cloud areas. For remote sensing cloud images, texture features have great reference value and significance. With the development of remote sensing satellite image performance indicators, the spatial resolution has also been greatly improved. The spatial characteristics and texture characteristics of remote sensing cloud images It plays an important role in cloud detection technology.

4 Experiment Analysis of High Speed Remote Sensing Satellite Image Data Transmission System Based on FPGA 4.1 Usage of Device Resources According to the device resource usage report compiled according to the results of the SYR file of the ISE comprehensive report, there are 301,140 flip-flops, 150,720 lookup tables, and 416 36Kb block RAMs in the Virtex-6 chip. The resource usage corresponding to the three hardware designs of the simulator side, the receiver side without the stripe noise removal module, and the receiver side with the stripe noise removal module is summarized, as shown in Table 1: Table 1. Device resource usage report

Trigger Lookup table Block RAM/FIFO

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141

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20%

34%

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29693 34%

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25000 20533

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18002 15795

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13026 1357116%

20% 15%

13% 10000 10%

9% 5000

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4% 67

5%

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87

0

0% Simulator

Receiver (without denoising)

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Usage/unit

Usage/unit

Usage/unit

Usage/%

Utilization/%

Usage/%

Fig. 1. Device resource usage report

From the results in Fig. 1, it can be seen that whether the logic resources represented by flip-flops and look-up tables or the storage resources represented by block RAM, the resource consumption of the simulator is the least. Even if the receiver with the stripe noise removal module is added, the usage of flip-flops, look-up tables and block RAM resources is only 6%, 19% and 34% respectively. The idle logic resources and storage resources on the chip are abundant, which can meet the future needs. The need to add more image processing modules such as cloud detection and elimination, object recognition, etc. 4.2 Comparison of Different Lossless Compression Algorithms for Binary Cloud Mask Table 2 shows the compression ratio of the binary cloud mask corresponding to the remote sensing cloud image using different lossless compression algorithms. These include CCITT-G3, CCITT-G4, JBIG1, RAR, ZIP and this program. Details as follows:

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Sample serial number

The compression ratio of each algorithm CCITT-G3

CCITT-G4

JBIG1

This program

RAR

ZIP

1

81.3

162.5

227.6

119.1

75.9

72.1

2

63.6

115.1

182.9

78.2

51.0

52.2

3

83.9

170.7

238.1

120.5

74.8

74.7

4

131.3

276.8

269.5

200.8

144.2

5

61.7

108.9

126.4

64.4

50.2

49.5

6

56.6

92.3

115.1

56.6

43.2

43.0

7

89.0

170.7

189.6

108.9

80.0

75.9

8

100.4

256

292.6

165.1

129.6

112.5

9

101.4

249.8

301.2

165.1

133.0

115.0

10

94.8

193.2

204.8

150.6

101.4

96.6

11

73.7

131.3

146.3

83.3

62.4

60.6

12

59.2

100.4

117.7

59.2

46.3

46.1

Average

83.1

169.0

201.0

114.3

82.7

77.2

350

300

250

200

150

100

50

0

CCITT-G3

CCITT-G4

JBIG1

This program

RAR

ZIP

Fig. 2. The binary cloud mask is compressed by different algorithms

128

FPGA-Based High-Speed Remote Sensing Satellite Image Data

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It can be seen from Fig. 2 that the compression ratio of this scheme after compression is at the middle level of all schemes, the implementation is simple, and the performance is better.

5 Conclusions With the rapid development of remote sensing and aerospace, the limited transmission bandwidth of satellite-to-ground images has been unable to meet the needs of the rapidly growing amount of remote sensing image data, and there is a huge contradiction between the two. There are a large number of cloud areas in remote sensing images, and the cloud areas have little or no available information. In the process of image download, they occupy a considerable part of the satellite-to-ground transmission bandwidth and data storage space, which will lead to effective data bandwidth. Utilization is greatly reduced. In view of the above problems, this paper designs a set of remote sensing cloud image compression scheme, and implements high-speed design and implementation in FPGA, which can effectively improve the compression performance of remote sensing cloud image and effectively improve the bandwidth utilization of satellite-to-ground transmission. This paper completes the FPGA high-speed design and implementation of the overall scheme, realizes the fast connected area marking algorithm, calls the off-chip high-speed dynamic data memory based on DDR4, integrates the ping-pong operation and serial-to-parallel conversion technology, and designs and implements the read-write control and arbitration module. Control the timing relationship of the interface, effectively relieve the pressure of storage, ensure the normal operation of the solution, and improve the system work efficiency.

References 1. Kim, H.Y., Chung, H.: FPGA implementation of an enhanced method for high-speed alignment marker recognition. The Trans. Korean Institute of Electrical Engineers P 68P(2), 6975 (2019) 2. Tsigkanos, A., Kranitis, N., Theodoropoulos, D., et al.: High-performance COTS FPGA SoC for parallel hyperspectral image compression with CCSDS-123.0-B-1. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 28(11), 23972409 (2020) 3. Yasumura, K., Inoue, Y., Morimoto, S., et al.: Efficiency improvement and torque ripple reduction by high-speed PMSM drive system implemented in FPGA. IEEJ Trans. Electronics, Information Syst. 139(1), 106–112 (2019) 4. Nascimento, J., Vestias, M.P., Martin, G.: Hyperspectral compressive sensing with a systemon-chip FPGA. IEEE J.of Selected Topics in Applied Earth Observations and Remote Sensing 13, 37013710 (2020) 5. Asif, M., Guo, X., Hu, A., et al.: An FPGA-based 1-GHz, 128 x 128 cross-correlator for aperture synthesis imaging. IEEE Trans. Very Large Scale Integration (VLSI) Systems 28(1), 129141 (2019) 6. Fjeldtvedt, J., Orlandic, M., Johansen, T.A.: An efficient real-time FPGA implementation of the CCSDS-123 compression standard for hyperspectral images. IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing 11(10), 38413852 (2018)

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7. Bascones, D., Gonzalez, C., Mozos, D.: An extremely pipelined fpga implementation of a lossy hyperspectral image compression algorithm. IEEE Trans. Geoscience and Remote Sensing 58(10), 74357447 (2020) 8. Cochrane, C.J., Cooper, K.B., Durden, S.L., et al.: An FPGA-based signal processor for FMCW doppler radar and spectroscopy. IEEE Trans. Geoscience and Remote Sensing 58(8), 55525563 (2020)

Application of Frequency Domain Filtering in Edge Detection Bo Li1,2(B) and Hongyan He1,2 1 Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China

[email protected] 2 Key Laboratory for Advanced Optical Remote Sensing Technology of Beijing,

Beijing 100094, China

Abstract. Image edge extraction is a widely studied problem in the field of digital image processing. When performing edge extraction on noisy images, in order to avoid that the spatial domain filtering cannot preserve the edge information to the greatest extent while suppressing the noise, the frequency domain bandstop filter is used instead of the spatial domain low-pass filter to preprocess the image. In this way, part of the high-frequency information generated by the edge is preserved, and the accuracy of the detection result is improved. Based on standard frequency domain band-stop filter, a special filter is constructed for the edge in a specific direction to further improve the edge detection quality in this direction. In addition, in the edge extraction algorithm, the double-threshold Otsu adaptive threshold detection algorithm is integrated into the Canny algorithm in the step of edge selection. Experiments show that frequency domain filtering can better detect edges and is more convenient to design specific filters for specific problems. Keywords: Frequency domain · Band stop filter · Edge extraction · Otsu

1 Introduction In the field of digital image processing, image edge extraction is a very important part. For an image, the edge contains most of the information. At present, the definition of image edge considers that the edge is the part of the image where the grayscale changes violently. Generally, it can be divided into step type, roof type and linear edge [1]. In edge extraction algorithms, most of the common algorithms are based on the spatial domain. Classic operators such as Robert operator, Prewitt operator, Sobel operator, etc., use the difference to replace the derivative, and extract the edge by convolving the operator template with the image [2]. This type of algorithm is very sensitive to noise. In order to suppress the influence of noise on the edge, low-pass filtering is generally performed on the image before the edge is extracted. For example, the Canny operator will use Gaussian low-pass filter to weaken the noise [3]. In the selection of filtering, the spatial filtering algorithm is simple and the processing speed is fast, but it is difficult to design templates with special functions. Compared with spatial filtering, frequency domain filtering has a more intuitive physical meaning. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 471–477, 2023. https://doi.org/10.1007/978-981-19-9968-0_57

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frequency domain filter filters out the specific grayscale change frequency, ensuring that other grayscale changes are basically unchanged [2], which is convenient for designing frequency domain for specific problems, and the denoising result is smoother than spatial domain filter. It is also easy to combine with spatial image processing at the same time [4, 5]. This paper conducts experiments on the role of frequency domain filtering in edge extraction, and obtains better edge extraction effect than spatial domain filtering. In addition, a special frequency domain filtering is constructed to detect edges in specific directions.

2 Method Principle 2.1 Image Frequency Domain The frequency and spatial domain of the image are the representation of the image in different reference frames. The high frequency and low frequency parts correspond to the areas where the image grayscale changes sharply and slowly, respectively. Spectrogram is a graph obtained by arranging the amplitudes of the signal components of different amplitudes and frequencies of the image according to different frequencies. In Fourier spectrogram, the brightness and darkness of a point reflects the number of pixels contained in the frequency corresponding to the point on the image. It also represents the size of the corresponding power spectrum of the image [2]. The spectrogram of a digital image can be obtained by Fourier transform:  |F(l, k)| = R(l, k)2 + I (l, k)2 (1) Among them, R(l, k)2 and I (l, k)2 are the real and imaginary parts of the twodimensional image after Fourier transform. For most images, as the frequency goes from low to high, the energy distribution increases from large to small, and the spectrogram takes the spectral component corresponding to the zeroth harmonic as the center (DC component), which is symmetrically distributed over the center. The DC component reflects the average brightness of the image. For the convenience of observation, the origin is usually moved to the center of the image. 2.2 Fourier Transform The Fourier transform realizes the transformation of the image between the spatial domain and the frequency domain. Fourier proposed that a function of any period can be decomposed into an infinite sum of sine waves of different frequencies, that is, a Fourier series [6]. Image is a two-dimensional signal. A two-dimensional function f (x, y) satisfies the square integrability, and its two-dimensional Fourier transform and inverse transform are:  +∞  +∞ f (x, y)e−j2π (ux+vy) dxdy (2) F(u, v) = −∞

f (x, y) =

1 4π 2



−∞

+∞  +∞

−∞

−∞

F(u, v)ej2π(ux+vy) dudv

(3)

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The digital image corresponds to the discrete Fourier transform (DFT) of a twodimensional finite sequence. Its mathematical meaning is to transform aperiodic discrete matrices into a series of periodic discrete matrices, and its physical meaning refers to converting the luminance value distribution of the spatial image into the frequency distribution of the image [7]. Set f (m, n) as the two-dimensional discrete signal of M ×N points, then the two-dimensional DFT is defined as: F(l, k) = f (m, n) =

−1 M −1 N  

m=0 n=0 −1 M −1 N  

1 MN

lm

kn

f (m, n)e−j2π( M + N ) lm

kn

F(l, k)ej2π( M + N )

(4)

(5)

m=0 n=0

The biggest obstacle to the direct application of DFT to digital images is its huge computational complexity. When the length of the one-dimensional discrete sequence is N , the number of complex multiplication operations is N 2 , and the number of complex additions is N (N − 1), which is larger for two-dimensional images [2]. In 1965, Cooley and Tukey proposed a fast Fourier transform (FFT) with successive doublings, which reduced the number of multiplications to N log2 N , which made Fourier transform widely used in digital image processing. 2.3 Canny Operator and Otsu Dual-Threshold Segmentation The Canny edge detection algorithm was proposed by Australian computer scientist John F. Canny [8] in 1986. The Canny algorithm mainly includes four steps: 1) 2) 3) 4)

Convert the image to grayscale and perform Gaussian low-pass filter on it; Calculate gradient using 2 × 2 domain finite difference algorithm; Perform maximum suppression in four directions; Using double threshold to eliminate false edge points and connect the edge.

In the last step of Canny, the Otsu dual-threshold segmentation algorithm can be used to adaptively find two thresholds as high and low thresholds. Otsu algorithm [9] divides the image into foreground and background, and the trough is the segmentation threshold. Otsu algorithm can be extended to double thresholds. Let the proportion of each type be ω1 , ω2 , ω3 , the average gray value of each type is u1 , u2 , u3 , and the total average gray value is u, Then the inter-class variance δ is: δ = ω1 (u1 − u)2 + ω2 (u2 − u)2 + ω3 (u3 − u)2

(6)

When δ is the largest, the corresponding t1 and t2 are the final double thresholds.

3 Experiment and Analysis 3.1 The Effect of Frequency Domain Filtering on Edge Detection The spectrum can intuitively reflect the edge. In Fig. 1, the bright lines in the vertical and horizontal directions are generated by the image boundary. In addition, the bright lines

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perpendicular to each other come from the edges of objects such as roads and houses, and the direction of the bright lines is perpendicular to the edges of the objects. When extracting edge in spatial domain, low-pass filtering is usually performed first to suppress the influence of noise, and then high-frequency information is extracted. These regularized edge detection operators all have band-pass characteristics [10]. Therefore, a band-stop filter can be used to filter the image to be processed. For example, the n-order Butterworth band-stop filter [11] is: H (u, v) =

 1+

1

2n

(7)

D(u,v)W D(u,v)2 −D02

where D(u, v) is the distance from the origin of the frequency coordinate to the point (u, v), D0 is the filter center cutoff frequency, and W is the bandwidth. Butterworth filter has no significant discontinuity between the bandpass and band-stop, so there is no ringing and the processed image is less blurry.

Fig. 1. Image and its spectrogram

In Fig. 2, the image with added noise is subjected to spatial Gaussian low-pass filtering and first-order Butterworth band-stop filtering. The size of the Gaussian lowpass filter is 5×5, and the standard deviation is 2; The Butterworth band-stop filter center cutoff frequency D0 = 200 Hz, bandwidth W = 130 Hz. When the noise suppression degree is equivalent, the boundary information of the Butterworth band-stop filtering results is more clearly preserved. Using Otsu and Canny to extract the edges, and the results are shown in Fig. 3. When the influence of the cloud in the upper left is roughly the same, the extraction result of the image after frequency domain filtering is better, and the main channel is more complete than the result after spatial low-pass filtering (the box in the Fig. 3). 3.2 Frequency Domain Filters for Specific Problems Still based on the 1st-order Butterworth band-stop filter, the polar coordinate system is established with the center of the image as the origin and the positive semi-axis in the

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Fig. 2. Comparison of filtering results (a): Original image; (b): Add noise; (c): Spatial filtering result; (d): Frequency filtering result

Fig. 3. Edge extraction results after filtering (a): Spatial filtering; (b): Frequency domain filtering

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horizontal direction as the polar axis. To detect edges in the image with angles of 44◦ 46◦ and 134◦ 136◦ , a filter can be designed as: ⎧ ⎪ ⎨ 1, 44◦ < θ < 46◦, 134◦ < θ < 136◦ 1 2n , Others  (8) H (u, v) = ⎪ D(u,v)W ⎩ 1+ 2 2 D(u,v) −D0

where θ is the angular coordinate of the line connecting each point in the spectrogram with the origin. The filter waveform is shown in Fig. 4. Compared with the result of conventional Butterworth band-stop filter with the same cutoff frequency, bandwidth, and order, the result of the specially designed filter in the approximate 45° and 135° directions are more complete (the box in the Fig. 5).

Fig. 4. Filter waveform

Fig. 5. Edge extraction results (a): Conventional filter; (b): Specially designed filter

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4 Summary In image edge detection, spectrum analysis can more intuitively describe the highfrequency signal generated by the edge of a specific direction and a specific frequency, and it is more convenient to design an appropriate frequency domain filter to process the image to detect the edge of the image. Frequency domain band-stop filtering retains the low-frequency signal and part of the high-frequency signal of the image at the same time, especially the high-frequency signal generated by the edge. It suppresses the influence of noise in a more targeted manner, and the filtered image is clearer while denoising, and the image information is preserved more abundantly. The method can be applied to the edge extraction of objects such as rivers and complex roads, and the results of different filtering directions can be combined to obtain final edges.

References 1. Duan, R., Li, Q., Li, Y.: A review of image edge detection methods. Optical Technol. 31(3), 415–419 (2005) 2. Wang, F., Wang, Y.: Space Remote Sensing Image Preprocessing Technology. National Defense Industry Press, Beijing (2020) 3. Huang, H., Dong, L., He, J.: Edge detection with improved Canny algorithm under strong noise. Computer Technol. Dev. 31(1), 83–87 (2021) 4. Lu, W., Ma, M.: Based on Edge Point Frequency Domain Spatial Features Combined with Image Fuzzy Area Localization Method. CN110619647A (2019) 5. Yang, C.: Research on Digital Image Enhancement Technology Combined with TimeFrequency Domain. North China University of Technology (2019) 6. Zhang, Y.: The application of fourier transform in digital image processing. J. Langfang Normal Univ.: Natural Science Ed. 15(3), 25–27 (2015) 7. Fan, Y.: Research on histogram equalization image feature recognition algorithm under fourier transform. Computer and Digital Eng. 45(9), 1848–1852 (2017) 8. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986) 9. Fu, Z.: Image threshold selection method - extension of otsu method. Computer Appl. 20(5), 37–39 (2000) 10. Huang, J., Zou, H.: Image edge detection method combining LOG operator and wavelet transform. Comput. Eng. Appl. 45(21), 115–117 (2009) 11. Wang, K., Xiao, P., Feng, X.: Frequency domain realization method of omnidirectional edge detection based on two-dimensional log butterworth filter. J. Surveying and Mapping 42(5), 682–690 (2013)

Research on the Anti-interference of a Satellite Telecommand Interface Shifeng Gao1(B) , Jingang Wang2 , Luyuan Wang1 , Zhenyu Wu2 , and Duanguo Si2 1 Beijing Institute of Spacecraft System Engineering, Beijing 100094, China

[email protected] 2 Institute of Telecommunication and Navigation Satellites, CAST, Beijing 100094, China

Abstract. During the in-orbit operation, on the one hand, the satellite is in the complex external space environment for a long time and faces the interference induced by the space environment; on the other hand, a large number of electronic devices in the small space inside the satellite operate in coordination, and inevitable interfere with each other. These factors may become the interference source of the telecommand interface and induce the abnormal operation of the command load. According to the satellite telecommand operation process, this paper studies the anti-interference capability of various telecommand load interfaces. The circuit characteristics of the user load of pulse command which is easily disturbed are analyzed and tested. It includes relays and optocoupler loads used extensively on satellites. The analysis and test of the anti-interference ability of relay load show that the relay load has a good anti-interference ability, mainly depends on relay type selection, anti-interference level is about 8 V–15 V, anti-interference pulse width is more than 1ms. The anti-interference capability of optocoupler load is closely related to the circuit design, and the anti-interference level is about 4.5 V–10 V, anti-interference pulse width varies with circuit parameters; The anti-interference capability of other loads needs to be analyzed according to the specific circuit. The anti-interference ability of integrated circuit load is relatively weak. Keywords: Satellite · Telecommand interface · Anti-interference

1 Introduction Satellite telecommand interface is used for remote control operation of the satellite from the ground to complete certain functions, including action execution, state setting, parameter adjustment, etc. After the ground sends the telecommand to the satellite, it is received by the TT&C transponder and sent to the on-board data handling subsystem to complete the demodulation and decoding of the telecommand, and output to each telecommand load. In recent years, with the accelerated development of aerospace engineering at home and abroad, the number of launch satellites is increasing year by year. No matter in orbit or on the ground test, there are some abnormal command load misoperation, exposing the weak anti-interference ability of telecommand interface. This paper studies the antiinterference design of various command loads commonly used in satellites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 478–485, 2023. https://doi.org/10.1007/978-981-19-9968-0_58

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2 Description of Satellite Interference During the in-orbit operation, on the one hand, the satellite is in the external complex space environment for a long time and faces the interference induced by the space environment. on the other hand, a large number of electronic devices operate in the small space inside the spacecraft, and inevitable interfere with each other. These factors may become the interference source of the telecommand interface and induce the abnormal operation of the command load. (1) Satellite surface/internal charge and discharge The satellite in high orbit is in the low-energy plasma environment of the earth’s magnetosphere. The interaction between the low-energy plasma and the surface materials of the satellite will cause the surface charge/discharge effect of the satellite, and the outer surface of the satellite may accumulate charge. Due to the different material properties, illumination conditions and geometry of the outer surface of the satellite, potential differences can be generated between the adjacent outer surface of the satellite, between the surface and the deep layer, and between the surface and the satellite ground. When the potential difference rises to a certain value, electrostatic discharge will be generated in corona, arc, breakdown and other ways, and electromagnetic pulses will be radiated to the internal equipment of the satellite through different ways such as physical path or space, affecting the normal operation of the equipment [1, 2]. The difference between internal charge and surface charge is that internal charge is caused by high-energy particles penetrating into the interior the satellite. High-energy particles penetrating deeper than the satellite structure can penetrate into the interior near the damage point and deposit charges, thus generating electrostatic discharge. The electrostatic discharge pulse width generated by discharging on the satellite surface/inside is 10 ns to 100 ns, with narrow pulse width, high voltage(2 kV–20 kV) and high current (peak value 80A). The typical discharge energy is about 6 mJ [3, 4]. (2) Complex electromagnetic environment inside the satellite Satellite is a combination of electronic equipment under strong space constraints. It has a complex electromagnetic environment with dense electronic equipment, wide frequency coverage, diverse functional modes, complex interface technology. There are many electrical interfaces inside the satellite, and the high and low frequency cables are complex. When all transmitters in the RF system work at the same time, interference is easily transmitted through conduction or radiation path, resulting in electromagnetic interference to highly sensitive receivers and sensors. Satellite are generally equipped with various electrical and electronic equipment that can generate signals of different powers and frequencies, such as switch that can generate small spark transient pulses, pulse control signal, high speed digital circuit, the modulation of switch power supply system, and multiple interfaces forms of electromagnetic interference sources. Transient operation of short-term highpower load switch will cause interference, and a large number of secondary power DC/DC modules working at the same time will form common-mode noise interference, which will form different degrees of interference to sensitive weak signals when working. According to the literature and satellite development experience,

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the typical pulse width of the in-satellite electromagnetic interference is about 10 us [5]. Various satellites will use different power systems, such as centralization and distribution, different equipment grounding requirements are different, resulting in the whole satellite grounding system is very complex, easy to cause ground interference. According to the test results of a satellite workshop, the interference level of ground wire sometimes reaches about 1 V–2 V.

3 Satellite Telecommand Operation Process The telecommand operation is initiated by the ground station, and the satellite receives the remote signal and performs the corresponding processing. After the ground station sends the telecommand signal to the satellite, it is received and demodulated by the TT&C transponder of the satellite TT&C node, and the telecommand is extracted and sent to the command processing node. Firstly, the remote control equipment in the command processing node completes the instruction decoding and pulse output, and directly drives the telecommand user equipment to act. Secondly, other injection telecommands are sent to the computer, which is distributed to the user device through the bus interface or other customized interfaces. The software of the telecommand user device will process and identify the corresponding processing, including action execution, state setting, parameter adjustment, etc. So as to realize a complete telecommand operation from the ground to the satellite (Fig. 1). Satellite Command processing node

TT&C node TT&C transponder

Remote control equipment

Injection telecommand

Telecommand Computer

Pulse interfaces

Bus interfaces

Other interfaces

Command user node Pulse command user

Bus command user

Other command user

Ground station

Fig. 1. Satellite telecommand operation process.

4 Anti-interference Analysis of Telecommand Load According to the different circuit interfaces of command processing node and command user node, telecommand interface can be divided into pulse command interface, bus

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command interface and other customized data interface. The anti-interference capability of pulse command interface depends on the design characteristics of interface circuit. The anti-interference ability of bus interface and customized data interface is firstly related to the anti-interference ability of physical layer data bus. Data bus with strong anti-interference ability is usually selected in satellite design, such as 1553B bus, which adopts A/B dual-bus redundancy, transformer coupling and twisted-pair shielded cable design. RS422 bus, using balanced differential signal transmission, twisted-pair shielded cable design. This kind of data bus has strong physical layer anti-interference ability. In addition, data transmission verification is generally designed in the data link layer to detect error codes in data transmission and retransmit, which effectively improves the anti-interference ability of telecommand in transmission process. Therefore, this paper focuses on the anti-interference ability of pulse command interface. According to the different characteristics of pulse command user load circuit, the circuit at the receiving end of a satellite telecommand includes the following: (1) Relay, the usage of this load type is about 80% of the total number of pulse commands; (2) Optocoupler, the usage of this load type is about 20% of the total number of pulse commands; (3) Other loads, such as operational amplifiers, integrated circuits, etc. are rarely used. 4.1 Anti-interference Analysis of Relay Telecommand Load An command load in the form of relay is widely used in a certain satellite. The typical command load circuit is as follows. The command pulse signal is added to the relay wire package to control the action of the contact and realize the on-off of the connected circuit. The countervailing electromotive force uses two diodes in series, and part of the circuit is connected with resistor in series on the relay wire package (Fig. 2).

Relay wire package

Fig. 2. Typical relay command load circuit.

Common types of relays include: 2JB2, 2JB5, 4JB5, TL26P, T26P, JMC, 2JB1, 2JB0.5, 2JT1, TL12P, etc. The following are typical relay operation characteristics and requirements for acting voltage and pulse (Table 1): Relay circuit design is mainly based on relay, other components include resistors, diodes, etc. The variation of the characteristics of each device has little influence on

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S. Gao et al. Table 1. Action characteristics of a typical relay.

Types of relays The normal temperature (20 °C)

Action voltage Action pulse width Coil resistance Action/transition whole temperature (ms) range and after life (1 ± 10%)  voltage (V) test (V)

2JB2

235 (25 °C)

≤ 18 (25 °C)

≤ 24

≥ 100

2JB5

730

≤ 18

≤ 24

≥ 30

4JB5

560

≤ 18

≤ 24

≥ 50

TL26P

2000

≤ 14.2

≤ 18

≥ 20

T26P

1560

≤ 14.2

≤ 18.0

≥ 20

JMC

1000 (25 °C)

8.016.0 (25 °C)

5.223.0

≥ 30

2JB1

2000

9.514.2 (25 °C)

6.318.0

≥8

2JB0.5

3600

≤ 17.0

≤ 23.0

≥ 20

2JT1

1560 (25 °C)

≤ 14.2 (25 °C)

≤ 18.0

/

the anti-interference ability at the end of satellite life. Therefore, within the limit of the number of relay actions, the anti-interference ability of relay circuit at the end of satellite life has little difference from that at the beginning. The test method of anti-interference ability of relay circuit is: apply a constant voltage to the relay circuit with DC power supply, adjust and increase the voltage amplitude until the relay acts, and obtain the maximum anti-interference level; Use the signal source to simulate the interference pulse, and the amplitude is the rated value. Adjust and increase the pulse width until the relay acts to obtain the maximum anti-interference pulse width. The test data of anti-interference ability of typical relay circuits are given below (Table 2). Table 2. The test data of anti-interference ability of typical relay circuits. Types of relays

Maximum anti-interference voltage (V)

Maximum anti-interference pulse width (ms)

2JB2

15.8

≥1

2JB5

1315.8

≥1

TL26P

10.714.5

≥1

T26P

11.512.4

≥1

JMC

13.513.7

≥1

2JB1

8.1

≥1

2JB0.5

8.28.7

≥1

TL12P

13.0

≥1

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According to the above test results, the anti-interference ability of the relay load mainly related to the different types of relay characteristic parameters, and other electrical components such as series resistance in the circuit. According to the measured data of a certain satellite, the anti-interference ability of large relays such as 2JB2 is about 15V, and that of small relays such as 2JB1 is about 8V. The anti-interference ability of other relays is between the two, and the anti-interference pulse width is more than 1ms. Compared with small relays, large relays need higher energy to act, so their anti-interference ability is stronger. 4.2 Anti-interference Analysis of Optocoupler Telecommand Load In a satellite payload equipment, optocoupler command load is often used, which is driven by low voltage or high voltage command. The commonly used optocoupler model is GH302. The anti-interference ability capability of optocoupler load is mainly related to the design of optocoupler circuit. The figure below shows the interface circuit of poweron command of payload equipment, which is a typical interface circuit of optocoupler command load (Fig. 3).

Fig. 3. Typical optocoupler command load circuit.

In the figure above, the input interference signal voltage VI of the command interface can be expressed as: VI = V1 + R2 × (IF +

VF ) + VF + V2 R3

(1)

In the formula: VI is the input interference signal voltage of the command interface; V1 is the diode conduction voltage (usually 0.7 V); IF is the optocoupler input current; R2 and R3 are the resistance value; VF is the optocoupler input voltage; V2 is the voltage of the stabilivolt (usually 5.3 V). According to the transmission characteristic curve of the optocoupler, when the input current IF = 0.5 mA (VF is approximately 1V), the output current ICE is approximately 0mA. Therefore, the maximum interference voltage VI that the load will not misoperate is calculated according to this constraint. According to the analysis and calculation of Formula (1), the anti-interference pulse voltage of the optocoupler circuit is VI =

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10.1 V. (Since the output terminals need a certain current to drive, the calculation of anti-interference voltage using IF = 0.5 mA is conservative.) The optocoupler front circuit is design with RC filter circuit, RC time constant is R2 ×C1 , Assuming that the interference with the same amplitude as the command voltage is used, the time required for the interference voltage to rise to the voltage amplitude of the interference signal as the width of the anti-interference pulse TI , and assuming that the interference waveform is a square wave. Therefore, the calculation result is conservative, and the anti-interference pulse width TI can be expressed as: TI = R2 × C1 × ln(

1 1−

VI VC

)

(2)

In the formula: TI is the anti-interference pulse width; R2 , C1 are the resistance and capacitance value; VI is interference signal voltage of the command interface; VC is the command voltage. According to the analysis and calculation of Formula (3), the anti-interference pulse width of the optocoupler circuit is TI = 2.1 ms. According to the above method, the anti-interference pulse amplitude and width of various optocoupler circuits used in the satellite are analyzed, and the actual test and verification are carried out. The test method is the same as that of relay circuit, the results are shown in the table below (Table 3). Table 3. The anti-interference data of various optocoupler load circuit. Load

Command type

R2 (K)

R3 (K)

C1 (uF)

VI (V)

TI (ms)

Test data (V)

A

High voltage command

5.1

10

1

10.1

2.1

7–10.5

B

5.1

10

0.47

10.1

0.9

7–10.5

C

2.2

1.2

0.1

9.9

0.1

6.9

D

4.7

10

0.18

9.8

1.4

4.2–5.4

E

Low voltage command

1.3

1.2

0.18

8.7

0.3

4.8–5.3

F

1.8

1.2

0.47

9.4

1.3

4.8

According to the above analysis results, the anti-interference ability of optocoupler load is mainly related to the design parameters of optocoupler circuit, so a reasonable circuit should be designed and appropriate parameters should be selected. According to the analysis and measured data of a satellite, the anti-interference capability of the optocoupler command load is usually about 4.5 V–10 V under the premise of appropriate design and parameters. 4.3 Anti-interference Analysis of Other Telecommand Load In a certain satellite, operational amplifier, integrated circuit and other command loads are used in a small amount. The anti-interference ability is related to the load type and

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interface circuit design. Operational amplifier circuit in the form of command load, commonly used operational amplifier models include LM139A, according to the measured data of a satellite, anti-interference ability is usually about 10.5 V, RC filter circuit is designed after LM139A, and the simulation result of anti-interference pulse width is more than 1ms. For integrated circuit form command load, anti-interference ability is related to device parameters and interface circuit design. For example, the anti-interference ability of CC4050 integrated circuit command interface is usually about 4 V–6 V. The width of anti-interference pulse depends on the RC filter circuit design.

5 Conclusion Satellite in-orbit faces internal and external electromagnetic interference environment, which will interfere with various telecommand interfaces inside the satellite, resulting in misoperation of the equipment. This paper analyzes the anti-interference characteristics of various telecommand loads. Focus on the analysis and testing of circuit characteristics of pulse command user load which is susceptible to interference. The results show that the relay load has good anti-interference ability, which mainly depends on relay selection. The anti-interference capability of optocoupler load is closely related to the circuit design. The anti-interference ability of other loads should be analyzed according to the circuit. The anti-interference ability of integrated circuit loads is relatively weak.

References 1. Weiquan, F., et al.: GEO satellite ground potential transients caused by surface charging & discharging and their interferences on secondary power supply. Spacecraft Environ. Eng. 30(1), 54–57 (2013) 2. Hongwei, L., et al.: Experimental study of the effects of spacecraft charging and discharging on typical satellite-borne electric instrument. Spacecraft Environ. Eng. 38(3), 370–374 (2021) 3. Purvis, C.K., Garrett, H.B., et al.: Design Guidelines for Assessing and Controlling Spacecraft Charging Effects. NASA TP 2361 (1984) 4. Shifeng, G., et al.: Research of surface discharge effect simulator for spacecraft. Spacecraft Environ. Eng. 29(5), 561–565 (2012) 5. NASA-HDBK-4002 Avoiding Problems Caused By Spacecraft On-Orbit Internal Charging Effects (1999)

Intelligent Propagation Prediction Model for Wireless Radio Channel Based on CNN Yajuan Qiao(B) , Yiyang Xiong, Shilei Dong, Xuezhi Zhang, and Hua Tan China Telecom Corporation Limited Research Institute, Beijing, China [email protected]

Abstract. As a reliable communication medium, the wireless radio channel determines the quality and performance of wireless communication systems. It is imperative for operators to accurately predict the propagation model before designing and planning the next-generation cellular networks. The intelligent propagation model of wireless radio channels based on the convolutional neural network (CNN) is proposed to predict reference signal receiving power (RSRP) in this paper. Specifically, several appropriate features are designed in terms of the transmitter, propagation, and receiver according to the empirical models, such as the OkumuraHata model and the Cost 231-Hata model. And then these features with high variance and correlation coefficient are selected. Data preprocessing is carried out through data cleaning, data normalization, data partition, and data visualization before the training stage of the intelligent propagation model. Finally, the selected features are fed into CNN to predict the RSRP value. The simulation results indicate that the proposed model has superior performance. The work of this paper will contribute to the planning and optimization of future cellular networks. Keywords: Intelligent propagation prediction model · Wireless radio channel · Convolutional Neural Network (CNN) · RSRP prediction

1 Introduction As the medium of wireless communication systems, the wireless channel is the key factor affecting communication quality. The prerequisite of network planning and optimization is to establish an accurate propagation prediction model. Generally, the operators deploy the sites of the base station (BS) by referring to the reference signal receiving power (RSRP). Without the propagation prediction model, RSRP can only be obtained through field measurements, which are very expensive and time-consuming. The existing literature has studied propagation prediction models, which can be roughly divided into two categories, namely empirical models and deterministic models. Empirical models are usually a series of simple and effective formulas obtained from field measurements, such as the Okumura model, the Standard Propagation Model (SPM), and the Cost 231-Hata path loss model [1]. The main drawback of empirical models is that they cannot be applied to different environments without modification. Deterministic models are the numerical derivation of reflection, scattering, and diffraction, such as © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 486–493, 2023. https://doi.org/10.1007/978-981-19-9968-0_59

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ray-tracing [2]. The disadvantages of the ray-tracing method are the large calculation burden for indoor complicated propagation environments. And the required parameters should be more accurate and detailed than that of the empirical models. The wireless radio channels are continuously exposing inherent limitations. Not only is it vulnerable to interference and noise, but also changes in unpredictable ways over time due to environmental dynamics and user mobility. These drawbacks are spurring operators to focus on selecting an appropriate intelligent propagation model of wireless radio model to accurately predict the RSRP value. As an important research field of artificial intelligence (AI), machine learning (ML) techniques have been successfully applied to the fields of language and image processing [3–5]. While the demand for wireless communication has increased dramatically, the development of wireless communication systems has encountered bottlenecks of poor real-time performance and high complexity. In addition, a large amount of data is generated with the penetration of wireless communication technology and vertical industries. Then, the measured data is collected to obtain a propagation model that better matches the actual environment. In order to improve network construction efficiency and reduce network construction cost at the same time, it is an effective method to build wireless intelligent propagation models by collecting historical data and using ML [6]. In this paper, appropriate features are designed based on the existing channel models, and a CNN-based intelligent propagation prediction model for wireless radio channels has been proposed. The simulation results indicate that the trained model has an excellent performance.

2 Feature Engineering of the Wireless Radio Channel 2.1 Empirical Models of Wireless Radio Channel There are several empirical models for propagation prediction, whose parameters can reflect the characteristics of the wireless radio channel [7]. This section first introduces the existing empirical models and designs the features on this basis. The Okumura-Hata model is one of the most common wireless signal models. It is an improved empirical model based on the path loss data provided by the Okumura model, and the formula is shown as below. PL = 69.55 + 26.16 lg f − 13.82 lg(hb ) − α(hm ) + (44.9 − 6.55 lg(hb )) lg d

(1)

where PL denotes the path loss, f denotes the frequency, hb denotes the effective height of the BS antenna, hm denotes the effective height of the user equipment (UE) antenna, d denotes the distance between the BS and the UE, and α(hm ) denotes the correction factor of UE antenna height. The region-specific correction parameter Cm was introduced into the Okumura-Hata model. The Cost 231-Hata model is considered as the extension to the Okumura-Hata model. The path loss can be expressed by the following formula. PL = 46.3 + 33.9 lg f − 13.82 lg(hb ) − α(hm ) + (44.9 − 6.55 lg(hm )) lg d + Cm (2) where Cm should be set to 3 dB for metropolitan areas and 0 dB for suburbs and mediumsized cities.

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2.2 Features Design and Selection of Wireless Radio Channel Generally speaking, the upper limit of ML is determined by data and features, and algorithms and models can only approach this upper limit [8]. An appropriate selection of designed feature values can reduce the time complexity and computational complexity of the RSRP prediction process. Increasing the selected feature dimensions can improve the accuracy of recognition. Here features are filtered by the correlation coefficient and variance. The Pearson correlation coefficient is the most popular correlation coefficient for measuring the direction and strength between two variables. It indicates a negative linear correlation between the two variables if the Pearson correlation coefficient is between −1 and 0. Otherwise, it indicates a positive linear correlation. The Pearson correlation coefficient is calculated by the following formula. ρX ,Y =

cov(X , Y ) σX σY

(3)

where σ denotes the standard deviation and cov(X , Y ) denotes the covariance. In statistical descriptions, the variance is a measure of how the feature dataset spread out from the overall mean, which is calculated as follows:  (X − E(X ))2 (4) σ2 = N where σ 2 is the overall variance of sample dataset, E(X ) is the expectation of variable X , and N is the overall number. According to the above models, the data features in this paper are designed mainly in terms of the transmitter, propagation, and receiver. Among them, the transmitting power, azimuth, and effective height of the BS are designed from the transmitting aspect of the signal. The clutter type, height of buildings, and density of buildings are designed from the signal propagation aspect. The horizontal distance, three-dimensional (3D) propagation distance, horizontal angle, relative height difference, and altitude of UE are designed from the receiving aspect of the signal. The effective height of BS is the sum of the altitude of BS and its own altitude. The relative height difference is the difference between the effective height of BS and UE. The coordinates of BS are denoted by (x0 , y0 ) and the coordinates of UE are denoted by for the horizontal distance between BS and UE is dxy = (x 1 , y1 ). The specific formula  2 2 (x1 − x0 ) + (y1 − y0 ) = x2 + y2 . The formula for calculating the 3D link  distance between UE and BS is d = x2 + y2 + h2 . The formula   for calculating y2 −y1 the horizontal angle between the BS and the UE is α = arctan x2 −x1 . In this paper, the features with high variance and correlation should be selected, and the appropriate features are shown in Table 1.

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Table 1. Appropriate features of wireless radio channel. Wireless radio channel

Definition of features

Variables

Transmission

Transmitting power of BS

P

6.3208

−0.0142

Azimuth of BS

β

1.0668e4

0.0031

Propagation

Reception

Variance

Effective height of BS

hBS

216.6888

Clutter Type of BS

IBS

10.5262

Pearson correlation coefficient

0.0048 −0.0177

9.2546

−0.0290

217.3138

−0.0465

ρ

0.0059

−0.0379

The horizontal distance between BS and UE

dxy

1.1888e6

−0.1856

3D link distance

d

1.1871e6

−0.1852

Horizontal angle of BS α and UE

2.1154e4

−0.0171

Clutter Type of UE

IUE

Height of buildings

hbuilding

Density of buildings

Relative height difference

h

107.8552

−5.9787e-4

Altitude of UE

hUE

120.9982

0.0070

3 CNN-Based Wireless Intelligent Propagation Model 3.1 Data Preprocessing Data preprocessing is carried out through data cleaning, data normalization, data partition, and data visualization before the training stage of the intelligent propagation model. 1) Data cleaning The required data sets are extracted from large databases in the real world. However, raw data may be missing or redundant. Therefore, outliers, missing values, and duplicate values are handled in the data cleaning process. 2) Data normalization Due to different scales of features in the wireless channel model, some features such as cell index may play a dominant role if the data is analyzed directly. Therefore, the feature dataset should be normalized before training. Here is the normalization formula. x=

x − xmin xmax − xmin

(5)

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3) Data partition The experimental sample dataset is a multi-cell with 12 columns of feature parameters. All samples are in CSV format. In this experiment, 70% of all samples are training data sets, and 30% of all samples are test data sets. 4) Data visualization As we can see, Fig. 1 shows the distribution of RSRP values at different geographical locations and directions. It can be seen that the RSRP value is inversely proportional to the distance. That is, the farther UE is from BS, the smaller the RSRP value. On the other hand, the signal of UE located in the horizontal coverage of BS is much stronger than others.

Fig. 1. Distribution of RSRP at different locations and directions.

3.2 CNN-Based Wireless Intelligent Propagation Model The neural network (NN) uses a forward propagation algorithm to obtain the current values of the network parameters and calculate the predicted values of the training data. And the gradients of the model parameters are computed by the back propagation algorithm based on the loss function. The network parameters are updated by the stochastic gradient descent algorithm. Finally, the model training is completed by iteration. Therefore, NN is good at mining potential features from the original data. Convolutional Neural Network (CNN) mainly consists of the input layer, output layer, convolutional layer, pooling layer, and fully connected layer [9]. The CNN-based RSRP prediction model in this paper is a new model built by modifying the parameters of the naive-network-in-network (NiN). NiN is a deep network constructed by concatenating several small networks consisting of convolutional and fully connected layers. A 1 × 1 convolutional layer is added to the different convolutional layers instead of the fully connected layers so that the spatial information is passed to the later layers. The impact and contribution of the number of convolutional kernels to the overall CNN are measured by comparing the RMSE of the RSRP predictions. As shown in Fig. 2, it

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Fig. 2. The architecture of the CNN-based wireless intelligent propagation model.

illustrates the network architecture of the CNN-based wireless intelligent propagation model. The purpose of deep network model parameter initialization is to attenuate the dependence of the non-convex optimization objective function on the initial value and to avoid the parameters of the desired model from falling into a local optimum prematurely. For the selection of hyper-parameters of deep CNNs, it is generally preferred to use small filters, small strides and zero padding to improve the accuracy of the entire network. The initial learning rate takes a larger value to quickly obtain the optimal solution. Then the model is stabilized by iteratively decreasing the learning rate.

4 Simulation Results The simulation results of the proposed CNN-based intelligent propagation model are presented in this section.

Fig. 3. Performance comparison of several CNN architectures.

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With the other network parameters fixed, the size of the convolutional kernel and the number of convolutional layers can significantly influence the system performance. In Fig. 3, (64, 1) in the legend represents the number of convolution layers and the size of convolution kernel respectively. As we can see, the performance of CNN with convolution kernel size of 5 is superior to that with convolution kernel size of 1 and 3. Moreover, the number of training iterations required for CNN convergence decreases as the number of convolution layer increases. Thus, considering both the calculate budget of training stage and the prediction performance, the number of convolutional layers is set as 256 in this paper.

Fig. 4. Comparison of RSRP prediction error between the proposed propagation model and several propagation models.

Considering that the existing advanced commercial network planning tools often use simple and effective empirical models [10], we compare RSRP prediction error of the proposed intelligent prediction model for wireless radio channel with several existing models in Fig. 4. In the empirical model, RSRP is the difference between the transmit power of BS minus the path loss. It can be seen that the prediction error of the model based on CNN is much smaller than that of other empirical models.

5 Conclusion In this paper, a CNN-based intelligent propagation prediction model for wireless radio channel is proposed. Specifically, features concerning the empirical model are extracted from raw data. Then features with high correlation and variance are preferred and the selected features are fed into CNN training module. Furthermore, the simulation results demonstrate that CNN improves the accuracy of prediction as compared to empirical propagation models. To the best of our knowledge, this paper focuses on the propagation prediction model, which is helpful for the further planning and optimization of future cellular networks.

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References 1. Walid, S., Mehdi, B., Mingzhe, C.: A vision of 6G wireless systems: applications trends technologies and open research problems. IEEE Network 34(3), 134–142 (2020) 2. David, T., Pramod, V.: Fundamentals of Wireless Communication. Cambridge University Press (2005) 3. Jiyu, J., Xuehong, S., Liang, F., et al.: An overview of wireless communication technology using deep learning. China Commun. 18(12), 36 (2021) 4. Tao, Z., Haitong, Z., Bo, A., et al.: Deep-learning based spatial-temporal channel prediction for smart high-speed railway communication networks. IEEE Trans. Wireless Commun. (2022) 5. Chen, H., Ruisi, H., Bo, A., et al.: Artificial intelligence enabled radio propagation for communications—Part II: Scenario identification and channel modeling. 70(6), 3955–3969 (2021) 6. Sai, W., BaoJuan, M., JiaRui, Z., SiSi, Z.: Intelligent propagation model method for rsrp prediction based on machine learning. In: 2021 7th International Conference on Computer and Communications (ICCC) (2021) 7. Usama, M., Hasan, F., Ali, I.: A machine learning based 3d propagation model for intelligent future cellular networks. In: 2019 IEEE Global Communications Conference (GLOBECOM) (2020) 8. Li, H., Li, Y., Zhou, S., Wang, J.: Wireless channel feature extraction via GMM and CNN in the tomographic channel model. J. Commun. Information Networks 2(1), 41–51 (2017) 9. Szegedy, C., Wei, L., Yangqing, J., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015) 10. Lingcheng, D., Hhongtao, Z., Yanli, Z.: Propagation-model-free coverage evaluation via machine learning for future 5g networks. In: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (2018)

Research on Application Efficiency Analysis Method of Remote Sensing Satellite for Emergency Gao Han1(B) , Bai Zhaoguang1 , Che Xiaoling1 , Zhao Liang2 , Wu Bin1 , Wang Chao1 , Zhu Jun1 , and Bai Yuchen1 1 DFH Satellite Co., Ltd, Beijing 100094, China

[email protected] 2 Institute of Remote Sensing Satellite, CAST, Beijing, China

Abstract. In the traditional remote sensing satellite evaluation, starting from the attributes of the satellite remote sensor itself, the traditional evaluation indexes with coverage characteristics as the core, the evaluation indexes based on navigation accuracy and the connectivity index of satellite network are used as evaluation items, and the application efficiency of the satellite in the actual use process is not considered. Starting from the existing evaluation methods of remote sensing satellites, this paper analyzes the influencing factors of satellite effectiveness in conventional tasks and emergency tasks, puts forward the analysis method of remote sensing satellite application effectiveness for emergency events, normalizes and quantifies the indicators by using the satisfaction membership function, determines the weight by the exponential scale weight determination method, establishes the evaluation model and establishes the satellite imaging model, The application efficiency of three satellites for emergency events is simulated and analyzed, and the feasibility and effectiveness of the method are verified. From the perspective of demand, it provides quantitative design reference for satellite top-level design, which is of great significance to satellite design and constellation system establishment. Keywords: Emergency events · Remote sensing satellite · Influencing factors · Application efficiency

1 Introduction Remote sensing satellites are constrained by orbit, attitude and illumination environment. Generally, the efficiency of the satellite can be preliminarily measured by calculating the time window of the earth imaging satellite and quantitatively describing the window length, revisiting cycle interval, coverage times, coverage rate and so on [1]. Wang Hao et al. [2] constructed satellite performance evaluation indicators from three aspects: coverage, cost and elasticity, and compared the performance of SkySat and Jilin-1 constellation. Li et al. [3] used the method of multi-attribute decision-making to evaluate the coverage performance of remote sensing satellites, and the attribute weight was © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 494–504, 2023. https://doi.org/10.1007/978-981-19-9968-0_60

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determined by analytic hierarchy process and entropy weight method. The efficiency of satellite image acquisition and target recognition is directly reflected in the effectiveness of satellite image acquisition and imaging. Shen Rusong et al. [4] established the target recognition capability model of imaging satellite, taking into account the influence of environmental factors on the probability of satellite target recognition. To sum up, the existing remote sensing satellite effectiveness evaluation methods mainly take various satellite performance indicators as factors, and do not consider the demand-oriented satellite mission effectiveness enough. Satellite system effectiveness is a measure of the degree to which a system is expected to meet a specific set of tasks [5]. From this definition, we can see that the system efficiency is different for different task sets, that is, the system efficiency changes dynamically with the change of tasks. Therefore, system effectiveness is the overall mission effectiveness. The quantitative conclusion provides a reference for the top-level design of satellite system to maximize the efficiency level, so as to meet the development requirements of high quality, high benefit and high efficiency of earth observation in the future, effectively avoid low-level repeated construction, improve satellite application benefits and solve the practical problems of low existing user satisfaction [6].

2 Analysis on Influencing Factors of Remote Sensing Satellite Application Efficiency 2.1 Influencing Factors of Routine Application Efficiency Effective Remote Sensing Data Acquisition Capability. The main factors that determine the data acquisition ability of remote sensing satellite include sensors, satellite platform, satellite orbit, remote sensing satellite imaging conditions and so on [7–9]. The satellite indexes that affect the data acquisition range include resolution, width, attitude mobility, attitude stability, positioning accuracy, etc. The imaging indexes that affect the data quality include satellite solar altitude angle, weather conditions, etc. APL_eff = APL · η

(1)

where APL_eff is the load effective data acquisition capability, APL is the load data acquisition capability, and η represents the effective coefficient of data acquisition. APL = Nrev ×

M 

k k ωk Nres Psce

(2)

k=1

where Nrev represents the number of revisiting per unit time, M represents the number of resolutions, ωk represents the application weight of the image obtained under the k k represents the number of comprehensive identification of single imaging resolution, Nres k represents the maximum number targets of the satellite under the k resolution, and Psce of imaging scenes of a zenith crossing satellite under the k resolution. η = (α1 η1 + α2 η2 )η3 η4

(3)

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where α1 and α2 represent the weight of satellite attitude stability and positioning accuracy, where α1 + α2 = 1, η1 and η2 represent the quantitative values of the above two influencing factors, and η3 represents the solar altitude angle. For visible light load, the imaging period with solar altitude angle greater than 15 ° is divided by the total imaging time; η4 refers to the maximum imaging weather conditions, which is the number of cloudless and rainy sunny days in the observation period of a year divided by the number of days in the whole year according to the data acquisition ability once a day. For the radar load, it is not affected by cloud and rain, so the effective coefficient η=1 is obtained from the data. Remote Sensing Information Transmission Capability. The main factors affecting the information transmission capability of remote sensing satellite include information transmission timeliness, information processing capacity, communication connection availability, etc. the influencing index factors include the signal power transmitted by the antenna, antenna gain, the number and capacity of ground stations, the number and capacity of relay satellites, weather conditions, etc. the specific indexes include telemetry code rate, data transmission code rate, on-board storage capacity, ground station reception rate, etc. Acom = β1 ρrat1 Irel + β2 ρrat2 Icon

(4)

where Acom represents the ability of remote sensing information transmission, β1 and β2 represent the time delay coefficient, which represents the time delay from data acquisition time to complete data transmission; ρrat1 and ρrat2 represent the effective data rate of relay data transmission and ground data transmission respectively, Irel represents the data transmission capacity of relay satellite, and Icon represents the data transmission capacity of ground station. Icon = γsta Twork−sta /Tinv−sta

(5)

Irel = γrel Twork−rel /Tinv−rel

(6)

where γsta and γrel represent the data transmission rate, Twork−sta and Twork−rel represent the total data transmission time of the relay satellite and the ground station respectively, Tinv−rel and Tinv−sta represent the visible time of the remote sensing satellite to the relay satellite and the ground station respectively. Satellite Identification Capability. The main factors affecting the recognition ability of remote sensing satellite include target detection ability, target recognition ability and target confirmation ability. It is necessary to take into account the number confirmation of targets and the description of remote sensing characteristics. The influencing index factors include load type, mapping quality, load resolution, number of image scenes obtained, satellite positioning ability, etc. R = λ1 Rdete + λ2 Rdist + λ3 Rconf

(7)

where R represents the remote sensing satellite identification capability, Rdete represents the target detection capability, Rdist represents the target identification capability, and

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Rconf represents the target confirmation capability, which is determined by the target a priori database and algorithm accuracy. λ1 , λ2 and λ3 represent the weight coefficients of the three capabilities respectively, which are determined according to the task type.  Rdete = νi Ti (8) i

where Ti and νi represent the load type and the quality factor of the image obtained by this type of load, respectively.

2.2 Influencing Factors of Emergency Application Efficiency Instant Messaging Capability. In the emergency observation task, information timeliness is an important consideration index. The main factors affecting the ability of satellite real-time information acquisition include information updating ability, continuous observation ability and so on. T=

δ1 δ2 δ3 + T + T e T1 e 2 e 3

(9)

where, δ1 , δ2 and δ3 respectively represent the coefficients of three influencing factors: emergency task response time, satellite system response time and task execution time, and δ1 + δ2 + δ3 = 1, T1 , T2 and T3 represent emergency task response time, satellite system response time and task execution time. Emergency Task Response Capability. The main influencing factors of emergency observation task response capability include emergency task response time (task planning and scheduling time), satellite system response time (annotation and distribution time of task data block), task execution time (maneuver time of satellite task execution), etc. K = ϕ1 M1 + ϕ2 M2

(10)

M1 = m1 + (1 − m1 )(TS /TL )

(11)

M2 =

SR SN

(12)

where m1 represents the satellite information update coefficient corresponding to the current task, TS represents the shortest observation interval, and TL represents the longest observation interval. SN represents the total number of observation windows, SR represents the number of observation windows that meet the time required for emergency observation, ϕ1 and ϕ2 represent the weight coefficients of information updating ability and continuous observation ability.

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2.3 System Comprehensive Efficiency Emergency task is the task with the highest priority. If there is any, it will be implemented first. The comprehensive effectiveness of the constellation system needs to consider both the conventional task effectiveness and emergency effectiveness of the system. The influencing factors include the total time of the emergency task, including the task execution time and the recovery time of the conventional task. In the comprehensive effectiveness evaluation of the satellite system, the impact cost factor of the emergency task is proposed to evaluate the comprehensive effectiveness of the constellation system.   N  Teme /N (13) = T0 i=1

where N represents the number of on orbit working days, Teme represents the total time of emergency tasks every day, and T0 represents the total time of tasks in a day.

3 Efficiency Analysis Method of Remote Sensing Satellite for Emergency Application 3.1 Quantification of Efficiency Influencing Factors In the first layer, the satellite effectiveness and the satellite emergency effectiveness need to be comprehensively evaluated. In the second layer, the satellite effectiveness and the satellite emergency effectiveness need to be comprehensively evaluated. Constellation effectiveness can be expressed quantitatively as E=

2 

k Ai (x)

(14)

k=1

where k represents the weight matrix of each layer, Ai (x) represents the satisfaction membership function, sets the number of fuzzy satisfaction levels, and the domain experts give the fuzzy set membership function corresponding to each satisfaction level according to the data variation range of single index [10]. There are five satisfaction levels, namely {highest, higher, medium, lower and lowest}. The trapezoidal fuzzy number is used to establish the membership function model, which is divided into the smaller the better type and the larger the better type, as shown in Fig. 1 and Fig. 2. Quantify the above five capability indicators according to the satisfaction level and its membership function. For each indicator, calculate its membership function according to the satisfaction level: ⎧ ⎪ ⎨ 1 0 ≤ x ≤ a1 −x a1 < x < b2 (i = 1) (15) Ai (x) = bb22−a ⎪ ⎩ 0 1 b2 ≤ x

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Fig. 1. The smaller the better membership function

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Fig. 2. The bigger the better membership function

⎧ 0 x ≤ ai−1 ⎪ ⎪ ⎪ x−ai−1 ⎪ a ⎪ ⎨ bi −ai−1 i−1 < x < bi Ai (x) = 1 bi ≤ x ≤ ai (i = 2, 3, 4) ⎪ bi+1 −x ⎪ ⎪ a i < x < bi+1 ⎪ ⎪ ⎩ bi+1 −ai 0 bi+1 ≤ x ⎧ ⎪ ⎨ 0 0 ≤ x ≤ a4 4 Ai (x) = bx−a −a4 a4 < x < b5 (i = 5) ⎪ ⎩ 51 b5 ≤ x

(16)

(17)

where a1 · · · · · ·a5 and b1 · · · · · ·b5 are the parameter values of satisfaction membership function, which need to be given according to expert opinions to obtain the corresponding parameter values of satisfaction membership function. The satisfaction subordinate value of the index is Bi = i − 1 + Ai (x)(i = 1 · · · 5)

(18)

The satisfaction vector = [Bi ](i = 1 · · · 5) is composed of the satisfaction membership values of each performance index. 3.2 Confirmation of Efficiency Influencing Factors According to the importance of the third level performance index elements on the satellite capability, the weight judgment matrix of pairwise comparison is established, and the “exponential 1–9 scale method” is adopted [11, 12], as shown in Table 1. Using scale to obtain pairwise weight judgment matrix ⎤ ⎡ a11 · · · a1n

 ⎢ .. ⎥ (19) R = aij n×n = ⎣ ... . ⎦ an1 · · · ann

The matrix elements must meet the following conditions: aij = 1/aji , aij > 0, aii = 1. Compare and judge the importance according to expert opinions. Under the given scaling principle, construct the weight judgment matrix aij∗ according to the scaling

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Scale

Comparison of performance elements and importance

0 98

Equally important

2 98

Strong importance

4 98

Strong importance

6 98

Very important

8 98

Absolutely important

Between the two elements

98 , 98 , 98 , 98

1

3

5

7

values given by multiple experts, calculate the optimal scaling value of the matrix, and set the number of experts participating in the evaluation as m. aij∗ =

m  t  m1 , i, j = 1, 2, · · · , n; t = 1, 2, · · · m t=1 aij

(20)

The final weight judgment matrix is   A = aij∗

⎤ ∗ · · · a∗ a11 1n ⎢ .. ⎥ = ⎣ ... . ⎦ ⎡

n×n

∗ an1

···

(21)

∗ ann

Calculate the maximum eigenvalue λmax of matrix A and the corresponding eigenvector I , verify the random consistency of the obtained weight vector. CI =

(λmax − n) (n − 1)

(22)

CI RI

(23)

CR =

where CI is the consistency standard, CR is the consistency test rate, RI is the random consistency index, and the value of RI is shown in Table 2. Table 2. Standard RI value. n

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.52

0.89

1.12

1.26

1.36

1.41

1.46

1.49

When CR ≤ 0.1, the eigenvector meets the consistency requirements. If CR > 0.1, the weight judgment matrix does not meet the consistency requirements, it needs to be

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reevaluated according to the matrix scaling principle [13, 14]. Calculate weight vector β=

I |I |

(24)

4 Application Effectiveness Evaluation Test 4.1 Simulation Scene Settings The start time is 00:00:00 (UTCG) on March 1, 2022 and the end time is 00:00:00 (UTCG) on March 4, 2022. Four satellite constellations are set in the scene to carry different payloads. The number of orbits and loads are shown in Table 3. The location of the emergency target area is a square area with a side length of 10 km and a center point of 27.8°N and 102.26°e. the characteristic size of the ground target is set as 20 m. Table 3. Simulation scene satellite settings. Satellite constellation

Semi major axis of track (km)

Track inclination (DEG)

Ascending node right ascension (DEG)

Perigee angle (DEG)

True proximal angle (DEG)

Ground resolution (m)

Field of view (DEG)

Load type

Sat1

6928.14

97.5976

151.229

0

0

0.5

2.1

Optical

Sat2

6878.14

97.4065

0

0

2

8.4

Radar

Sat3

6939.06

97.6399

0

0

16

64

Optical

86.2288 143.729

4.2 Satellite Application Efficiency Calculation According to the characteristics of satellite effectiveness influencing factors, the satisfaction membership function value is determined by expert opinions, and the decision matrix of satellite effectiveness influencing factors in Table 4 is brought into formulas (1–13), so as to obtain the satisfaction vector of three satellites, as shown in Table 5. The judgment matrix is ⎡ ⎤ 1 0.732 0.211 0.191 0.182 ⎢ 1.366 1 0.331 0.314 0.322 ⎥ ⎢ ⎥   ⎢ ⎥ ∗ (25) = ⎢ 4.739 3.021 1 1.115 1.115 ⎥ A = aij ⎢ ⎥ 5×5 ⎣ 5.236 3.185 0.897 1 1.234 ⎦ 5.495 3.106 0.897 0.810 1 The maximum eigenvalue of the weight judgment matrix is λmax = 5.168 and the corresponding eigenvector is I = [ −0.114 −0.175 −0.576 −0.580 −0.537 ]. Check the consistency of the matrix according to the consistency standard, and calculate the

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Efficiency index

Sat1

Sat2

Sat3

Number of revisits

2.09

1.96

2.13

Target comprehensive identification quantity

30

20

10

Maximum number of imaging scenes

3.3

13.1

100

Data transmission rate (Gbps)

1.5

1.5

1.5

Visible time of ground station (s)

1200 s

3000 s

1200 s

Satellite identification probability

0.9

0.75

0.6

Target detection probability

0.9

0.7

0.5

Target recognition probability

0.9

0.6

0.2

Response time of emergency task (s)

30

25

20

Satellite system response time (s)

10

10

10

Task execution time (s)

60

30

5

Shortest observation interval (s)

18301.042

17020.112

16701.552

Longest observation interval (s)

33316.252

31966.159

31374.863

Number of observation windows

5

10

5

Proportion of emergency task time

0.3

0.3

0.3

Table 5. Satisfaction membership function value and corresponding satisfaction. a1

b2

a2

b3

a3

b4

a4

b5 Sat1 Sat2 Sat3

Effective data acquisition 0.65 0.7

0.75 0.8

0.85 0.9

0.95 1

4.4

3.41 3.63

Information transmission 0.5

0.5

0.7

0.9

4

4.5

4.5

3.4

3.13

3.11 3.5

4.76

Satellite identification

0.5

0.6

0.35 0.35 0.35 0.5

Emergency task response 0.4

0.4

0.4

Instant messaging

0.7

0.75 0.8

0.7

0.8

0.65 0.7

1

0.85 1

0.52 0.64 0.76 0.88 1 0.85 0.9

0.95 1

4

3.03 3.98 4.03

corresponding RI = 1.12 for CI = 0.042 and n = 5, then CR = 0.0375, and CR ≤ 0.1 eigenvectors meet the consistency requirements. The weight vector is obtained by normalizing the feature vector I0 = [0.0575 0.088 0.291 0.293 0.271]

(26)

The membership vector and weight vector are brought into formula (14), where the weight vectors between the first and second layers are 1 = 0.7 and 2 = 0.3. The calculated efficiency values of the three satellites are shown in Table 6. According to the test results, it can be seen that the evaluation value of satellite effectiveness is SAT3 > SAT2 > SAT1, in which the scores of various capability indexes of satellite 2 are relatively balanced. Satellite 3 has the characteristics of large width.

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Table 6. Effectiveness value of three satellites.

Effectiveness value

SAT1

SAT2

SAT3

1.2885

1.3007

1.4382

Although the resolution is low, based on the characteristics of large width, the number of acquired scenes is much larger than satellite 1 and satellite 2, and the score of effective data acquisition ability is between satellite 1 and satellite 2, At the same time, it has the highest score in emergency task response ability and instant information acquisition ability; The load of satellite 2 is radar load and runs in the morning dusk orbit. Its information transmission ability is strong, which can realize the continuous transmission of remote sensing information and has a great weight contribution to emergency applications. Therefore, the evaluation value is between satellite 1 and satellite 2; Satellite 1 is superior to the other two satellites in terms of effective remote sensing data acquisition ability and satellite identification ability. However, due to its narrow width, it needs more response time in case of emergency, and its revisit ability to the emergency area is slightly insufficient. Therefore, it has a low score in the evaluation of satellite application ability facing emergency.

5 Conclusions This paper analyzes the influencing factors of the effectiveness of remote sensing satellites for emergency applications, and puts forward the effectiveness evaluation method. The index weight is calculated by using the exponential scale weight determination method. Finally, the comprehensive evaluation values of different satellites are compared, and the satellite effectiveness ranking is given. The feasibility and effectiveness of the evaluation method are verified by simulation experiments, and the quantitative evaluation of satellite application effectiveness for emergency events is realized, which can provide a reference for the design of remote sensing satellite system and large-scale constellation deployment. When designing and deploying high-density remote sensing satellite constellation system, it is necessary to consider the impact of conventional mission and emergency mission indicators on satellite efficiency at the same time, and carry out top-level design guided by demand and practical application ability, so as to improve the comprehensive application efficiency of remote sensing satellite and realize the progress of satellite system from “available” to “easy to use”. According to the system engineering manual of the international society of systems engineering, 85% of the development cost of the whole life cycle of the system is determined in the design stage of the engineering project. Establish a quantitative satellite system effectiveness evaluation system, provide optimization direction and reference in the satellite design stage, help establish a demand-oriented constellation system and effectively control the constellation engineering cost. Follow up research will continue to focus on the practical application capability of satellites, explore top-level design optimization under the condition of fixed cost, and

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improve the application efficiency of satellite system for emergency observation through the configuration of satellite function, quantity and orbit. Acknowledgement. This project was supported by National Natural Science Foundation of China (Grant No. 42192582).

References 1. Chen, H., Li, Y., et al.: Weapon system effectiveness evaluation and evaluation innovation. J. Equipment Command Technol. College 15(4), 5 (2004) 2. Wang, H., Zhang, Z., Zhang, H., et al.: Research on performance evaluation of small satellite reconnaissance constellation. China Space Science and Technol. 40(06), 68–76 (2020) 3. Li, H., Li, D., Li, Y.: A multi -index assessment method for evaluating coverage effectiveness of remote sensing satellite. Chinese J. Aeronautics 31(10), 2023–2033 (2018) 4. Shen, R., Song, G., Lv, W., et al.: Analysis of target recognition ability of imaging reconnaissance satellite. J. System Simulation (S2), 34–37 (2006) 5. Jiang, W., Li, T., Chen, S.: A design method of remote sensing satellite system based on multi-user demand driven. China Aerospace (6), 28–31 (2014) 6. Lv, Y., Zhang, W., et al.: The important role of exponential scaling in AHP scaling system. Journal of Systems Eng. (05), 452–456 (2003) 7. Wang, Y., Zhao, C., Gao, Y., et al.: Application Research of satellite effectiveness analytic hierarchy process based on HLA simulation. China Science and Technol. Information (3) (2021) 8. Lu, X., Li, F., Xiao, B., et al.: Evaluation method of detection efficiency of space-based optical imaging system. Journal of Photonics (12) (2020) 9. Wang, B.: Research on effectiveness evaluation method of low earth orbit imaging satellite. Science and Technology Innovation (9) (2021) 10. Zhang, Y., Liang, Y., Chen, L., et al.: Threat assessment method of optical imaging reconnaissance satellite. J.of National University of Defense Technol. 34(05), 32–35 (2012) 11. Qiu, D., Tan, Q., Ma, M., et al.: Research on evaluation method of satellite imaging reconnaissance demand satisfaction. Computer Eng. 38(8), 256–259 (2012) 12. Yu, Y., Zheng, Q., Yang, S., et al.: Satellite comprehensive efficiency evaluation based on multi factor fuzzy reasoning. Computer Measurement and Control 28(9) (2020) 13. Zhibo, E., Li, J., et al.: A fast method for calculating the visibility of remote sensing satellites to regional targets. J. Tsinghua University (NATURAL SCIENCE EDITION) 59(09), 699–704 (2019) 14. Wang, Y.M., Luo, Y.: Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling 51(1–2), 1–12 (2010)

Research on Blind Restoration Algorithm of Motion Blurred Remote Sensing Image Hanwen Yang1(B) , Yanran Liu1 , Li Luan2 , Yunsen Wang2 , and Haoxuan Wang2 1 Shanghai Jiao Tong University, Shanghai 200240, China

[email protected] 2 Shandong Institute of Space Electronic Technology, Yantai 264670, Shandong, China

Abstract. The reconstruction of motion blur is a significant subject in remote sensing image processing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intelligent processing, a new blind restoration method based on Radon transform and autocorrelation function is proposed to process remote sensing image blurred by motion. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blur image. Autocorrelation function is quoted to calculate the blur scale. After the point spread function is estimated, Wiener filter is applied to realize the restoration of motion blurred images. By rotating blurred image to the horizontal axis direction, the two-dimensional restoration problem is converted into one dimension. Test results indicate that the method can obtain satisfactory recovering results of remote sensing image blurred by linear motion. Keywords: Image restoration · Point spread function · Radon transform · Autocorrelation function · Wiener filter

1 Introduction Motion blur is an important factor leading to remote sensing image degradation, which could destroy the edge and detail information of the image, and has a very adverse impact on the feature extraction and analysis of the image [1]. Many classical restoration algorithms are proposed to realize motion blurred image restoration, such as inverse filter [2], Lucy-Richardson filter [3], Wiener filter [4] and Laplacian constraint [5]. These methods are always realized based on modulation transfer function (namely MTF) or point spread function (namely PSF). However, both MTF and PSF are unable to be accurately estimated. Recently, neural network methods initiate rapid development in remote sensing processing image field [7], especially CNN (Convolution Neural Network) [8] and Hopfield neural network [9]. Besides that, other popular models such as U-net [10], GAN [11], residual net [12] have also developed rapidly. But these methods require high computing resources. This paper proposes a new blind restoration algorithm of remote sensing image based on Radon transform and autocorrelation function. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blurred © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 505–512, 2023. https://doi.org/10.1007/978-981-19-9968-0_61

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image. Autocorrelation function is introduced to compute blur scale. Wiener Filter is applied to realize restoration of motion blurred images. Test results indicate that the proposed method can obtain satisfactory recovering results of remote sensing image blurred by linear motion.

2 Image Deterioration Model 2.1 Description of Deterioration Model Different kinds of factors could degrade remote sensing data under complex imaging conditions in space. But most motion degradation models could be simulated by linear system approximation. Then the remote sensing image’s deterioration and reconstruction models would be shown in Fig. 1.

Fig. 1. Image deterioration and restoration model

Here f (x, y) represents the original remote sensing data, h(x, y) represents the point spread function, g(x, y) represents the degraded image, n(x, y) represents the stochastic noise. Then the image degradation model could be signified by g(x, y) = f (x, y) ∗ h(x, y) + n(x, y)

(1)

Assume r(x, y) is the restorative remote sensing image. So the objective of restoring method is to realize the following formula. r(x, y) ∼ = f (x, y)

(2)

2.2 Remote Sensing Image Motion Degradation Model During the imaging process of remote sensing camera, the relative motion of camera and targets might lead to image’s blurring. Motion blur can be considered as the averaging treatment of an image along the blur direction. On the basis of the satellite’s motion characteristics, uniform motion degraded images are premeditated. Assume a is the fuzzy scale, the PSF of degrading system fulfill the following formula.  1/a, 0 < x < a (3) h(x, y) = 1, others

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3 Remote Sensing Image Restoration Method 3.1 Estimation of Motion Angle Based on Radon Transform Frequency spectrum is applied here to estimate the motion angle, as shown in Fig. 2. The frequency spectrum diagram of motion degraded image is expressed with light and dark stripes. And the direction of motion is perpendicular to these stripes.

(a) Original data

(b) Degraded data

(c) Spectrum diagram

Fig. 2. Frequency spectrum diagram of motion degraded data

To determine the direction of motion blurred data, Radon transform is adopted to seek for lines in spectrum diagram. The implementation steps are as below. Step 1: Implement Gaussian filter on frequency spectrum diagram, which could reduce noise of image, the results are shown in Fig. 3(a).

(a) Results of Gaussian filter

(b) Results of edge detection

Fig. 3. Results of Gaussian filter and edge detection

Step 2: Use the Sobel operator to detect the edge of frequency spectrum diagram, as shown in Fig. 3(b). Step 3: Calculate the edge-image’s Radon transform form, then get a twodimensional matrix. The peaks on the transform graphic namely the straight lines on the raw image data, as shown in Fig. 4(a). Step 4: According to radon transform results, find the lines in the spectrum diagram, as shown in Fig. 4(b). Estimate the average geometric angle of these lines. The angle’s vertical direction could be regarded as the direction of motion blurred image.

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(a) Results of Radon transform

(b) Results of line detection

Fig. 4. Results of Radon transform and line detection

3.2 Estimation of Blur Scale Based on Autocorrelation Function Generally, the motion direction of blurred image is arbitrary. After measuring the motion direction, if the motion orientation is not horizontal, the image should be rotated to the horizontal axis direction, and the two-dimensional restoration problem would be converted into one dimension, as seen in Fig. 5.

Fig. 5. Rotated image of motion blurred image

In this paper, self-correlation equation is quoted to calculate the blur scale d. And The concrete steps are as follows. Step 1: Fulfill difference operating on vertical orientation of blurred data g(x, y). gy (i, j) = g(i, j) − g(i, j − 1)

(4)

Step 2: Implement difference operating on horizontal orientation of gy (x, y). gyx (i, j) = gy (i, j) − gy (i − 1, j)

(5)

Step 3: Evaluate self-correlation function of the blurred data. If the pixel quantity of the j-th row is l, and h is the image’s height, the self-correlation function can be expressed by R(k) =

h−k−l 

l 

i=l

m=−l

g(i + k + m, j)g(i + m, j)k ∈ [−1, 1]

(6)

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Step 4: Compute the mean value of self-correlation equation, and evaluate distance between two negative correlation peaks. Then the degraded scale can be defined. 3.3 Wiener Filter Restoration Model Wiener filter model is founded on the minimization statistics theory, which is still the optimal restoration algorithm to a certain degree. The Fourier transform of Wiener filter can be expressed by Hwiener (u, v) =

|H (u, v)|2 1 H (u, v) |H (u, v)|2 + C

(7)

Here H(u, v) is h(x, y)’s Fourier transform, and C is the power spectrum ratio of noise to primordial or non-degrading image.

4 Experimental Results 4.1 Comparative Experiment Comparative simulations are implemented to test the proposed image restoration method. Firstly, the remote sensing image shown Fig. 2(a) is chosen as research object. After blurring this image with different linear motions, motion angle and scale are estimated respectively. The contrasts between the true value and estimated value of the angle and scale are shown in Table 1, 2. As shown in Table 1, the difference between true blur angle and estimated blur angle is limited in 1.2°. So the proposed motion angle estimation method based on Radon transform is effective. Table 2 shows the contrast between estimated value and actual value of blur scale. And the difference is controlled below 1.0. Table 1. Contrast between true value and estimated value of the angle True blur angle (degree) Estimated blur angle (degree) Difference (degree)

10

20

30

60

90

9.8

21.2

29.1

61

89.6

−0.2

1.2

−0.9

1.0

−0.4

Table 2. Contrast between true value and estimated value of the length True blur scale (pixel)

15

20

25

30

40

Estimated blur scale (pixel)

16

19

25

31

39

1

−1

0

1

−1

Difference (pixel)

Wiener filter is put to use in the proposed method to restore the motion degraded data, as shown in Fig. 2(b). The motion angle is 30°, and the blur scale is 20. The power

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spectrum ratio is 0.005. To prove the advantages of the proposed method, L-R filter model and constrained least square method are implemented. The restoration results are as follows.

(a) L-R filter method

(b) Least square method

(c) Proposed method

Fig. 6. Restoration results of three methods

It is demonstrable that the process effect of proposed blind-restoration method has higher clearness than the other two methods. Figure 6(b) displays the processing effects of constrained least square method. And the quality of Fig. 6(b) is worse than Fig. 3. (b). It seems that additional noises are lead into the data. Table 3 displays the remote sensing images’ grayscale mean gradient (GMG) and spatial frequency (SF) factors of three methods. It was noticeable that the proposed method could get better recovery effect. Table 3. Contrast of three algorithms GMG

SF

L-R filter method

0.082

19.1

Least square method

0.062

15.4

Proposed method

0.096

21.6

4.2 Statistical Experiment To verify the validity of proposed algorithm, three degraded remote sensing data is selected to achieve recovery operations, as shown in the three subgraphs on the up side of Fig. 7. These images’ size is 2048 × 2048. The down side three images of Fig. 7 are restored results. It’s apparent that the details and contours of rehabilitative image data is more vivid. The texture features seems more remarkable. After implementing the proposed blind reconstructive method, all images’ GMG and SF parameters improves to a certain degree, as shown in Table 4. Through the restoration algorithm, the image quality is improved.

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Fig. 7. Restored results of proposed blind restoration algorithm

Table 4. Contrast of before and after restoration Image number

GMG

SF

Earlier

Later

Earlier

Later

1

0.058

0.066

16.5

17.4

2

0.072

0.083

17.6

18.9

3

0.064

0.072

17.8

19.3

5 Conclusions Based on Radon transform and autocorrelation equation, this paper proposes a new blind-restoration method of remote sensing image. Compared to conventional restoration algorithms, the proposed algorithm could obtain more accurate PSF and more satisfactory recovering results. And the blind restoration algorithm does not require training samples like neural network method. The method can be applied to recover low-quality remote sensing images degraded by linear motion. Acknowledgments. The work is supported by the National Natural Science Foundation of China under Grant U2013203.

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References 1. Li, Y., Xu, Q., Li, K.: New method of residual dense generative adversarial networks for image restoration. J. Chin. Comput. Syst. 41, 830–836 (2020) 2. Wu, X., Wu, J., Zhang, H.: Research on image restoration techniques based on inverse filtering algorithm. Inf. Technol. 35, 183–185 (2011) 3. Khetkeeree, S.: Optimization of Lucy-Richardson algorithm using modified Tikhonov regularization for image deblurring. In: CCISP2019, vol. 1438, pp. 1–6 (2020) 4. Khan, M., Nizami, I.F., Majid, M.: No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features. Multimedia Tools Appl. 78(11), 14485–14509 (2018). https://doi.org/10.1007/s11042-018-6797-4 5. Chen, Y., Nakao, Z., Arakaki, K., et al.: Restoration of gray images based on a genetic algorithm with Laplacian constraint. Fuzzy Sets Syst. 103(2), 285–293 (1999) 6. Nie, R., Liu, D., Zhang, X.: Image restoration based on MTFC. Beijing Surv. Mapp. 33(1), 116–119 (2019) 7. Wang, Y., Mu, W., Du, X., Ma, C., Shen, X.: Remote sensing image on-board restoration based on adaptive wiener filter. In: Wang, L., Wu, Y., Gong, J. (eds.) CHREOC 2019. LNEE, vol. 657, pp. 271–282. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-39473_20 8. Liu, L., Li, S., Lai, S.: Advance of neural network in degraded image restoration. J. Graph. 40(2), 213–224 (2019) 9. Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 3. IEEE Press, New York (2017) 10. Roy, H., Chaudhury, S., Yamasaki, T., et al.: Lunar surface image restoration using U-net based deep neural networks. Comput. Vis. Pattern Recognit. 50 (2019) 11. Kupyn, O., Budzan, V., Mykhailych, M., et al.: DeblurGAN: blind motion deblurring using conditional adversarial networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8183–8192 (2018) 12. Tang, J., Wang, K., Zhang, W., et al.: A deep learning-based image restoration method in optical synthetic aperture imaging system. Acta Optica Sinica 40(21), 1–9 (2020)

A Fast Algorithm of CU Block Division Based on Frequency Domain on VVC Zihao Liu, Lei Chen(B) , and Chenggang Xu Beijing University of Posts and Telecommunications, Beijing 100876, China {liuzihao1998,leichen}@bupt.edu.cn

Abstract. As the most mainstream codec standard in the world, VVC has been widely used all over the world and widely serves our lives. Compared with the previous generation of coding standards, in order to meet the needs of modern media, VVC has more feature that improves the coding quality, but also brings higher time complexity. In order to be better used in engineering, VVC coding needs to introduce a fast algorithm to reduce the time complexity. This paper proposes a method based on the fast algorithm of CU block division in the frequency domain assists the selection of the fast division mode by analyzing the texture features of the image to reduce the time complexity. At the same time, on this basis, optimization is also made to further reduce the time. It can be confirmed by the results that the algorithm can indeed reduce the time complexity with the least performance loss, and it is an effective algorithm. Keywords: VVC · Fast algorithm · Frequency domain

1 Overview VVC (Versatile Video Coding), also known as H.266 is a video compression standard finalized by the Joint Video Experts Team (JVET) on June 6, 2020 [1–3]. The main function of encoding and decoding technology is to improve the compression ratio as much as possible on the premise of ensuring the quality of the reconstructed video within the available computing resources, to achieve as much as possible on the premise that the user content accepts the loss. It can reduce the utilization rate of bandwidth. Especially in today’s era of more and more Internet applications, even if the network bandwidth has increased, but under the premise that various applications need to occupy network resources, network resources are still in short supply. Therefore, the encoding of video Efficiency plays a pivotal role in user perception, and excellent codec algorithms can greatly improve user experience [4–6]. Compared with the previous generation HEVC (H.265), VVC has better compression effects, and has many excellent features such as support for high-resolution and VR360° video, HDR, etc., but at the same time, the coding complexity of the algorithm is also It is greatly increased to about ten times, which means that the time complexity will be greatly improved [7, 8]. Therefore, reducing the time complexity of the algorithm is an urgent need for current projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 513–519, 2023. https://doi.org/10.1007/978-981-19-9968-0_62

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In the current research, to reduce the complexity of coding time, most of them are from the perspective of pixel domain, and the rules of pixel domain are extracted to assist CU block division, to achieve the purpose of reducing coding time. Later, I will propose a frequency-based method. The fast algorithm of CU block division in the VVC domain, in the block division, the frequency domain information of the current block is obtained first, and then the information obtained from the frequency domain is used as a reference to guide the CU block division steps in the VVC coding process, and eliminate errors as much as possible. In order to reduce the time complexity as much as possible on the premise of ensuring the video quality.

2 Basic Theory 2.1 VVC Coding Framework The framework is shown in the figure (Fig. 1):

Fig. 1. VVC coding framework

Overall, the coding process of VVC can be divided into several modules, including division module, prediction module, transform and quantization module, loop filter module and entropy coding module. Among them, the function of the division module is to divide the original image into several subblock units for encoding respectively, and the prediction module is to obtain the prediction block information for intraframe prediction or interframe prediction, and make a difference with the original image to obtain the residual Difference information [9]. For this killing information, after the transformation and quantization module, a set of change coefficients is obtained. This set of transformation coefficients is subjected to the final entropy encoding and compression by the entropy encoding module to obtain the bit stream [10]. It should be noted that the decoding end cannot get the original video. Therefore, when making predictions, the encoding segment should refer to the same reconstructed video signal as that obtained by the decoding end [11, 12]. So, inverse quantization and inverse transformation will be performed first, and then the

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reconstructed data will be filtered by the filtering module, and then pass this part into the prediction module for prediction [13, 14]. 2.2 DCT Transformation Discrete cosine transform is a transform related to the Fourier transform. There are eight standard types of DCT, of which the second type is often used in the fields of signal processing and image processing. The formula for DCT-II is as follows: The formula of two-dimensional DCT transform is as follows: The spectrogram of the image can be obtained by performing DCT transformation on a two-dimensional grayscale image. In the spectrogram, different coefficients have different meanings, respectively representing the proportion of a certain base image of the image (Fig. 2).

Fig. 2. DCT base image

2.3 Main Idea The division method is to calculate the RD-cost value of various division methods in a recursive way, and take the most optimal division method as the final block division method. The division methods include MTT (include BT and TT) and QT, and each includes horizontal and vertical division methods, so the speed of traversing all the division methods to obtain the optimal value will be very slow. Using the above DCT coefficients, if we can find the image in advance. The texture regularity can skip inappropriate division methods, thereby reducing the time complexity.

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3 Main Work and Contribution 3.1 Determine the Eigenvalues of the Current Block Define a texture feature energy value representing a block: Among them, represents the coefficient of the i-th row and the j-th column in the m-th DCT block. We mainly focus onand, here, and their values represent the energy values of the horizontal and vertical texture of the image respectively. In order to represent more conveniently and clearly the relationship between the horizontal and vertical features of the image, we define a new constant normalized image texture feature Therefore, the two coefficients are unified, and only one eigenvalue is used to represent the characteristics of the image. The definition of the normalized image texture eigenvalue R is as follows: 3.2 Determining the Coefficient R Threshold Perform mathematical statistics on several frames of a typical test sequence, compare the block division method of the original encoding with the results determined by yourself, and count the correct rates of different R values. The statistical results are as follows (Table 1): Table 1. The threshold of R is correct in the reference sequence test frame R

Correct rate

0.3

90.981%

0.4

94.066%

0.5

94.505%

0.6

95.385%

0.7

96.703%

0.8

96.703%

Because the threshold of this coefficient is to determine the way of block division, and block division is the first step of coding, therefore, we should try our best to ensure that this coefficient can achieve the highest possible accuracy. Once the number is bigger than 0.7, the accuracy rate will no longer be Therefore, we set the threshold to 0, 7 here. When R is greater than 0.7, we can determine his image texture. 3.3 Further Optimization By normalizing the image texture feature value R, the traversal process can be reduced by half if the conditions are met, but there are still places that can be optimized. For some images with obvious feature values, we can directly use the features of the current layer as the features of the next layer. In this way, the process of DCT transformation

A Fast Algorithm of CU Block Division Based on Frequency Domain on VVC

Fig. 3. Overall 8 * 8 local 4 * 4

Fig. 4. Overall 16 * 16 local 8 * 8

Fig. 5. Overall 32 * 32 local 16 * 16

Fig. 6. Overall 64 * 64 local 32 * 32

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can be reduced. The threshold of this obvious eigenvalue is also obtained by induction after mathematical statistics (Figs. 3, 4, 5 and 6). After statistics, combined with the test results after the actual parameter configuration, it is found that the performance of this strategy is not good in the 8 * 8 block, but the performance will be improved in other large blocks. According to the figure, we set the parameter as 0.9.

4 Experiment Result The current algorithm can achieve a time saving of nearly 20% with a time loss of less than 0.4%, and the average loss of 1% of BD-Rate can save nearly 43% of the time, while another algorithm compared with Although the time saved is more, the performance loss is too large, and the performance is not as good as this algorithm after comprehensive consideration (Table 2).

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Class Class A1 4K

Class A2 4K

Class B 1080P

Class C WVGA

Class D WQVGA

ClassE 720P

Name

BD-Rate(Y)

EncRate

BD-Rate(Y)

EncRate

Current algorithm

Reference [5] algorithm

Tango2

0.23%

92.14%

1.27%

47.09%

FoodMarket4

0.20%

86.55%

0.87%

46.45%

Campfire

0.12%

92.89%

1.09%

48.73%

CatRobot1

0.34%

85.07%

1.37%

47.19%

DaylightRoad2

0.31%

84.52%

1.51%

47.57%

ParkRunning3

0.07%

91.83%

1.27%

48.54%

Cactus

0.27%

86.83%

1.64%

49.09%

BasketballDrive

0.34%

79.59%

2.86%

40.13%

BQTerrace

0.60%

79.60%

1.82%

44.56%

BasketballDrill

0.64%

83.82%

2.04%

47.10%

BQMall

0.39%

81.03%

1.83%

49.68%

PartyScene

0.13%

89.83%

0.99%

50.07%

RaceHorsesC

0.22%

88.62%

1.17%

51.13%

BasketballPass

0.33%

83.27%

1.59%

45.33%

BQSquare

0.21%

88.68%

0.89%

50.60%

BlowingBubbles

0.18%

85.93%

1.12%

50.28%

RaceHorses

0.22%

87.88%

1.01%

47.99%

FourPeople

1.09%

81.30%

1.72%

47,27%

Johnny

0.50%

81.38%

1.89%

47.11%

KristenAndSara

0.48%

81.29%

1.56%

81.29%

Average

0.34%

85.60%

1.48%

49.47%

TS/BD-Rate

42.35%

34.13%

5 Conclusion Based on the VVC coding standard, this paper proposes a fast algorithm that guides block division through the frequency domain, and on this basis proposes an optimization scheme for the algorithm. On the premise, it can effectively reduce the time complexity of the encoding algorithm.

References 1. Xiong, Q., Gao, W., Teng, G.: H.266/VVC intraframe prediction CU fast partition control algorithm based on decision Dan tree. Ind. Comput. 34(07), 88–90+92 (2021)

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2. Zhang, H.: Research on Video Coding Standard H.266/VVC Intraframe Fast Algorithm. Huazhong University of Science and Technology (2021). https://doi.org/10.27157/d.cnki. ghzku.2021.003402 3. Huang, X., Shen, L.: Texture image classification based on DCT compression domain. J. Electron. Inf. (02), 216–221 (2002) 4. Zhang, D.: Research on Fast Algorithm for H.266/VVC Intra-Frame Coding. Chongqing University of Posts and Telecommunications (2021). https://doi.org/10.27675/d.cnki.gcydx. 2021.000998 5. Yang, H., Shen, L., Dong, X., et al.: Low-complexity CTU partition structure decision and fast intra mode decision for versatile video coding. IEEE Trans. Circuits Syst. Video Technol. 30(6), 1668–1682 6. Zhang, Q., Wang, Y., Huang, L., Jiang, B.: Fast CU partition and intra mode decision method for H.266/VVC. IEEE Access (2020) 7. Park, S.H., Kang, J.W.: Context-based ternary tree decision method in versatile video coding for fast intra coding. IEEE Access (2019) 8. Cho, S., Kim, M.: Fast CU splitting and pruning for suboptimal CU partitioning in HEVC intra coding. IEEE Trans. Circuits Syst. Video Technol. (9) (2013) 9. Sullivan, G.J., Ohm, J.-R., Han, W.-J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. (12) (2012) 10. Correa, G., Assuncao, P., Agostini, L., da Silva Cruz, L.A.: Performance and computational complexity assessment of high-efficiency video encoders. IEEE Trans. Circuits Syst. Video Technol. (12) (2012) 11. Yu, L., Chen, S., Wang, J.: Overview of AVS-video coding standards. Signal Process. Image Commun. (4) (2009) 12. Fitzek, F.H.P., Reisslein, M.: MPEG-4 and H.263 video traces for network performance evaluation. IEEE Netw. (6) (2001) 13. Liou, M.: Overview of the p×64 kbit/s video coding standard. Commun. ACM (4) (1991) 14. Kailath, T.: The divergence and bhattacharyya distance measures in signal selection. IEEE Trans. Commun. (1) (1967)

Optimization of AVS3 Intra Derived-Tree Mode Algorithm Based on DCT Coefficients Chenggang Xu, Fuyun Kang, Yi Wu, and Lei Chen(B) Beijing University of Posts and Telecommunications, Beijing 100876, China {xuchenggang,leichen}@bupt.edu.cn

Abstract. The audio and video coding standard AVS3 independently researched by China introduces many new technologies on the basis of AVS2, which greatly improves the coding efficiency, but at the expense of extremely high encode complexity. In order to promote the commercial use of the AVS3 coding standard, it is essential to reduce the coding complexity and strive to achieve real-time coding. Therefore, this paper proposes a novel fast algorithm for selecting intra prediction model based on Discrete cosine transform (DCT) coefficients in frequency domain, and thus reducing Derived-Tree mode traversal during intra Derived-Tree mode prediction. Finally, the experimental results show that our approach can reduce the time complexity by an average of 25% with only a 0.17% Bjøntegaard Delta Bit Rate (BDBR) increase, and in the case of higher resolution sequences and more complex texture, the overall performance is better, and the encoding time can be reduced by up to 40%. Keywords: AVS3 · Derived-tree mode · DCT coefficients · Optimization

1 Introduction With the scale of the video industry increasing progressively, video applications are constantly pursuing high frame rate, high resolution, and high compression ratio. In response to the above development trends, the AVS Working Group is developing the next generation of digital video coding standard AVS3 for Ultra-High-Definition video applications. It basically follows the core framework of block based on hybrid coding, including block division, intra-frame and inter-frame prediction, transformation, quantization, entropy coding, loop filtering and other modules. The latest tests of the reference software show that the performance of the AVS3 benchmark class is approximately 30% better than the previous generation standards AVS2 and HEVC, but at the expense of increasing the coding complexity. The complexity overhead of AVS3 primarily stems from its sophisticated block partitioning scheme that complements Binary-Tree (BT) and Extended Quad-Tree (EQT) split in order to better fit the local variations of the video signal, making it intractable to be implemented on real-time application in the field of high-definition and ultra-high-definition. Therefore, domestic and foreign researchers have proposed many fast algorithms in the field of video coding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 520–527, 2023. https://doi.org/10.1007/978-981-19-9968-0_63

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Generally, the traditional works for AVS3 complexity reduction can be classified two modules: coding block CU division and intra prediction mode selection. In the research of CU block partition optimization algorithm, some intermediate information are applied to early determine the CU partition before executing all the rate-distortion optimization (RDO), including texture features based on the current block, temporal correlation based on the corresponding position of adjacent frames, spatial correlation based on adjacent blocks and feedback of coding history information. Specifically, Reference [1] uses image pixel variance to measure the complexity of image textures. Reference [2] determines the depth range of the current CU according to the temporal correlation between the co-located CU and the current CU in adjacent frames. References [3] and [4] proposed to determine the optimal division method of the current block by using the division depths of adjacent coding units of the current block as reference, as well as the rate-distortion cost information of adjacent coded blocks. In [5], a Canny edge detector is applied to skip vertical or horizonal partition and carry out early termination. Reference [6] evaluates the current CU texture complexity through some coding history information such as Motion Vector (MV), adjacent block division depth, and skips unnecessary divisions in advance. Reference [7] propose a fast CU partition method for VVC based on random forest classifier model, which can distinguish smooth and complex area to carry out early termination. Reference [8] proposes a fast algorithm for intra-frame prediction pattern selection based on image DCT coefficients, which achieves good results and is worthy of continued research. The intra-frame prediction of the AVS3 standard expands the intra angle prediction mode while adopting the Intra Derived Tree (Intra DT) mode proposed by Tsinghua University [9]. Figure 1 is the diagram of block division after adding DT mode. The scheme introduces the concept of predicting unit (PU) on the basis of Coding Unit (CU), and adds six new block division methods, namely horizontal derivation mode (2N × nU, 2N × nD, 2N × hN) and vertical derivation mode (nL × 2N, nR × 2N, hN × 2N).To be more specific in the horizontal derivation mode, 2N means that the width of the PU and the CU is equal, hN means that the height of the PU is equal to a quarter of the CU, and nU indicates that the current division is asymmetrical with the small pieces at the top. The scheme cooperating with the QTBT and EQT divisions produces richer block divisions, so as to accommodate more complex textures and motion features, improving coding performance but at the expense of increasing complexity. For reducing the complexity of the DT partition mode selection process, the AVS3 reference software platform integrates a DT advance skip algorithm based on RD cost calculation, however, which mode is divided into is still a traversal selection process. The above process requires a lot of computing power and time complexity, therefore, there remains a lot of optimization space. Based on this, A calculation process of skipping unnecessary DT mode in advance based on the image features reflected by Discrete cosine transform (DCT) coefficients is proposed.

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Fig. 1. QTBT+EQT+DT coding diagram

2 Proposed Algorithm 2.1 DCT and Its Coefficients The DCT transform is an orthogonal transform, which is a lossless transform and causes no loss to the image after the transform, and the distribution of its coefficient matrix has certain characteristics. Figure 2 shows a DCT base graph of size 8 × 8. The base image represents the frequency of image changes in horizontal and vertical directions, and the spatial domain image pixel values can be combined by weighting the base image, with the DCT coefficients being its weight values. After the image block undergoes a twodimensional DCT transformation, its energy is mainly concentrated on the DC and lowfrequency AC components in the top left corner, and the vast majority of the remaining AC components are zero. Where the value of the (0, 0) position is the DC coefficient, which represents the average brightness of the current image block, and the size of the

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DC coefficient between different image blocks can be used to determine the similarity between the image blocks. In addition, the high-frequency base image represents the details of the image, specifically, the higher the value of the high-frequency part of the DCT coefficient matrix, indicating the more components of the high-frequency base image, the richer the image detail and the higher the texture complexity. More importantly, in the first row, all base images have a horizontal change frequency of 0 and appear as vertical textures. Therefore, all the values in the first row of the coefficient matrix (except the DC coefficient) can represent the vertical direction texture intensity of the current image block, Similarly, the horizontal texture intensity of the current image block can be represented as values in the first column of the coefficient matrix (except for the DC coefficient).

Fig. 2. A DCT base image of size 8 × 8.

Based on the above and the paper [10], the texture intensity of the block to be predicted can be denoted vertically and horizontally respectively as Energyv , Energyh so as to derive the approximate angle between the image texture and the horizontal direction θ as shown in Eq. (3). Energyv = Energyh = θ = tan

N −1 v

N −1

−1

v



|F(0, v)|

(1)

|F(u, 0)|

(2)

Energyv Energyh

 (3)

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2.2 Proposed Algorithm According to the characteristics of the Intra DT mode division, the newly proposed algorithm is divided into two steps. First of all, the direction of the DT is required, determining whether the current PU is suitable for horizontal derivation mode or vertical derivation mode, so as to skip the three division modes in different directions. Then select the optimal division method under the current division direction within the selected derivation mode range. When entering the intra-frame angle prediction mode selection, take the smaller value between the height and width of the current block to be predicted as the DCT transformation kernel size. Split the current block into square blocks for DCT transformation, and calculate the texture angle of each block according to (1) (2) (3). Finally, calculates the texture angle θ of the block by weight coefficients. If θ < 40°, the three derivation modes in the vertical direction are skipped; If θ > 50°, the three derivative modes in the horizontal direction are skipped; If 40 < θ < 50, nothing is done. The method mentioned above can basically determine the DT direction of the current PU. Take the three partitioning methods of the vertical derivation mode as an example, the division of nL × 2N and nR × 2N indicates that the pixel distribution characteristics on the left side of the current block are quite different from the right side. Specially, the three quarters of the pixel distribution features on the right side of nL × 2N are more similar, while the nR × 2N represents that the pixel distribution characteristics of the left three quarters are more similar. And the variance of the DC coefficient can be used to characterize its similarity. According to the analysis, the complete algorithm optimization flow is showed in Fig. 3 and the details of the algorithm is described as follows: 1. Calculate its texture angle θ for the current CU to determine its DT direction, and carry out the subsequent process in two cases according to the determination result. 2. When the determination is vertical or horizontal direction, taking the vertical direction as an example. – 2.1. Skip the three DT modes in horizontal direction, index 1, 3 and 4 in Fig. 3. – 2.2. Calculate the variance of DC coefficients varianceL , varianceR for two equal parts of the current CU. – 2.3. If varianceL > varianceR × 1.05, the texture similarity of the left part of the current block is smaller than the right part, and the small block in the asymmetric division should favor the part where the left block is located, then the pattern of the small block on the right is skipped, index 6 in Fig. 4. – 2.4. The remaining modes are determined by the selection process processing in the original standard code. 3. When the determination is not to give a clear direction. – 3.1. Handled by the selection process in the original standard code.

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Fig. 3. Algorithm optimization flow chart

Fig. 4. DT mode skip schematic

– 3.2. When the original algorithm needs to perform the traversal calculation of the patterns corresponding to index 3, 4, 5, and 6, the calculation is compared for patterns index 3, 4, and 5 and 6 according to 2.2 and 2.3, respectively. – 3.3. The remaining modes are determined by the selection process processing in the original standard code.

3 Experimental Results The proposed algorithm is implemented and tested on AVS3 official reference software HPM6.0. For the hardware environment, all of the experiments are conducted on the

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Intel(R) Core(TM) i7-7700K CPU at 4.20 GHz with 16 GB RAM. QP are set as 27, 32, 38, 45 and all experiments are carried out on the All-Intra configuration under the common test conditions (CTC). Bjøntegaard Delta Bit Rate (BDBR) and time saving (TS) are used to measure the overall performance of our algorithm. TS of each test sequence is computed by (4). Tanchor is the encode time under HPM 6.0. TS =

Tanchor − Tproposed × 100% Tanchor

(4)

Table 1. The experimental results of the proposed algorithm over HPM 6.0 Class A

B

E

Resolution 2840 * 2160

1920 * 1080

1280 * 720

Average

Sequences

BDBR (%)

Time saving (%)

Y

U

V

Tango2

0.12

0.62

0.45

30

ParkRunning3

0.03

0.03

0.07

31

Campfire

0.11

0.15

0.07

28

DaylightRoad2

0.51

0.89

0.65

40

UHD average

0.19

0.42

0.31

32

Cactus

0.17

0.21

0.29

29

BasketballDrive

0.21

0.40

0.67

35

MarketPlace

0.04

0.19

0.28

27

RitualDance

0.12

0.31

0.59

30

1080P average

0.14

0.28

0.46

30

City

0.05

0.66

0.25

16

Crew

0.17

0.81

0.42

14

vidyo1

0.22

0.90

0.77

10

vidyo3

0.31

0.70

0.70

12

720P average

0.19

0.77

0.54

13

0.17

0.49

0.44

25

Table 1 shows the result of the proposed algorithm under the All-Intra configuration. As can be seen, with the proposed algorithm, the encoding time is reduced by about 25% while the increase of BDBR is only 0.17%. It is effective for all of the test sequences with the proposed algorithm, especially for sequences with higher resolution.

4 Conclusion In this paper, we put forward a novel fast algorithm for Intra DT mode determination based on the characteristics of frequency domain DCT coefficients. The algorithm skips a

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lot of the traversal calculations, and the calculation amount of the new skipping method is very small compared to the original standard code. Compared with the original HPM, our approach reduces encoding time by 25% with only a 0.17% BDBR increase, and in the case of higher resolution sequences and more complex texture, the overall performance is better, and the encoding time can be reduced by up to 40%.

References 1. KaiHong, H.: Study on CU Partitioning Fast Algorithm and RDO Optimization of AVS2 Intra-Frame Predictive Coding. Southwest Jiaotong University (2016) 2. Shen, L., Zhang, Z., Liu, Z.: Effective CU size decision for HEVC intracoding. IEEE Trans. Image Process. 23(10), 4232–4241 (2014) 3. Yang, W., Fan, X., Liang, Z., et al.: A fast intra coding algorithm for HEVC. In: ICIP. IEEE (2014) 4. Luo, X., Liu, Z.: HEVC efficiency improvement algorithm based on fast coding unit division and adaptive mode decision making. Electron. Technol. (2020) 5. Tang, N., et al.: Fast CTU partition decision algorithm for VVC intra and inter coding. In: 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp. 361–364 (2019) 6. Ahn, S., Lee, B., Kim, M.: A novel fast CU encoding scheme based on spatiotemporal encoding parameters for HEVC inter coding. IEEE Trans. Circuits Syst. Video Technol. 25(3), 422–435 (2015) 7. Zhang, Q., Wang, Y., Huang, L., Jiang, B.: Fast CU partition and intra mode decision method forh.266/VVC. IEEE Access 8, 117539–117550 (2020) 8. Kang, F., Chen, L., Xu, C., Tao, X., Liu, Z.: Fast intra prediction algorithm for AVS3 based on DCT coefficients. Video Eng. 45(08), 90–94 (2021) 9. Wang, L., Niu, B., Wei, Z., et al.: CE1: intra derived tree mode. In: AVS M4540 (2018) 10. JunJuan, L., Yu, L.: Research on DCT domain HEVC intra-frame predictive pattern mapping algorithm. Telev. Technol. 39(3), 64–67 (2015)

Integrated Sensing and Communications with OTFS: Application in V2I Communications Zhilei Ling(B) , Yuxin Wu, Guanyu Zhang, and Songlin Sun Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected]

Abstract. Orthogonal time frequency space (OTFS) modulation is viewed as a promising technique to realizing integrated sensing and communications (ISAC) as the physical parameters can be directly extracted through the delay Doppler domain channel. In this paper, we investigate the sensing ability of OTFS signals to assist vehicle-to-infrastructure (V2I) communications. Specifically, we propose a new scheme to alternatively sense wide beam and narrow beam to vehicles, where the wide beam is adopted to estimate and refine the sensed parameters with multiple echoes. Then, the RSU can formulate the narrow beam according to the predicted parameters for effective communication. Finally, we show the performance of our method, which attains effective communications. Keywords: Integrated sensing and communications · Orthogonal time-frequency space · Vehicle networks

1 Introduction In the future, wireless networks are expected to support an extensive range of applications for mobile terminals, from people connections to Internet-of-things (IoT). With the development of unmanned driving, many derivative technologies such as in-vehicle operating systems, traffic management, intelligent transportation system, and in-vehicle data interaction have become the new commanding heights of the automotive industry, which has also contributed to the development of vehicular communication [1, 2]. Due to the integration gain and coordination gain of integrating sensing and communication, two fundamental functionalities of vehicular application changing from separation to integration, which result in the emergence of integrated sensing and communication (ISAC) [3]. Initially, ISAC was implemented by radar, namely, dual-functional radar and communication (DFRC). Later, as the application of ISAC in vehicle-to-infrastructure communication became more and more extensive, its implementation method was gradually explored and developed, such as vision-aided communication and OTFS. The intent of ISAC is to extract environmental data from dispersed echoes while radio emissions transport communication data. Generally, the approaches to realize ISAC can be split into two categories: spectrum sharing and joint designs. The former regards sensing and communication as two separate functions and tends to avoid interference between radar sensing and communication under the same spectrum or © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 528–537, 2023. https://doi.org/10.1007/978-981-19-9968-0_64

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resources [4]. Nevertheless, it’s inevitable to cause performance attenuation due to the existence of interference. On the other hand, the joint design approaches aim to design a unified waveform that is capable of achieving both two functionalities [5]. The joint design approaches have shown great potential in balancing the trade-off between S&C, achieving higher spectrum-/energy-efficiency [6, 7]. As a new research hotspot to realize ISAC, aiming to solve the Doppler frequency shift problem of traditional OFDM waveforms in high-speed mobile situations, the orthogonal time-frequency space (OTFS) technique receives much attention, which modulates data in the delay-Doppler (DD) domain rather than the conventional timefrequency (TF) domain, implemented by the symplectic finite Fourier transform (SFFT) and inverse symplectic infinite Fourier transform (ISFFT) [8]. OTFS technology is proposed to solve the Doppler frequency offset generated when the terminal moves at high speed and is one of the critical technologies to promote integrated sensing and communications [9]. Relying on the DD domain signal representation, OTSF provides a perspective to find the relationship between information symbols and wireless channels for information transmission [10]. By unitary transformations from the TF domain to the DD domain, we can get some properties through analysis, such as signal separability, compactness, stability, and possibly sparsity, which can be leveraged to channel estimation with high accuracy and low training costs as well as low-complexity signal detection [11]. In order to establish a dynamic vehicle network, RSU can obtain vehicle-related information through the OTFS-ISAC signals in the area covered by the RSU signal, such as the estimates of delays, Dopplers, and angles associated with vehicles. Furthermore, RSU can improve the anti-interference performance of uplink transmission and downlink transmission by predicting channel parameters to obtain better error performance [12]. For the pencil-sharp mMIMO beams, it’s of vital importance for beam tracking to estimate the precise location of the communication receiver through the overall sensing of the vehicle. However, sensing the vehicles with fixed beamwidth and single sensing procedure cannot provide high-resolution sensing to locate the CR, leading to mismatch between the position of real CR and the estimated position. Especially when the vehicles are moving fast around the RSU, the CR may not be covered by the narrow beam, causing significant performance loss. To solve this problem, we propose to utilize the sensing functionality of OTFS signals to assist mmWave communication. First of all, we sense the parameters with a wide beam to estimate the precise position of the CR. Then, the motion state can be predicted through OTFS-ISAC signals. The wide beam is also implemented for further estimation and refinement through the communication process. To validate the effectiveness of our proposed method, we also carry out simulations to compare the performance. Numerical results demonstrate that our proposed method has an excellent performance to sense vehicles and then estimates channel parameters, resulting in effective communication.

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2 Related Work One of the key steps to effectively implement ISAC is predictive beamforming, which may significantly affect beam alignment, further influencing overall communication rates. In this section, we intend to introduce some technology about predictive beamforming, which is widely used in the field of vehicle networking. 2.1 Radar-Based Predictive Beamforming In order to solve the spectrum resource congestion problem, dual-functional radar and communication (DFRC) have aroused wide public concern. Utilizing the radar capabilities of the RSU can greatly reduce beam tracking overhead, which can significantly outperform techniques based on only communication feedback in both angle tracking and downlink communication due to the matched filter gain. For example, du Zhen proposed a dynamic predictive beamforming scheme using the “stop-go” radar model to realize ISAC in [13]. Moreover, it innovatively treats the vehicles as extended targets, to be more exact, is that dividing the vehicle into uniformly distributed scatterers and estimating the parameter of RSU by these scatterers. 2.2 Vision-Based Predictive Beamforming Inspired by machine learning using visual data, people combine vision and communication to sense the channel’s physical parameters and the environment’s situation more precisely. Most of the existing solutions are based on data analysis and machine learning from the massive amount of captured visual data for beam training and predictive beamforming to acquire higher performance and lower overhead [14]. In [15], Weihua Xu proposed a scheme novel beam alignment framework using 3D object detection techniques and deep neural network (DNN) to improve the performance of V2X communications. Shuaifeng Jiang proposed a machine learning (ML) framework based on an encoder-decoder architecture to predict the beams using the visual sensing information obtained from camera capture in [16]. Furthermore, vision-aided communication technology is applied in blockage prediction, which is a crucial challenge, especially for mmWave and terhertz networks due to the dependence of these scenes on line-of-sight (LOS). By means of a deep learning algorithm that can learn from visual information to predict future blockages and take appropriate measures such as hand-off initiation [17]. 2.3 OTFS-Based Predictive Beamforming As mentioned before in introduction, OTFS has unique advantages in channel estimation resulting from its transform domain. In practical application of implementing ISAC, it is natural to employ OTFS to estimate the channel, sensing the kinematic parameters of vehicles and carry out predictive beamforming. In [10], a maximum likelihood scheme is proposed and combined with hybrid beamforming to perform object detection and parameter estimation jointly. While in [11], by using the OTFS-ISAC signal, the RSU can predict the beam at the next moment and make corresponding changes to the beamformer, consequently can omit the step of channel estimation, further reducing relay on the basis of [10].

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3 System Model As demonstrated in Fig. 1, we take a possible scenario into account. In the scenario, the RSU is equipped with a uniform linear array (ULA) with transmit antennas and receive antennas, denoted by Nt and Nr , respectively, to provide communication service to vehicles in the scenario equipped with single antenna. Simultaneously, we set the road as the x-axis and the direction perpendicular to it as the y-axis. Furthermore, we assume that the vehicle is moving along the road which is parallel to the ULA.

Fig. 1. A diagram of the considered vehicular network.

3.1 Signal Model First, we give the transmit steering vectors of the RSU a(θ ), which can be expressed as  T 1  jπ sin θ 1, e a(θ ) = , . . . , ejπ (Nt −1)sin θ . (1) Nt where θ is the angle between the location of the vehicle relative to RSU and the y-axis. Since the ULA is parallel to the road, the angle of arrival (AoA) and the angle of departure (AoD) are identical. Similarly, the receive steering vectors can be expressed as  T 1  jπ sin θ 1, e , . . . , ejπ (Nr −1)sin θ . (2) b(θ ) = Nr Then the received echoes e(t) reflected by the vehicle can be expressed as √ e(t) = ς pαb(θ )aH (θ )˜c(t − ε)ej2π μt + w(t),

(3)

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where ς , p and α denote array gain factor, the allocated transmit power and the reflection coefficient, respectively. And ε and μ denote the round-trip time-delay and the Doppler frequency of the vehicle. w(t) ∈ CNr ×1 represents the complex additive white Gaussian noise with zero mean and variance σ12 . c˜ (t) denotes the transmitted signal that is formulated as c˜ (t) = pc(t), p ∈ CNt ×1 is the transmit beamforming vector, which is   where determined as p = a θ , where θ denotes the estimated value of the angle-of-departure (AoD), and c(t) denotes the data symbol at time-slot t. 



3.2 Communication Model We use OTFS modulation for the downlink transmission. The transmitted symbols are mapped to M × N data symbols in the Delay-Doppler (DD) domain, where M and N denote the number of subcarriers and the number of time slots in a frame of OTFS, respectively. The transmitted signal is transformed from the DD domain to the timefrequency (TF) domain by inverse symplectic finite Fourier transform (ISFFT), which can be formulated as follows [18] k=0 l=0

1

X [n, m] = √ MN

N −1 M −1  



x[k, l]e

j2π

nk ml N −M



,

(4)

where x[k, l] denotes the data symbol in the DD domain signal. k and l denotes the time-delay and the Doppler indices of the DD domain, respectively. X [n, m] denote the data symbol in the TF domain signal, where n and m denote the time and the frequency indices, respectively. Then, the transmitted signals in the time domain can be generated by Heisenberg transform, which is expressed as [19] m=0 n=0

ci (t) =

M −1 N −1  

Xi [n, m]ej2π mf (t−nT ) gtx (t − nT ),

(5)

where gtx (t) is the transmit filter. T and Δf denote the duration of a time slot and frequency spacing, respectively. The downlink channel model h(t) can be expressed as c ej2π μt aH (θ ), (6) h(t) = 4π fc d 2 where θ and d represent the angle and the distance between the RSU and the CR of the vehicle, respectively. fc , c and μ denote carrier frequency, light speed and the Doppler shift, respectively. Then the received OTFS-ISAC signal at the vehicle transmitted by the RSU can be presented as   y(t) = h(t)a θ c(t) + z(t). (7) 



where z(t) denotes the complex additive white Gaussian noise with zero mean and variance σ22 .θ represents the estimated angle of CR. 

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4 Parameter Estimation and Prediction In this section, we propose a scheme of predictive beamforming design by estimating and predicting kinematic parameters of vehicles using OTFS-ISAC signals to obtain a better performance of downlink communication. First, we demonstrate the detailed process of parameter estimation. Then we present the procedure of parameter prediction. 4.1 Parameter Estimation 4.1.1 Estimation of Time Delay and Doppler The delay and Doppler can be estimated by matched filter on RSU [13]. When the matched filter output the peak, the RSU can obtain the time delay and Doppler at this point as an estimate, denoted as ε and μ. 



4.1.2 Estimation of Reflection Coefficient χ The reflection coefficient o α is calculated as α = 2d , where χ is the radar cross section (RCS) [20]. Meanwhile, since the time delay is equal to the quotient of the round-trip distance and the signal transmission speed, we can use the formula ε = 2d c to obtain the estimate of the reflection coefficient, given by 

α=

χ . cε

(8)



4.1.3 Estimation of Angle By substituting the reflection coefficient values into the previously matched filtered echoes, we can obtain   √ e(t) = ς pGαb(θ )aH (θ )a θ + w(t) (9) 

We can obtain the θ that maximizes the likelihood function L(θ | e) as the angle estimate θ , formulating as 



θ = argmaxL(θ | e).

(10)



Noted that when determining the angle estimate θ , we can pick values within a small range around the angle prediction θ to simplify the calculation. 4.1.4 Estimation of Location and Velocity 





After the estimated values of the three basic parameters ε, μ and θ , we can obtain the estimated values of the location and velocity by using the formulas. We define ux and uy as the x-axis and y-axis coordinates of the vehicle, respectively. ux,o and uy,o is the x-axis and y-axis coordinates of the RSU. Next, the prediction of the position can be

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completed using the time delay and angle estimates, expressed as ux = ux,o + cεsinθ 2 cε cosθ and u = u + . 

 



y

y,o

2

Similarly, the velocity estimates can be obtained using the Doppler and angle esticμ , where the positive or negative values of v represent the vehicle mates with v = 2fc cosθ moving in the positive or negative directions along the x-axis. 







4.2 Prediction of Location, Distance and Angle Based on the above estimates of the sensing parameters, the RSU can predict the values of parameters such as position, distance, angle, etc. The prediction for the location parameter is expressed as 



u¯ x = ux + ΔT · v

(11)

where ΔT is an OTFS frame duration, which is the predicted time interval. Meanwhile, since the time interval ΔT is very short. We can assume that the velocity at the next moment is equal to the velocity at the current moment, i.e. the predicted value of the velocity is equal to the estimated value at the present moment. The formula is s = s. In addition, the predicted value of the distance and angle can be expressed as

 2  2 u¯ x − u¯ x,o + uy − uy,o , d= (12) 

θ = arcsin

u¯ x − ux,o d

.

(13)

Note that θ is the most important parameter in the predicted value. The RSU can control the transmit beamformer to transmit the beam in the specified direction. The channel h¯ gain and Doppler shift μ¯ can be predicted using the formula h¯ = c 4π fc d

 vcos θ fc and μ¯ = , respectively. c 



5 Simulations In this section, we demonstrate the results of experiments supported by our proposed method to verify its effectiveness. We select 30 GHz as the carrier frequency of the RSU used in the model. Furthermore, we assume that the duration of completing single simulation process is ΔT = 0.02 s, and as for simulation time instant, we choose tsim = 160. The number of transmit antenna Nt and the number of receive antenna Nr are both configured at 32. As usual, the length of common cars on the road is between 4 m and 6 m. Therefore, we assume that the vehicle used in the method as a cuboid of 1.8 * 5 m. Furthermore, we set the coordinate of the RSU is (0 m, 0 m), and the initial position of the vehicle in the established coordinate system is (20 m, 40 m), and the speed at which a car travels at a constant speed is 10 m/s, moving forward along the x-axis.

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5.1 Performance of Parameters Estimation The comparison between the real angle and the estimated angle of CR is shown in Fig. 2(a). We can observe that the estimated angle of CR oscillates periodically around the true angle figure, which may be due to the periodicity of the sine function. In Fig. 2(b), we present the comparison between the real location and the estimated location. As the vehicle travels at a constant speed along the road parallel to the ULA, its real location curve correspondingly appears as a straight line, while the estimated location curve also fluctuates periodically around the real location resulting from the calculation formula.

Fig. 2. (a) The estimated angle of CR. (b) The estimated location of CR.

5.2 Performance of Downlink Communication The received SNR after the pre-qualization process can be represented as follows 2 

  √  pNt aH (θ)a θ x[k,l]   

SNR =

σ22 

2   H a (θ)a θ    

= pNt E

(14)

σ22

where E and p denote the data symbol power and the allocated transmit power, respectively. In Fig. 3, we give the SNR of our proposed method obtained by Eq. (14). From the figure, we can see that the value of SNR fluctuates between 25.4 dB and 27.2 dB. The SNR achieve a maximum value of 27.09 dB at the 10 time instant.

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Fig. 3. SNR of the proposed method.

6 Conclusion In this paper, we proposed a new scheme to realize V2I downlink communications with ISAC-OTFS signals. Due to the unique characteristic of OTFS, we can estimate the delay and Doppler shift more precisely. Then we predict the physical parameter of vehicle and adjust the beamformer accordingly. Furthermore, we adopt dynamic beamforming, that is to say, using wide beams to search vehicles while shifting to narrow beams to track vehicles and implement downlink communication efficiently. Numerical results are exhibited to show the accuracy and validity of our method.

References 1. Paul, A., Afia, A.: Survey of interoperability challenges in the internet of vehicles. IEEE Trans. Intell. Transp. Syst., 1–24 (2022) 2. Dimitrakopoulos, G., Demestichas, P.: Intelligent transportation systems. IEEE Veh. Technol. Mag. 5(1), 77–84 (2010) 3. Yuanhao, C., Fan, L.: Integrating sensing and communications for ubiquitous IoT: applications, trends, and challenges. IEEE Netw. Mag. Glob. Internetw. 35(5), 158–167 (2021) 4. Grossi, E., Lops, M., Venturino, L.: Energy efficiency optimization in radar-communication spectrum sharing. IEEE Trans. Signal Process. 69, 3541–3554 (2021) 5. Liu, F., Zhou, L., Masouros, C., et al.: Toward dual-functional radar-communication systems: optimal waveform design. IEEE Trans. Signal Process. 66(16), 4264–4279 (2018) 6. Zou, J., Cui, Y., Liu, Y., et al.: Energy efficiency optimization for integrated sensing and communications systems. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 216–221. IEEE (2022) 7. Zou, J., Cui, Y., Liu, Y., et al.: Optimal energy-efficient beamforming for integrated sensing and communications systems. In: 2022 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 01–05. IEEE (2022) 8. Wei, Z., Yuan, W., Li, S., et al.: Orthogonal time-frequency space modulation: a promising next-generation waveform. IEEE Wirel. Commun. 28(4), 136–144 (2021)

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9. Gaudio, L., Kobayashi, M., Caire, G., et al.: On the effectiveness of OTFS for joint radar parameter estimation and communication. IEEE Trans. Wirel. Commun. 19(9), 5951–5965 (2020) 10. Gaudio, L., Kobayashi, M., Caire, G., et al.: Hybrid digital-analog beamforming and MIMO radar with OTFS modulation. arXiv preprint arXiv:2009.08785 (2020) 11. Yuan, W., Wei, Z., Li, S., et al.: Integrated sensing and communication-assisted orthogonal time frequency space transmission for vehicular networks. IEEE J. Sel. Top. Signal Process. 15(6), 1515–1528 (2021) 12. Li, S., Yuan, W., Liu, C., et al.: A novel ISAC transmission framework based on spatiallyspread orthogonal time frequency space modulation. IEEE J. Sel. Areas Commun. 40(6), 1854–1872 (2022) 13. Du, Z., Liu, F., Yuan, W., et al.: Integrated sensing and communications for V2I networks: dynamic predictive beamforming for extended vehicle targets. arXiv preprint arXiv:2111. 10152 (2021) 14. Zou, J., Mei, K., Sun, S.: Multi-scale video inverse tone mapping with deformable alignment. In: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9–12. IEEE (2020) 15. Weihua, X., Feifei, G.: Computer vision aided mmWave beam alignment in V2X communications. arXiv:2207.11409 [eess.SP] 16. Shuaifeng, J., Ahmed, A.: Computer vision aided beam tracking in a real-world millimeter wave deployment. arXiv:2111.14803 [eess.SP] 17. Gouranga, C., Muhammad, A.: Vision-aided 6G wireless communications: blockage prediction and proactive handoff. IEEE Trans. Veh. Technol. 70(10), 10193–10208 (2021) 18. Hadani, R., Rakib, S., Tsatsanis, M., et al.: Orthogonal time frequency space modulation. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2017) 19. Raviteja, P., Phan, K.T., Hong, Y., et al.: Interference cancellation and iterative detection for orthogonal time frequency space modulation. IEEE Trans. Wirel. Commun. 17(10), 6501– 6515 (2018) 20. Ngo, H.Q.: Massive MIMO: Fundamentals and System Designs. Linköping University Electronic Press (2015)

Space Technology Session 1

Fault Analysis and Evaluation of Control Moment Gyroscope on Orbit Jinfeng Zhou(B) , Xi Chen, and Boneng Tan Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. CMG is the main actuator of the agile spacecrafts, its stable performance on orbit is particularly important for the spacecraft, as CMG’s failure usually leads to long-term mission interruption or attitude-loss, it usually took several hours to get CMG back to work. In this paper, we analyze the cases of failure, and summarize CMG’s characteristics and mechanism. Then, based on the actual state data on orbit, a comprehensive evaluation and prediction method based on data transformation is proposed to quantitatively evaluate the operation performance of CMG. Keywords: CMG · Fault analysis · Evaluation · Prediction

1 Preface Control moment gyroscope (CMG) is an important attitude actuator of agile spacecraft, with large output of torque, fast response speed and low energy consumption, and is widely used nowadays at home and abroad, such as “ISS” and Tiangong. Taking CAST as example, in 2011, there were 6 applications on orbit, which increased sharply to nearly 100 in 2018, meanwhile the angular momentum rising from 5 nms to 200 nms. CMG is generally composed of high speed rotor and low speed frame, according to the numbers of freedom, it be divide into single frame CMG and double frame CMG; If the speed of high-speed rotor is variable, we call it variable speed CMG [1]. As Fig. 1, the high-speed rotor provides constant or adjustable angular momentum by rotating at high speed; The low-speed frame generates the angular momentum in the required direction through the rotation angle. Figure 2 gives a classical configuration of CMG. CMG group receives the command of the control system, and exchanges the angular momentum with the spacecraft by changing the angular momentum vector direction of each CMG, in order to quickly and efficiently change the attitude of the whole spacecraft. The stable performance of CMG on orbit is very important for spacecraft. Due to its failure often leads to long-term interruption of mission or attitude-loss of the spacecraft, and it often takes several hours for recovery. In the bad cases, degradation of spacecraft performance is inevitable [2]. During the operation of The International Space Station, some of CMGs went wrong partially, and finally CMG-1 was removed and replaced in 2005 [3]. At present, many researchers focus on theoretical of the attitude control © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 541–550, 2023. https://doi.org/10.1007/978-981-19-9968-0_65

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Fig. 1. CMG structure

Fig. 2. The classic installation configuration

in the case of some CMGs go wrong. Sands and Kim designed an optimal installation matrix of non singular angular momentum space based on the non redundant three single frame control, and realized the attitude maneuver with three CMGs based on their design [4]. Shi Yongli proposed a hall fault detection and signal reconstruction method in the case of variable speed, from the hall signal monitoring of high-speed rotor [5], Feng Ye proposed a method based on parity vector to monitor the consistency of the rotation speed of each frame. That indicates which one is out of order once the consistency is destroyed, and it needs to be isolated immediately [6]. However, the research above is few about the fault mechanism and evolution process of CMG. In this paper, based on the analysis of two kinds of common faults on orbit, a comprehensive evaluation and prediction method based on data transformation is proposed to evaluate the performance of CMG on orbit.

2 Fault Analysis on Orbit From the perspective of CMG evaluation, the main characteristic parameters of high speed rotor are mainly as the following: motor current, high-speed rotor speed, bearing temperature of high-speed rotor. The main characteristic parameters of CMG low-speed part are low-speed motor current, frame speed and bearing temperature of frame. A lot of experiments and engineering practice show that there are mainly two causes of CMG fault on orbit: the abnormal increase of local impedance of low-speed frame conductive ring, which leads to the abnormal detection signal of frame angle, and finally makes the whole system jump into safe mode; The other cause is the accumulation of debris between the high-speed bearing and the lubrication system, which leads to the increase of bearing friction torque, and the controller can not stably hold the speed of rotor, till the high-speed rotor stop working and the CMG shutdown. This section further analyzes the mechanism of these two causes.

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2.1 Abnormal Increasing of Local Impedance in Low Speed Frame Conductive Ring In CMG, permanent magnet synchronous motor is adopted as driver of low-speed frame, and inductosyn servers as the detection element of frame angle, then a control system with three closed-loop of position, speed and current is formed. The schematic diagram of frame angle detection is shown in Fig. 3 below. The excitation chip provides a necessary excitation signal for the synchronizer rotor (the effective value is 1 Vrms, the frequency is 10 kHz sine signal, which is transmitted to the synchronizer rotor through the conductive ring). The output signal of the inductosyn is sent to the signal demodulation chip through the signal conditioning circuit, and the frame angle digital signal is obtained through AD conversion. DSP couples the angle data of roughing and finishing machine to get the detection data of frame angle. Inducetosyn

Exited signal generator

Exited signal

Conductive ring 12

Amplifier GND

Conductive ring 14

Rotor excitaon winding

Fine winding

Fine angelmeasured chip Angel output

DSP+FPGA Rough angelmeasured chip

Rough winding

Stator fine signal processor Stator rough signal processor

Fig. 3. Principle block diagram of low-speed frame angle detection

In case of over flying, safe mode is set up to avoid the abnormal angle detection of low speed frame. The self-test program of frame angle runs every 500 μs. Once the frame angle detection system is found to be abnormal, it meets the criteria for entering safety mode, CMG will automatically jump into the safe mode. In this mode, the frame is in the state of motor current locking to prevent the frame from flying and other accidents. If the impedance of the conductive ring increases abnormally, the amplitude of the excitation signal of the rotor excitation winding will be greatly attenuated due to the resistance voltage division effect, and the stator can not sense any electromagnetic signal, which will lead to no output of the stator and the failure of angle detection. In the end, the whole CMG system will enter the safe mode. For example, a type of CMG, the equivalent load impedance of the excitation winding of low speed frame is 5.6 , The ground test tell us that if the sum impedance of rotor exciting winding and conductive ring is greater than 8.9 , the signal output of synchronizer will be affected. To ensure reliability and security, safety mode criterion will be triggered as long as the impedance of conductive ring is abnormal for more than 500 μs. Once the number of trigger criteria exceeds 7 times in 2 s, the synchronizer will enter the safe mode immediately. The fault phenomenon is shown in Fig. 4. The motor current and speed instantly drop to zero, and there was no previous accumulation process before.

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Fig. 4. The motor current and speed drop to zero instantly

Accumulation of wear debris often leads abnormal increase of local impedance of conductive ring. Due to the pressure between the contact surfaces, local plastic deformation, dislocation slip and aggregation are produced in the subsurface layer of the material during the relative sliding process of ring and brush of conductive ring. At the same time, many vacancies and microcracks are produced, which make the surface layer structure loose and soften, and the formation of the soft layer will seriously weaken the wear resistance of the alloy. After long-term use, the wear debris generated from conductive ring forms a high and local wear debris layer in the contact area under the micro motion condition of small swing of CMG frame. When the distribution and accumulation of wear debris makes the contact points of the ring brush contact surface decrease and the contact point radius decrease, the path impedance increases. It is reversible that the local impedance of the conductive ring increases due to the accumulation of wear debris. Because of the weightlessness on orbit, the wear debris in the area near the brush contact in the conductive ring groove may be redistributed, so the brush contact gets better, and the brush impedance returns to a normal state in the following aspects: (1) When the CMG frame swing with a large angle or turn in the opposite direction, the wear debris near the brush contact area in the conductive ring groove is redistributed driven by the brush wire. (2) Through running in one direction, the wear debris in the ring groove near the ring brush contact area is redistributed under the drive of the brush wire, and the high points accumulated near the ring brush contact area are eliminated. (3) By changing the center position of the frame swing, debris distribution may get a new distribution state. 2.2 Friction Moment of Bearing Increases Due to Wear of High Speed Bearing Cage Statistical data of spacecraft show that most faults on orbit come from attitude and orbit control systems, and more than half were caused by fault of bearings [7]. The abnormal operation of high speed rotor bearing is also one important reasons for fault of CMG. High speed rotor bearing is composed of inner ring, outer ring, ball and cage. There are many friction pairs between ball and inner ring, ball and outer ring, ball and cage, cage and outer ring guide surface during operation. For example, there are 12 balls in a small

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CMG bearing, and there are nearly 40 friction pairs in the bearing while working. In the process of operation, there will be high-frequency collision and friction with the ball and ferrule guide surface. The fatigue life of CMG bearing is more than 20 years, so the weak point is the cage when the friction and wear occurs inside the bearing, although it is a gradual process. With the extension of the working time of the bearing, the degree of wear increases gradually. The resistance moment increase gradually when it reaches a certain limit. Moreover, due to the dynamic randomness of the wear morphology and distribution, the fluctuation of the resistance moment usually increases. At the same time, the vibration and noise amplify gradually, and the shaft temperature get higher and higher slowly. As shown in the Fig. 5 below, the motor current and bearing temperature increased slowly, the fault evolved for nearly one year, eventually leads CMG shutdown.

Fig. 5. The motor current and bearing temperature gradually increased

The current and bearing temperature of high speed rotor motor are directly related to the bearing friction torque. The current is proportional to the bearing friction torque. With the accumulation of friction, the motor current is increased synchronously to ensure sufficient torque output, however, the rotor speed fluctuation increases ineluctable, and the CMG diagnostic score of the control system decreases; When the friction torque get a certain extent, the motor current reaches the upper limit of the output threshold, and the rotor stops, then The fault is immediately identified by the control system. But in this process, the speed works relatively stable before high-speed rotor stops. For example, the nominal rotor speed of QM-1 is 6000 r/min, and its fluctuation is maintained at 0.227 r/min for a long time, which is just one stratification of telemetry acquisition. However, when the friction increases slightly, the fluctuation range will increase slightly, until the point of CMG is diagnosed as fault, the fluctuation is only about 1 r/min.

3 Evaluation Method for Performance of CMG The fault due to increase of impedance of the conducting ring in the low-speed frame often occurs instantaneously on orbit. It is hard to predict this kind of fault because monitoring the change of impedance in real time is nearly impossible, whether itself or external diagnosis. However, fault caused by increase of friction torque of high-speed

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bearing cage usually has a long way to go, with obvious external performance, that is the increase of motor current and the climb of bearing temperature. In this case, fault diagnosis and performance evaluation becomes a possibility. In this section, based on large number of telemetry data from the spacecraft on orbit, we will propose a comprehensive evaluation method to quantitatively evaluate the performance of CMG on orbit. Assume the original matrix is: ⎛

x11 ⎜ x21 x=⎜ ⎝ ... xm1

x12 x22 ... xm2

x13 x23 ... xm3

x14 x24 ... xm4

x15 x25 ... xm5

⎞ x16 x26 ⎟ ⎟ ... ⎠ xm6

(1)

 In this matrix, xi = xi1, xi2 , xi3 , xi4 , xi5 , xi6 stands for feature vector of CMG-i, including current of the high-speed motor, speed of high-speed rotor, temperature of high-speed rotor and current of low-speed of frame, temperature of low-speed frame and angular velocity of frame. The weight vector is established by coefficient of variation method as follow:

6 vj (2) wj = vj / j=1

We can see: vj = sj /|xj |

(3)

sj and xj stand for the deviation and expectation of index J. According to the 7300 data from June 1 of 2016 to June 1 of 2017, the deviation and expectation of each index are calculated as fellow Table 1. Table 1. The standard deviation and expectation of each index(deviation σ, expectation ε) Type

Motor current

5 Nms

0.458 0.015 7680.099 0.118

2.706 0.015

15 Nms

0.815 0.043 6000.833 0.093

3.125 0.037

25 Nms

0.509 0.037 6000.016 0.096 20.823 0.539

200 Nms

2.172 0.031 1424.285 0.425

0.087 0.035

ε

σ

Speed of rotor

Rotor temperature

ε

ε

σ

σ

2.502 0.059

Frame current ε

σ

Bearing temperature

Angular Velocity

ε

ε

σ

σ

0.002 0.073

2.608 0.018

0.017

0.952

2.499 0.100

2.771 0.042

-0.013

0.909

0.026 0.311 14.767 0.638

0.008

0.953

2.515 0.058

−0.00003 0.00151

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So the weight vector is: ⎞ w5N ⎟ ⎜ ⎟ ⎜w w = ⎜ 15N ⎟ ⎝ w25N ⎠ ⎛ w200N 0.00035389 0.0000001660 0.00005990 0.39440182 0.00007458 ⎜ ⎜ 0.00075326 0.00000022 0.00016904 0.00057131 0.00021640 =⎜ ⎝ 0.00055393 0.00000012 0.00019725 0.09115058 0.00032923 0.00028097 0.00000587 0.00046422 0.00791976 0.00045400 ⎛

0.60510964 0.99828977 0.90776889 0.99087517

⎞ ⎟ ⎟ ⎟ ⎠

(4) We establish the ideal scheme:  u = u10 u20 u30 u40 u50 u60

(5)

Here: uj0 =

avr

1≤m≤7300

 xmj , j = 1, 2, 3, 4, 5, 6

Substituting the sample data, we get the following results: ⎞ ⎛ ⎛ ⎞ 0.4824 7680.221 2.7687 0.3365 2.6687 0.07388 u5N ⎜ u15N ⎟ ⎜ 2.1665 6000.453 2.784 3.1375 2.412 0.06745 ⎟ ⎟ ⎜ ⎟ u=⎜ ⎝ u25N ⎠ = ⎝ 0.53 6000.5905 34.342 0.98 25.698 0.06045 ⎠ u200N 2.0067 1425 2.624 0.268 2.42 0.036 The relative deviation fuzzy matrix is established: ⎞ ⎛ r11 r12 r13 r14 r15 r16 ⎜ r21 r22 r23 r24 r25 r26 ⎟ ⎟ R=⎜ ⎝ r31 r32 r33 r34 r35 r36 ⎠ r41 r42 r43 r44 r45 r46 In this case: rij =



x¯ ij − uo i

max {xmj } −

1≤m≤7300

min {xmj }

, i = 1, 2, 3, 4

(6)

(7)

(8)

(9)

1≤i≤7300

Finally, we get the comprehensive evaluation model Di =

6 j=1

rij wj , i = 1, 2, 3, 4

(10)

From now on, we check the comprehensive evaluation model above. Take 15 nms CMG for example, the average of each index of the sample data from June 1 of 2016 to June 1 of 2017 is taken into the evaluation model, we calculate the comprehensive evaluation index D of one year.

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For the sample data of CMG 1–5, the average of the six indicators is shown as follow: ⎛ ⎞ 0.688 6001.245657 3.3422 2.51506 3.105758459 0.16894337 ⎜ 0.816 6000.948672 3.4446 2.53046 3.079372611 −0.026010695 ⎟ ⎜ ⎟ ⎜ ⎟ x = ⎜ 0.808 6000.728926 3.4120 2.47823 3.09907519 0.058618127 ⎟ ⎜ ⎟ ⎝ 0.656 6001.276573 3.4373 2.61144 3.14667531 0.245255176 ⎠ 0.817 6000.788936 3.2904 2.54038 3.051117176 −0.162181955 Substituting:

xij − u0 i   rij = max xij − min xij 1≤i≤4

(11)

1≤i≤4

And:  u15N = 2.1665 6000.453 2.784 3.1375 2.412 0.06745 So: ⎛

0.747636364 ⎜ 0.531210191 ⎜ ⎜ r = ⎜ 2.759322034 ⎜ ⎝ 0.986363636 2.442335766

−3058.242883 −1223.067974 −6107.243905 −6127.534405 −6107.025687

3.652866242 1.724234694 4.661594203 4.880291971 3.808029197

2.847792055 2.94491651 2.428262703 1.706630409 2.203883046

⎞ 2.023418792 0.000613185 1.610480827 −0.000600604 ⎟ ⎟ ⎟ 2.44531358 −9.35594E-05 ⎟ ⎟ 2.70042548 0.00101291 ⎠ 2.172824864 −0.00139771

Using the formula: Di =

6 j=1

rij wj , i = 1, 2, 3, 4

(12)

And:   w15N = 0.00075326 0.00000022 0.00016904 0.00057131 0.00021640 0.99828977

So we get the scores of five CMGs is: D1 = 0.00303; D2 = 0.00478; D3 = 0.00316; D4 = 0.00260; D5 = 0.00132.

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Fig. 6. Trend evaluation of CMGs with one month Step from 2016 to 2018

Fig. 7. Real trend of three index for one year

if Di < Dj , then i’s performance is better than j’s. So the evaluation result is as follows: CMG5 > CMG4 > CMG1 > CMG3 > CMG2 Indeed, the speed and current of the CMG-2 did fluctuate slightly for more than one year. In early 2017, it was diagnosed and eliminated by the control subsystem due to large output fluctuation. After that, it was restored after stopped for a while (Fig. 7). Two elements worked stably, but the high-speed motor current climbed slowly, fluctuation get bigger and bigger till failure occurred one year later, the results of Fig. 6 is basically consistent with this trend.

4 Conclusion Fault recovery time is a important index for spacecraft reliability evaluation. As an actuator, CMG is usually cut off by the control system if failure occurs on orbit, resulting in mission interruption. In serious cases, the spacecraft will lose its attitude, and relying on the remaining CMG or jet to turn to solar orientation mode. To reestablish the normal attitude again, we need to re running-in CMG and introduce it into control. Generally, at

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least two to three orbit period will be spent, in other words, it is more than 3–4 h, which seriously affects the task of users. The fault analysis is not only helpful for improving ability of operation, accumulating experience of maintenance, but also necessary for shortening the repair time as much as possible. Meanwhile, it is benefit for design improvement obviously. In this paper, we comprehensively consider the characteristics of CMG, establish a normalized weight vector of element by using coefficient of variation, compare the actual data on orbit with the ideal value to obtain the relative deviation fuzzy matrix, and finally we establish the comprehensive evaluation model of CMG, so quantitative evaluation of performance on orbit is realized. From the case of test, we can see that the evaluation results are in line with the actual state of the project, which verifies the feasibility and rationality of the method.

References 1. Ning, X., Han, B.C., Fang, J.C.: Disturbance observer based decoupling method of doublegimbaled variable speed control moment gyroscope. J. Mech. Eng. 53(10), 52–59 (2017) 2. Wang, J.W., Wang, H., Zhou, N.N., He, T.: Bearing fault diagnosis method based on K-medoids clustering. Aerosp. Control Appl. 46(4), 24–28 (2020) 3. Fehse, W.: Automated Rendezvous and Docking of Spacecraft. Cambridge University Press, UK (2003) 4. Sands, T.A., Kim, J.J., Agrawal, B.N.: Nonredundant single-gimbaled control moment gyroscopes. J. Guidance Control Dyn. 35(2), 578–587 (2012) 5. Shi, Y.L., Xiong, J., Zhi, K.Y., Wu, D.Y., Wei, W.S.: Fault detection and signal reconstruction for hall sensors in high-speed rotor of control moment gyroscopes. Micromotors 52(11), 70–75 (2019) 6. Fault detection of CMG based on parity vector method. In: The 13th Chinese Conference of Space and Motion Control Technologe, pp. 356–359 (2008) 7. Zhang, S., Shi, J.: Spacecraft on-board failure statistics and analysis. Spacecr. Eng. 19, 41–46 (2010)

Precision Orbit Determination for LEO Based on BDS Navigation Data Chao Li1,2(B) , Xiusong Ye1,2 , Hong Ma1,2 , Shouming Sun1,2 , and Yang Yang1,2 1 State Key Laboratory of Astronautic Dynamics, Xi’an 710043, China

[email protected] 2 Xi’an Satellite Control Center, Xi’an 710043, China

Abstract. Since the Beidou navigation system was officially opened to the world. The number of satellite that equipped with multimode navigation receivers to receive BDS, GPS and other navigation system signals has been increased greatly. This paper analyzes the precision orbit determination based on the Beidou navigation data of spacecraft in orbit. The satellite orbit perturbation model is selected properly, and the spaceborne BDS orbit determination algorithm with dynamic compensation is established effectively. The accuracy of BDS navigation data and dynamic compensation orbit determination results are verified by using GPS navigation data and GPS Precise Orbit Determination results. The results indicate that the accuracy of BDS navigation data is equivalent to GPS navigation data, the rms (root mean square) deviation of position is less than 10 m, and the rms deviation of velocity is less than 0.05 m/s. The orbit determination accuracy of BDS data and that of GPS data are nearly equal. The position difference of orbit determination results is better than 10 m, and the velocity error is less than 0.006 m/s. It can be seen that the method improved the orbit determination accuracy of BDS navigation data and provide a basis for increasing the application of BDS navigation data in subsequent spacecraft orbit determination tasks. Keywords: BDS · GPS · Dynamic compensation orbit determination · Perturbation model

1 Introduction GNSS is used more often in remote sensing, meteorology and oceanography LEO, which not only because of its low cost and portability but also because of its advantages of the full-time, high-precision and continuous observation. The early TOPEX/Poseidon satellites used spaceborne GPS data to conduct orbit determination, and the accuracy is nearly centimeter level [1]. The Beidou navigation system of China, the GPS navigation system of United States, the GLONASS navigation system of Russia, and the Galileo navigation system of Europe were all included in GNSS [2–4]. The number of satellite that equipped with multimode navigation receivers to receive signals from BDS, GPS and other navigation systems has been greatly increased after the opening of the global service of BDS. The application of spaceborne BDS technology is still in its infancy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 551–559, 2023. https://doi.org/10.1007/978-981-19-9968-0_66

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The research on orbit determination using only spaceborne BDS navigation data is rare. In FY-3 mission, the orbit determination analysis is conducted by using the raw data of spaceborne BDS, and good results are obtained [5]. Many scholars analyzed the orbit determination result by using GNSS raw data [6, 7]. The efficiency of using the raw data is low in engineering practice due to the complex processing procedure of the raw data and also the large amount of data can’t be downloaded in real time. It has great popularization value in engineering practice to use the navigation data for orbit determination because of the higher efficiency after eliminating of the complex data processing process. Therefore, this paper uses the BDS navigation data of LEO to carry out dynamic compensation precision orbit determination [8–13].The accuracy of BDS navigation data is analyzed, which can give a suggestion for the further application of Beidou navigation system.

2 Dynamic Compensation Orbit Determination Dynamic orbit determination is to improve the orbit by integrating its motion equation according to the satellite dynamic model. Its advantage is that it can provide the continuous position of the satellite and extrapolate the orbit more accurately. The solar radiation pressure coefficient and atmospheric drag coefficient are estimated because of the difficulties to accurately model the solar activity and atmospheric density. In addition, while introducing the constraints of high-precision orbit dynamics model, the empirical acceleration component is introduced to compensate all other unmodeled perturbation terms other than the high fidelity model constraints of orbit dynamics, which are estimated together with other state variables as unknown parameters. Satellite position and velocity, solar pressure coefficient, atmospheric drag coefficient, and acceleration compensation terms are the parameters to be estimated.   r(t0 ) (1) y0 = y(t0 ) = v(t0 ) T  (2) Y = yT0 ; CR ; CD ; a0 ; · · · ; ana −1 There are nY dimensional orbital model parameters, where CR is the solar pressure coefficient; CD is the atmospheric drag coefficient; there are na dimensional empirical acceleration vectors am (ti ); the interval for ti is [t0 + m · τ, t0 + (m + 1) · τ ], m = 0, 1, · · · , na − 1. If the orbit model parameters Y are solved, the satellite state y(ti ) at the time ti can be obtained by dynamical integration. The observation equation at the time ti is expressed  function hi (Y), which will  by be linearized and expanded at the approximate point Y ∗ , Y = Y ∗ + Y

(3)

Y is the correction of Y ∗ . The least squares batch method is used to solve the problem. It can be written as, T    T     ∂h ∂h ∂h  ∗  W  ∗  Y =  ∗ W z − h Y ∗ (4) ∂ Y ∂ Y ∂ Y

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z is the observation, W is the weight matrix. For the convenience of description, the partial derivative in the design matrix is replaced by, ∂h   = HY ∂ Y∗

(5)

So the least squares estimation is written as, H TY WH Y Y = H TY W(z − h(Y ∗ ))

(6)

The equation above is simplified as, N YY Y = nY

(7)

N YY = H TY WH Y , nY = H TY W(z − h(Y ∗ )).

(8)

It is not easy to solve N −1 YY because of the high dimension of N YY . The orbital model parameters are decomposed into position and velocity parameters X and other parameters T(solar pressure, atmospheric drag and empirical force). The above equation is redefined as,    T N TT N TX nT (9) = N XT N XX nX X So we get,  −1   −1 X = N XX − N XT N −1 n N − N N n TX X XT T TT TT

(10)

T = N −1 TT (nT − N TX X)

(11)

The Gauss Newton iterative method is used and the parameters Y that meet the iterative convergence conditions are taken as the final results for parameter estimation (Fig. 1).

∗

=

(

Initial Orbit T 0

; CR ; CD ;

0

;

;

na −1

)=(

T 0

;0;0; 0;

; 0)

Geopot enti al Perturbat ion Tides

Integration of Orbital Motion

y ( ti )

Observation Errors

Integration of Variational Equations

∂y ( ti )

Coefficient of Normal Equation ∂hi ∂Y

The Thi rd B ody Atmospheric Drag Sol ar Radiation Pressure General R elativi ty Empirical Acceleration

Measurement Model

Initial Orbit Update Y ∗ = Y ∗ + ΔY

−

( ) ∗

Force Model

N o

∂Y

∂hi ∂y ( ti )

Convergence? Yes Stop

Fig. 1. Flow chart of dynamic compensation POD

Improvement of Orbit ΔY

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3 Perturbation Model In the inertial frame the motion of a satellite is described by the differential equation: r¨ = −

GME r + a(t, r, r˙, P) r3

(12)

where GME is the gravitational coefficient; r, r˙ and r¨ are the position,velocity and acceleration vectors of the satellite, P is the dynamic parameter vector; a is the summation of other perturbation besides two body central gravitational attraction. a = aNS + aNB + aTD + aDG + aSR + aRL + aRTN

(13)

where aNS is the nonspherical perturbing acceleration, aNB is the third body perturbed acceleration with sun and moon attraction perturbation, aTD is the tidal perturbing acceleration which include the earth tide, ocean tide and atmosphere tide perturbation, aDG is the atmospheric drag perturbing acceleration, aSR is the solar radiation perturbing acceleration, aRL is the general relativistic perturbing acceleration, aRTN is the empirical acceleration. ⎡ ⎤ ⎡ ⎤ Cr cos u + Sr sin u dr aRTN =⎣ Ct cos u + St sin u ⎦ · ⎣ dt ⎦ (14) dn Cn cos u + Sn sin u where u is the argument of latitude, dr , dt , dn are the unit vectors of the radial, tangential, and orbit normal direction of the satellite respectively. The coordinate system and time system for dynamical POD are J2000 inertial system and Terrestrial Dynamic Time (TDT). The arc length of one dynamic solution is 24 h. Solving the parameters in three directions at the same time will affect the stability of orbit determination due to the coupling between the empirical acceleration components in the three directions. On the other hand, the T and N directions of the orbit just correspond to the atmospheric drag, and the accuracy of the force model is lower than that in the R direction. Therefore, only the empirical acceleration parameters in T and N directions are generally estimated (Table 1). Table 1. Model and parameters used in dynamic compensation POD POD model

Description

Gravity

JGM3 70 × 70

Solid tides

IERS96, 4 × 4

Ocean tides

CSR4.0

Third body

Moon, Sun (continued)

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Table 1. (continued) POD model

Description

Solar radiation

Box-Wing model, C R estimated

Atmospheric drag

Msis90, C D estimated

General relativity

Schwarzschild

Periodic empirical Acceleration

Piecewise estimation of Ct , St , Cn , Sn

Precession nutation

IAU1980 + EOPC correction, IERS96

EOP

EOPC04

Planetary ephemeris

DE405

4 Data Analysis The external precision data is always used to verify the measurement accuracy of equipment. Appropriate reference data selection can help to assess the quality of spaceborne BDS navigation data. Therefore, we use the GPS navigation data of a LEO satellite in a certain period as the external measurement and verification data, and selects the GPS Precise Orbit Determination results in the corresponding period as the external orbit determination and verification data after dynamic orbit determination. The specific analysis methods are divided into the following three types. Analysis case 1: compare and analyze the consistency, and stability of BDS navigation data and GPS navigation data transmitted from the satellite; Analysis case 2: compare and analyze the difference between BDS and GPS orbit determination after dynamic orbit determination to verify the accuracy of BDS navigation data orbit determination; Analysis case 3: compare and analyze the consistency between BDS and GPS navigation data and corresponding precision orbit determination results in this period. Case 1. Differences Between GPS and BDS Navigation Data The BDS and GPS navigation data of arc 1 (2019-09-04 00:00:0.000–2019-09-06 00:00:0.000utc) and arc 2 (2019-09-06 00:00:0.000–2019-09-08 00:00:0.000 UTC) are selected to compare and analyze the navigation data of the two arcs respectively, as shown in Fig. 2 and Table 2.

Fig. 2. Differences between GPS and BDS navigation data (Arc 1 & Arc 2)

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C. Li et al. Table 2. Result of differences between GPS and BDS navigation data

Arc 1

2

Item

Position (m)

Velocity (m/s)

MAX

RMS

MAX

RMS

X

16.400

2.874

0.0800

0.0177

Y

29.800

3.062

0.2100

0.0286

Z

41.000

2.981

0.1000

0.0220

Total

41.317

5.150

0.2147

0.0402

X

32.100

5.013

0.0600

0.0168

Y

31.300

3.848

0.1700

0.0207

Z

91.900

5.400

0.0800

0.0172

Total

92.320

8.313

0.1729

0.0317

For arc 1, it can be seen from Fig. 2 and Table 2 that the rms deviation in the total position of BDS and GPS navigation data is about 5 m, and the maximum difference in the total position is about 40 m. The rms deviation on the total velocity is about 0.04 m/s, and the maximum difference on the total velocity is about 40 m. The position differences of the two navigation data is the largest in Z direction, about 40 m; The maximum velocity deviation is in the Y direction, which is about 0.2 m/s; The calculation results of arc 2 are consistent with those of arc 1. It can be concluded that the deviation of BDS and GPS navigation data in the total position is mainly caused by the deviation in their respective Z direction, and the deviation in the total velocity is mainly caused by the deviation in their respective Y direction. Case 2. Differences Between POD Result of GPS and BDS Navigation Data The BDS and GPS navigation data of arc 1 (2019-09-04 00:00:0.000–2019-09-06 00:00:0.000 UTC) and arc 2 (2019-09-06 00:00:0.000–2019-09-08 00:00:0.000 UTC) are used to determine the precision orbit respectively. Figure 3 and Table 3 show the comparison result of BDS precision orbit determination result and GPS precision orbit determination.

Fig. 3. Differences between POD result of BDS and GPS navigation data (Arc 1 & Arc 2)

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Table 3. Result of differences between POD result of GPS and BDS navigation data Arc 1

2

Item

Position (m)

Velocity (m/s)

MAX

RMS

MAX

RMS

R

0.230

0.160

0.0010

0.0005

T

1.135

0.556

0.0002

0.0002

N

3.611

2.543

0.0039

0.0028

Total

3.759

2.609

0.0040

0.0028

R

0.705

0.485

0.0023

0.0013

T

2.819

1.442

0.0007

0.0005

N

5.013

3.458

0.0054

0.0037

Total

5.446

3.778

0.0058

0.0040

As can be seen from the figure and table above, although case 1 shows that there are certain differences in position and velocity between BDS and GPS navigation data, after dynamic orbit determination, the position error of precision orbit of the two navigation data is less than 10 m and the velocity error is less than 0.006 m/s. It can be concluded that the accuracy of dynamic compensation orbit determination using BDS navigation data is equivalent to that of GPS navigation data. Case 3. Differences Between Navigation Data and POD Result Using the BDS and GPS navigation data (2019-09-04 00:00:0.000–2019-09-06 00:00:0.000 UTC) to determine the precision orbit, and compare the navigation data with the precision orbit results. The results are described in Fig. 4 and Table 4.

Fig. 4. Differences between navigation data and orbit determination result (BDS & GPS)

It can be seen from Fig. 4 and Table 4 that when comparing the BDS navigation data of arc 1 with the precision orbit itself, the position difference rms is about 7.1 m, the maximum value is about 44 m. The velocity difference rms is about 0.054 m/s, and the maximum value is about 0.355 m/s. Comparing GPS navigation data with precision orbit itself, the position difference rms is about 4.9 m, the maximum position difference is about 14.7 m. The velocity difference rms is about 0.044 m/s, and the maximum velocity difference is about 0.16 m/s.

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Data BDS

GPS

Item

Position (m)

Velocity (m/s)

MAX

RMS

MAX

RMS

X

21.444

3.564

0.0947

0.0341

Y

25.587

3.681

0.3354

0.0371

Z

40.734

4.951

0.1510

0.0194

Total

44.163

7.125

0.3551

0.0540

X

10.052

2.000

0.0667

0.0279

Y

11.113

2.792

0.1371

0.0274

Z

14.211

3.526

0.1255

0.0205

Total

14.705

4.923

0.1568

0.0441

5 Summary In this paper, the dynamic orbit determination analysis based on BDS navigation data is carried out by using LEO measurements. It shows that the rms of position difference between BDS navigation data and GPS navigation data is less than 10 m, and the maximum difference is less than 100 m; rms of velocity difference is less than 0.05 m/s, and the maximum deviation is less than 0.25 m/s. Based on the dynamic compensation method, the BDS and GPS navigation data are used for orbit determination respectively. The position accuracy of the orbit determination results is better than 6 m, and the velocity difference is better than 0.006 m/s. The orbit determination accuracy of the two data is nearly the same. Comparing the BDS navigation data with the precision orbit, the rms of the position difference is better than 8 m, and the maximum difference is about 44 m; rms of velocity difference is less than 0.06 m/s, and the maximum difference is better than 0.2 m/s. Comparing GPS navigation data with precision orbit, the rms of position difference is better than 10m and the maximum difference is better than 15 m; rms of velocity difference is less than 0.05 m/s, and the maximum difference is better than 0.2 m/s. The dynamic compensation method discussed here can effectively reduce the influence of BDS navigation data error and improve the orbit determination accuracy of BDS navigation data, which can provide a basis for increasing the application of BDS navigation data in the future tasks. In addition, it has been demonstrated from the above results that the stability of BDS navigation data is slightly insufficient and the error is large in some arc, which needs to be further studied.

References 1. Tapley, B.D., Ries, J.C., et al.: Precision orbit determination for TOPEX/POSEIDON. J. Geophys. Res. Oceans 99(C12), 24283–24404(1994) 2. Li, C., et al.: Precise orbit determination of grace satellite based on spaceborne acceleration data. In: csnc2014, Wuhan (2014)

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3. Schutz, B.E., et al.: Precision orbit determination, validation and orbit prediction for ICESat. In: Advanced Maui Optical and Space Surveillance Technologies Conference (2008) 4. Beidou Navigation Satellite System. http://www.beidou.gov.cn 5. Li, M., et al.: Precise orbit determination of the Fengyun-3C satellite using onboard GPS and BDS observations. J. Geod. 91, 1313–1327 (2017). https://doi.org/10.1007/s00190-0171027-9 6. Duan, B., Hugentobler, U., et al.: Prediction versus real-time orbit determination for GNSS satellites. Adv. Space Res. GPS Solut. 23, 39 (2019) 7. Li, X., et al.: Integrated precise orbit determination of multi GNSS and large LEO constellations. Remote Sens. 11(21), 2514 (2019). https://doi.org/10.3390/rs11212514 8. Allahvirdi-Zadeh, A., Wang, K., et al.: Precise orbit determination of LEO satellites based on undifferenced GNSS observations. J. Surv. Eng. 148(1), 03121001 (2022) 9. Zeng, T., et al.: Uncombined precise orbit and clock determination of GPS and BDS-3. Satell. Navig. 1, 19 (2020). https://doi.org/10.1186/s43020-020-00019-7 10. Hauschild, A., et al.: Precise real-time navigation of LEO satellites using GNSS broadcast ephemerides. Navigation, 1–14 (2021). https://doi.org/10.1002/navi.416 11. Zou, D., et al.: Orbit determination algorithm and performance analysis of high-orbit spacecraft based on GNSS. IET Commun. 13(20), 3377–3382 (2019) 12. Jun, Z., Chong, W., Yu-Fan, H., et al.: High accuracy navigation for geostationary satellite TTS-II via space-borne GPS. In: 39th Chinese Control Conference (2020) 13. Wang, C. et al.: Accuracy analysis of the calibration satellite orbit determination based on onboard BDS data. In: Yang, C., Xie, J. (eds.) China Satellite Navigation Conference (CSNC 2021) Proceedings. LNEE, vol. 772, pp. 453–462. Springer, Singapore (2021). https://doi. org/10.1007/978-981-16-3138-2_42

Research and Practice of Electrostatic Grounding Technology of Aerospace Electronic Products Based on Intelligent Active Protection Concept Ying Zhu(B) , Dong Wang, Ke Li, Wei Qiao, Yijian Zheng, and Wenze Qi Xi’an Institute of Space Radio Technology, Xi’an 710100, China [email protected]

Abstract. This paper introduces the practical experience of the electrostatic grounding technology of aerospace electronic products, analyzes the key and difficult problems in the electrostatic grounding technology of ESDs (electrostatic sensitive) products, and overcomes the difficulties such as “many kinds of ESDs components used in the production process”, “many links in the product development and production process”, “many operators handling electrostatic sensitive electronic products”, and “wide” electrostatic protection work area, Put forward the working ideas and methods of establishing the intelligent active electrostatic protection concept, integrate the intelligent active protection concept into the research of electrostatic grounding technology, and summarize the practical results of the electrostatic grounding technology of aerospace electronic products based on the intelligent active protection concept by actively identifying the electrostatic protection needs of personnel, products, links, regions, environment and other dimensions, It is of great significance and value to improve the quality and reliability of aerospace electronic products. Keywords: Intelligent active electrostatic protection · Electrostatic discharge (ESD) · Electrostatic discharge-sensitive (ESDS) · Anti-static working area (EPA)

1 Hazards and Loss of ESD to Electronic Products ESD as one of the most important hazards of electronic equipment, seriously affect product quality and reliability, causing huge economic losses, Japan has unqualified failure analysis, found that 45% of unqualified products are caused by electrostatic, the United States released more than 10 industry electrostatic investigation report shows that the average annual direct economic loss of more than 20 billion dollars, British electronic annual loss of 2 billion pounds, China’s electronic industry electrostatic direct loss of more than 1 billion yuan. In the electronics industry, the ESD damage of semiconductor devices is also very serious. The survey results of only several device factories and instrument factories in Shanghai show that 8%–33% of MOS devices fail due to ESD damage, and the instrument repair loss caused by ESD failure is more considerable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 560–566, 2023. https://doi.org/10.1007/978-981-19-9968-0_67

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The product characteristics of “high reliability, long life and unrepair” of spacecraft electronic equipment determines the importance and complexity of electrostatic protection. It is of great practical significance to conduct electrostatic protection management in the aerospace and electronic industry. Electrostatic electricity protection work has become an extremely important content in the modern industrial production process management (Table 1 and Fig. 1). Table 1. Loss of components caused by electrostatic discharge Order number

Department

Minimum loss rate

Maximum loss rate

Average loss rate

1

Component manufacturing plant

4%

97%

16%–22%

2

Retail trader

3%

70%

9%–15%

3

Underwriter

2%

35%

8%–14%

4

User

5%

70%

27%–33%

Fig. 1. Data investigation of component loss caused by electrostatic discharge

2 Development Overview Xi’an Branch of China Academy of Space Technology (hereinafter referred to as “Xi’an Branch”) is the core unit of space vehicle payload development in China. The number of electrostatic sensitive electronic products developed accounted for more than 90% of the total products, Xi’an Branch has started the exploration and practice of electrostatic grounding technology since the late 1990s, In 2008, Xi’an Branch Space Electronic Products development and Production Base (201 workshop) put into use, The plant is built in accordance with the international advanced management concepts and technical standards, The whole plant adopts the protection grounding, working grounding and lightning protection grounding remote common ground, proximal separation, independent system working principle, Ensure that all kinds of grounding operate independently in the plant and do not interfere with each other, Further improve the electrostatic grounding technology. In 2014, with the operation of the space base industrial park new area, Xi’an branch anti-static working area further improve the configuration of

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electrostatic protective equipment, equipment and supplies, through power protection grounding, auxiliary grounding and potential grounding three grounding way to ensure EPA personnel, products, facilities and supplies. According to the standard specification to implement the Xi’an branch ESDS components, components, single machine, subsystem and satellite load module in electric, debugging, testing, test, packaging, transportation, hoisting, storage, security link of anti-static grounding management and technical requirements, effectively improve the Xi’an branch ESDS product development process static protection grounding technology ability.

3 Key Points and Difficulties As the national team for the development and production of space payload products in China, the annual delivery volume of Xi’an Branch accounts for more than half of the total volume of China Institute of Space Technology. The heavy scientific research and production tasks bring great challenges to the static electricity protection management of Xi’an Branch. The research of electrostatic grounding technology in Xi’an Branch presents the characteristics of “three more and one wide”. The ESDS components used “more”, “product production process “more”, disposal of electrostatic sensitive electronic products operators “more”, “wide” electrostatic protection area characteristics, the characteristics of the electrostatic grounding technology research, therefore, the intelligent active protection concept into electrostatic grounding technology research, through active personnel, products, link, area, environment dimensions of electrostatic protection requirements identification, has achieved significant practical effect, specific situation analysis is as follows: 3.1 Identification of Protection Requirements of Static-Sensitive Components of Xi’an Branch The protection management of ESDS components is the source and foundation of the static electricity protection management of Xi’an Branch. Xi’an branch from the ESDS components identification, the branch introduction, domestic and self-made three types of electrostatic sensitive components, by consulting the components manual, product data manual and components test for ESDS components electrostatic sensitive voltage, according to the GJB548B device sensitivity classification, form the Xi’an branch ESDS components list. All professional research institutes and overall design units shall organize the publicity and implementation training of the List, Make the design, production and protection, process, testing and test personnel timely grasp the static sensitivity level of the branch ESDS components, Identify ESDS devices in the product design outline and technical drawings, By making an active identification, Introduce the concept of electrostatic protection in the product design stage, Taking ESD protection design technical measures such as series current limiting resistance and parallel transient inhibitory diodes, Strengthen control from the source, Improve the product and subsystem robustness and antistatic capability, For the active electrostatic protection of electrostatic sensitive components in the design stage and in the use process, Provide the management basis and technical measures.

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3.2 Identification of Protection of Electrostatic Sensitive Electronic Products The development process of ESDS electronic products is the key link of static electricity protection management of Xi’an Branch. Branch combined with 23 stage important process of protection link. Each development unit through the active identification of electrostatic protection process, clear the ESDS electronic products development process of special process of electrostatic protection and key link, in process specification, process documents, operation instructions and operation procedures system, such as pin potential, static dissipation process measures, ions and dissipation targeted static grounding technical measures. The whole process of static sensitive electronic products in the branch and the static protection management of each link are controlled, which lays a solid foundation for the integrity of the static protection management chain of Xi’an Branch. 3.3 Identification of Static Electricity Prevention and Control of Insulated Articles and Isolated Conductors Common insulators in anti-static working area include drawings, integrated circuit packaging pipe, tape, plastic foam sponge, polymer insulation material tooling, fixture; isolated conductors such as suspended test cables at both ends, ungrounded satellite support truck, mobile steps and other equipment and facilities. The insulation and isolated conductor because of its own polymer material characteristics, electrostatic charge is not easy to migrate, so cannot through discharge method antistatic grounding, its induction electric field itself easily damage to ESDS products, such as FPJA, MOS device gate breakdown, hybrid integrated circuit metal wiring heat melt, hot spots, spherical and other potential damage. According to statistics, ESDS products caused by insulating items and isolated conductor induced electric field account for 66% of the electrostatic quality problems, and more than 90% are potential slow failure, which is greatly harmful to the quality and reliability of aerospace electronic products. Therefore, the active identification and control of insulating articles and isolated conductors in the antistatic working area has become the focus and difficulty of the antistatic management in the electronic industry (Fig. 2).

Fig. 2. Electrostatic damage morphology and schematic diagram

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4 How to Establish Electrostatic Grounding Measures for Aerospace Electronic Products Based on the Concept of Intelligent Active Protection 4.1 Innovate the Anti-static Grounding Monitoring Technology to Improve Xi’an Branch has established the “ESD grounding pole wrist band online monitoring system” in the EPA. The system can actively and continuously monitor the grounding effect of the anti-static equipment and facilities in the anti-static work zone, and provide real-time ESD protection network monitoring in the work area. Using the networked intelligent monitoring system, not only can do “unattended”, but also can achieve “24 + 7” uninterrupted monitoring, the monitoring of abnormal ESD grounding problems timely alarm, real-time feedback and record. The static electricity generated on the site will be discharged timely and effectively to the greatest extent to improve the yield rate of products, reduce the production cost and personnel management cost. 4.2 Use Anti-static Door Machine Chain Detection Technical Measures to Strengthen the Active Control Ability of Static Electricity Protection In order to further improve the active control ability of antistatic, the branch has established a door machine chain anti-static detection system in the B 1 floor, B2 workshop and B4–6 hall of the new district. The system integrates the original anti-static integrated resistance tester and access control gate through the network, the operators entering the antistatic work area, the system can record and save the personnel entering the antistatic working area in real time, with the personnel authorization limit function, can actively limit the electrostatic grounding unqualified personnel into the EPA, and can effectively control the number of personnel entering the antistatic working area. The application of this technology has changed the original passive management mode of artificial supervision, and improved the effectiveness and reliability of static grounding management of personnel in the anti-static working area through the active detection and access technology (Fig. 3).

Fig. 3. Anti static door crane interlocking system and its application

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5 Actual Effect 5.1 Cure and Form a Complete Anti-static Grounding Management System Xi’an Branch continuously carries out the exploration, research and engineering practice of electrostatic grounding technology of space electronic products based on the concept of intelligent active protection, Form a series of complete anti-static electricity management rules and regulations, For example, based on ESDS components, ESDS product development process links, insulators and isolated conductors, Based on the management and technical requirements of active monitoring of ESD online monitoring system, Curing in the enterprise ESDS product anti-static design task book, ESDS product outline, electrostatic protection process specification, anti-static operation instructions, process flow card, operation procedures and product resume and other operation document records, Formed the enterprise, department, office (workshop), team at all levels of the system complete anti-static management system documents, Standardize anti-static management in all levels of personnel, all links and all kinds of areas. It provides a scientific, applicable and executable management basis for the electrostatic grounding control of aerospace electronic products based on the concept of intelligent active protection. 5.2 Help to Build a National “Five-Star” Demonstration Site, and Improve the Active Control Ability of EPA In the China quality association, aerospace science and technology corporation, aerospace five courtyard star demonstration site quality improvement project practice, Xi’an branch in accordance with the quality association, group company and five courtyard on the star production demonstration site unified deployment, carried out the 201 workshop anti-static work area for pilot star demonstration site construction work. As one of the key evaluation elements of the star demonstration site construction, anti-static electricity plays a prominent role in the construction and evaluation of the whole star demonstration site. 5.3 The Quality Problem of Static Damage Products in Xi’an Branch Has Decreased Significantly Xi’an branch through more than ten years based on the concept of intelligent active protection aerospace electronic electrostatic grounding technology exploration research and engineering practice, antistatic work area basic management level significantly, operators, products, facilities, regional anti-static grounding timeliness and reliability significantly improved, according to the quality management department, Xi’an branch ESDS electronic products production quality problems decline year by year, strong guarantee the quality and reliability of active electronic products.

6 Conclusion The Outline of China Intelligent Manufacturing 2025 Plan sets out the goal of becoming a quality country. Based on the concept of intelligent active protection of aerospace

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electronics electrostatic grounding technology research and practice for the enterprise introduced a scientific antistatic management concept and technology, through the active static grounding identification and control concept, active static grounding online monitoring technology, make enterprise static sensitive electronic products in the whole process of static protection chain, personnel antistatic awareness and skills, enterprise static protection technology level improved significantly. It has laid a solid foundation for helping China’s high-quality space development and accelerate the realization of the magnificent blueprint of the “Chinese Dream and the space dream”.

References 1. ISO/IEC Directives Part 1: Procedures for the Technical Work, 10th edn. ISO, Geneva 2. Ji, Q.: Development status and countermeasures of electrostatic discharge protection standards in China. Saf. EMC (1), 9–14 (2022) 3. Feng, J.: Research and Countermeasures of electrostatic protection. Sci. Technol. Innov. Her. (4), 101, 103 (2020) 4. Gao, Z.: Analysis on the domestic and foreign standards for product and test method of electrostatic discharge shielding materials-bags. Stand. Sci. (12), 109–113 (2020) 5. Li, X.: Research on Calibration Technology of electrostatic charge sensor. J. Astronaut. Metrol. Meas. (39), 05 (2020) 6. Zhang, W.: Differential analysis of impact resistance standards for antistatic floor product. Stand. Sci. 8(8), 122–125 (2018) 7. Jiayu: Electrostatic protection technology and design of electronic information system room. Technol. Enterp. (4), 22–25 (2015) 8. Guo, D.: Solution and inspiration from USA’s involvement in international standards activity of IEC electrostatic protection. China Stand. (5), 76–80 (2014) 9. Q/QJA 118-2013 Electrostatic protection management system requirements of aerospace electronic products 10. Q/QJA 119-2013 Technical requirements for electrostatic protection of aerospace electronics products

Investigation on Feasibility of Covert TT&C Scheme Using Satellite Payload Channel Jie Chen(B) , Haiming Qi, and Fengchun Wang Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. A covert TT&C scheme is proposed transmitting TT&C signal via satellite payload channel. The proposed covert TT&C scheme is fulfilled by multiplexing the spectrum spread signal of TT&C transponder and the signal of repeater in the same frequency bandwidth into one payload channel and transmitting to the earth station. The proposed TT&C scheme has been demonstrated that TT&C signal and the repeater signal can operate compatibly by theoretical analysis and numerical simulation. The proposed TT&C scheme is a kind of covert communication, which ensures the safety of TT&C information effectively. And also it might be a redundant means of on-board TT&C subsystem, which enhance the reliability and robustness of on-board TT&C subsystem and guarantee the safety and stability of in-orbit operation of satellite. Keywords: Satellite TT&C · Repeater · Spread spectrum · Covert communication · Compatibility

1 Introduction Satellite TT&C technique is a kind of method tracking, telemetering and commanding a satellite by radio [1]. Owing to the characteristic of radio propagation, the critical information has the risk of interception by the third party. With rapid development of satellite technology and communication technology, it is imperative to investigate the topic on secure transmission of TT&C information [2]. In recent years, a kind of new theory called covert communication or low detection probability communication has been proposed. Unlike the traditional security technology such as encryption to protect the integrity of the transmission content, covert communication emphasizes the protection of the transmission process itself, i.e., reduces the detection probability of the third party, which provides the most direct and effective security protection strategy [3]. A model that uses the artificial noise generated by the interfering node to assist in the implementation of covert communication has been proposed [3]. It has been proved by the mathematical derivation that whether the transmit power of the interfering node is constant or uniform distributed can both achieve the covert communication with non-zero capacity. The noise or malicious interference © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 567–573, 2023. https://doi.org/10.1007/978-981-19-9968-0_68

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wave in the environment is used as the carrier wave to transmit information and a new covert communication model has been proposed [2]. On the other hand, satellite wireless TT&C technique serves as the only measurement, which is required to be highly reliable. It is a common measure by adding redundant backups to improve the reliability of TT&C subsystem. New methods for designing an integrated channel are proposed, using the isochronous service proposed by the Advanced Orbiting System (AOS) [4]. Incoherent data frames are used to transmit payload data and ranging information in uplink and downlink, working in two modes. However, despite previous works providing useful information for facilitating researchers and engineers to secure the TT&C information or improve the reliability of a TT&C subsystem, there still exists the problem whether security of TT&C information and reliability of a TT&C subsystem can be fulfilled at the same time. The feasibility of using satellite translator to realize multiple-station ranging in TT&C of spacecraft is analyzed [5]. On basis of spread spectrum ranging principle, using communication translator that translates uplink ranging signals to achieve distance-measuring for satellite is studied. A method for calibrating the delay of the forward and return direction SSA (Sband Single Access) transponder of TDRS satellites has been proposed [6]. The result of calibration is helpful for analysis of the systematic errors and improvement of the orbit determination accuracy for user spacecraft. These works provide an idea of realizing the covert communication and TT&C redundant backups at the same time. This article proposes a covert TT&C scheme which is fulfilled by multiplexing the spread spectrum signal of TT&C transponder and the signal of repeater in the same frequency bandwidth into one payload channel and transmitting to the earth station. Meanwhile, as a redundant means of on-board TT&C subsystem, it enhances the reliability and robustness of on-board TT&C subsystem and guarantees the stability of in-orbit operation of a satellite.

2 Covert TT&C Scheme Figure 1 depicts the schematic illustration of the TT&C scheme. The part in the shaded box is the covert TT&C channel, where uplink signal flows by TC antenna, receiver and downlink signal flows by transmitter, switch, OMUX and payload antenna. The part out of the shaded box is traditional TT&C channel, where uplink signal flows by TC antenna, receiver and downlink signal flows by transmitter, switch, SSPA and TM antenna. The downlink signal modulated as spread spectrum multiplexes with the repeater signal in the same frequency bandwidth into one payload channel and transmits to the earth station. The feasibility of the proposed TT&C scheme depends on a key aspect. The earth station receives the combined signal of TT&C and repeater and need to demodulate the information of TT&C and repeater, respectively. However, it needs to be discussed whether the repeater signal interferes with the TT&C signal or the TT&C signal interferes with the repeater signal, i.e., the compatibility of TT&C subsystem and payload subsystem [7].

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Fig. 1. The schematic illustration of the proposed covert TT&C scheme

2.1 Interference of Repeater Signal on TT&C Signal While weighing the anti-interference quality of spread spectrum communication system, the concept of process gain Gp is used [8], which is defined as the ratio of output SNR to input SNR [9]. Gp =

(S/N)OUT =N (S/N)IN

(1)

where N is the ratio the chips to baseband information rate. Gp represents the ability of useful signal resisting the interference by applying the spread spectrum technique, i.e., Gp is larger and the ability of resisting the interference is stronger. Furthermore, the concept of interference margin is used to weigh the ability of spread spectrum communication system operating under interference. Interference margin M j is expressed as follows [9], Gp  

Mj = Lsys

S N REC

(2)

where Gp is the process gain; L sys is the system losses defined as losses in the path; (S/N)REC is the required Signal to Noise ratio. Suppose that the transmit power of TT&C transponder is 300 mW (24.77 dBm) and the TT&C signal is modulated as BPSK and spread spectrum and Gold code is used as the pseudo-random code. The transmit power of repeater is 50 W (17 dBW) and the repeater signal is modulated as BPSK. With regard to TT&C signal, the repeater signal is referred to as the interference. Thus, TT&C subsystem must operate compatibly with payload subsystem under the condition that the transmit power of repeater is about 23 dB larger than that of TT&C transponder, i.e., interference margin M j of this system is 23 dB.

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For BPSK modulation, the bit error rate (BER) is expressed as follows [10],   2Eb BER = Q N0 where E b /N 0 is the normalized carrier-to-nose ratio and Q(x) can be written as   1 x Q(x) = erfc √ 2 2

(3)

(4)

where erfc(x) is complementary error function. Thereby, E b /N 0 should be larger than 9.6 dB while the BER of TT&C signal is required to be less than 1 × 10–5 , i.e., (S/N)REC is 9.6 dB. Meanwhile suppose that L sys is 0 dB, performing the calculation of (2) in decibel,  





S Gp dB = Mj dB + Lsys dB + N REC dB = 23 dB + 0 dB + 9.6 dB = 32.6 dB

(5)

According to (1), N should be 1819. Suppose that the baseband information rate is 100 bps and then the chip rate is 181900 cps. In order to make simulation easier, taking N = 2047 into account, it leads to the solution that Gp is 33.11 dB. Furthermore, according to (5) and (3), BER is 3.04 × 10–6 . According to the above-mentioned analysis, under the condition that the transmit power of repeater and TT&C transponder is determined, it is feasible to fulfill the multiplex of the TT&C signal and the repeater signal compatibly by varying the chip rate to change the process gain. 2.2 Interference of TT&C Signal on Repeater Signal With regard to the repeater signal, TT&C signal is referred to as the interference. According to the previous assumption in Sect. 2.1, SNR of payload subsystem is 23 dB. Performing the calculation of (3), BER is 4.43 × 10–89 which fully satisfy the bit error requirement of the payload subsystem. According to the above-mentioned analysis, under the condition that the transmit power of TT&C transponder is determined, it is feasible to fulfill the multiplex of the TT&C signal and the repeater signal compatibly by varying the transmit power of repeater.

3 Model Validation and Discussion In this section, the feasibility of the scheme is simulated by MATLAB/SIMULINK. Simulation model is constructed exactly according to that shown in Fig. 1. The model in details is presented in Fig. 2, where the part within the dotted line represents the TT&C transponder and the earth station for generating and receiving the TT&C signal; the part within the solid line represents the repeater and the earth station for generating

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B-FFT

Bernoulli Binary

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Bernoulli Binary Generator

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Gold Sequence Generator Gold Sequence Generator

Unipolar to Bipolar Converter 1

B-FFT

Spectrum Scope1

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30 dB (31.6228) Product

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dB Gain1

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Integrate and Dump

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Spectrum Scope3

Time Vector Scope

Fig. 2. The numerical model of the covert TT&C scheme

and receiving the repeater signal. Gain module is added to adjust the transmit power of TT&C transponder and repeater and an AWGN channel is adopted for transmission channel. User 1 and User 2 are used for analyzing the bit error performance of the TT&C signal and the repeater signal respectively. The TT&C signal and repeater signal is multiplexed via an AWGN channel and demodulated in the earth station and bit error performance of the covert TT&C scheme is analyzed by comparing the data transmitted at the sending end with those received at the receiving end. In the simulation, the chip rate is set to be 204700 cps and the Gold code is generated by modulo-2 addition of two m-sequences. The polynomial of these two m-sequences is expressed as follows: f1 (x) = x11 + x2 + 1

(6)

f2 (x) = x11 + x8 + x5 + x2 + 1

(7)

The transmit power of TT&C signal is set to be 24.77 dBm and the transmit power of repeater signal is set to be 17 dBW. The bandwidth of these two signals is the same. The spectrums of TT&C signal, repeater signal and these two combined signals are shown in Fig. 3(a), (b), (c). It is clearly found that the TT&C signal is completely lost within the repeater signal, which indicates that covert communication for TT&C is feasible. Figure 4(a), (b) presents the bit error rate of demodulated TT&C signal and repeater signal, respectively. User 1 detects 3 bit errors after receiving 1 × 106 -bit data, which means that the BER is 3 × 10–6 . This result indicates that the proposed covert TT&C scheme meets the requirement of BER less than 1 × 10–5 and complies with the theoretical analysis in Sect. 2.1. Meanwhile, User 2 detects no error after receiving 2.047 × 109 -bit data, which implies that the TT&C signal doesn’t cause any interference to the repeater signal, i.e., TT&C subsystem and payload subsystem can operate compatibly.

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Fig. 3. The spectrum of signal: (a) TT&C signal; (b) the repeater signal; (c) the multiplexing signal of spread spectrum signal and the signal of repeater

Fig. 4. Bit error rate after demodulation: (a) TT&C signal; (b) the repeater signal

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4 Conclusion In this paper, a covert TT&C scheme based on spread spectrum technique is proposed. The spread spectrum TT&C signal multiplexes with the repeater signal which is used to lower the probability of intercepting the TT&C signal. And then the covert communication is fulfilled and also a redundant backup of traditional on-board TT&C subsystem is provided to improve the reliability of the on-board TT&C subsystem. The feasibility of the covert TT&C scheme has been verified by checking against the numerical results obtained by MATLAB/SIMULINK and theoretical analysis. In practical applications, by adjusting the parameters such as transmit power of TT&C transponder and repeater, the chip rate, etc., the compatible operation of TT&C subsystem and payload subsystem could be implemented and the requirement of TT&C subsystem and payload subsystem can be satisfied, either.

References 1. Tan, W., Hu, J.: Spacecraft System Engineering. China Science and Technology Press, Beijing (2009) 2. Hu, H.: Research on information covert communication based on DSSS/OOK code and modulation. University of Electronic Science and Technology of China (2019) 3. Zhu, Q.: Research on covert wireless communication technology – analysis of covert communication performance with node assistance. University of Electronic Science and Technology of China (2019) 4. Qin, Y., Nie, S., Zhao, H., et al.: Integration of ranging and data transmission based on advanced orbiting system. J. Spacecr. TT&C Technol. 35(5), 409–419 (2016) 5. Chen, Y., Chen, J., Li, G.: Spread spectrum ranging technique based on communication translator. Telecommun. Eng. 53(9), 1197–1201 (2013) 6. Wang, L., Zhu, Z., Qiu, H., et al.: A method for calibration of the zero-range values of the SSA transponders of TDRS Satellites. J. Spacecr. TT&C Technol. 32(3), 224–228 (2013) 7. Guo, D., Zhang, B., Liu, A., et al.: Anti-jamming technologies and simulation system for transparent transponder in satellite communication. J. PLA Univ. Sci. Technol. 3(1), 40–44 (2002) 8. Zeng, X., Liu, N., Sun, X.: Spread Spectrum Communication and Multiple Access Technology. Xidian University Press, Xi’an (2004) 9. Tian, R.: Spread Spectrum Communication. Tsinghua University Press, Beijing (2007) 10. Li, X., Zhou, N., Zhou, L., et al.: Principle of Communication, 2nd edn. Tsinghua University Press, Beijing (2014)

A Satellite Imaging Stability Monitoring Method Based on Lunar Calibration Technology Yong-chang Li(B) , Qiang Cong, and Shun Yao DFH Satellite Co., LTD., Beijing 100094, China [email protected]

Abstract. The lunar calibration on-orbit of remote sensing satellites is an important way to improve the radiometric calibration efficiency. Aiming at the low-orbit remote satellite, a satellite imaging stability monitoring method based on lunar calibration is proposed, including calibration imaging window, satellite attitude and imaging parameters, etc. Based on three low-orbit optical remote satellites launched simultaneously in 2018, the lunar calibration experiments were carried out on July 23, 2021, and the lunar phase angle is between 7.360–7.892º. The experimental results suggest that the satellite attitude executes correctly. The reconstructed lunar images obtained by the three satellites on the ground have clear texture, and the dynamic MTF at Nyquist frequency is 0.1557. Under approximately the same lunar phase conditions, the radiation characteristics of the lunar surface images obtained by the three satellites are very close (better than 20 DN values). The results verify the correctness of lunar calibration and imaging stability monitoring method proposed, which can provide reference frame for monitoring the space camera imaging stability. Keywords: Remote satellite · Attitude maneuver · Lunar calibration · Imaging stability

1 Introduction After the optical remote satellite is launched into orbit, the imaging performance of the satellite will change due to the impact vibration during launch and the working environment in orbit changes. In order to monitor the on-orbit imaging stability of satellites for a long time, the on-orbit calibration of remote sensing satellites must be carried out. In-orbit calibration methods of traditional optical remote sensing satellite mainly include spaceborne calibration, vicarious calibration and cross-calibration, which have various shortcomings. As the products of remote sensing satellites gradually move towards quantitative operational application, higher requirements are put forward for the imaging stability of remote sensing satellites. Therefore, more convenient and efficient imaging stability monitoring methods are urgently needed to meet this demand [1–4]. The spectral characteristics of the moon are related to the absorption and reflection of solar spectrum, and the light intensity has good stability which is within the dynamic range of remote sensor. The moon is considered as the most ideal on-orbit calibration © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 574–580, 2023. https://doi.org/10.1007/978-981-19-9968-0_69

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source for remote sensing satellites. Thus, lunar calibration on-orbit is an important way to monitor the imaging stability of detectors. At present, a considerable number of satellites have carried out radiometric calibration and radiation performance monitoring using the moon, such as low-orbit satellites MODIS [5], SeaWIFS [6], VIIRS, HYPERION and Pleiades [7], and geostationary satellites MSG and SEVIRI, etc. They mainly use the lunar radiation model to monitor the long-term attenuation of the camera’s radiation performance and the radiation difference between spectrum segments, which effectively guarantees the application of the operational calibration of satellites in orbit. In recent years, the on-orbit lunar calibration carried out by China is mainly aimed at polar orbit and geostationary orbit satellites, it is still a blank field for low-orbit remote sensing satellites before this study [8–10]. Therefore, a satellite imaging stability monitoring method based on lunar calibration technology is put forward, which investigates the lunar calibration imaging window, satellite attitude and camera imaging parameters. The research results are applied to the three low-orbit remote satellites launched simultaneously in 2018. Under the similar lunar phase conditions, the imaging stability of the three satellites after three years in orbit is evaluated, which verifies the correctness of the proposed method.

2 Key Technology of Lunar Calibration 2.1 The Imaging Window of Lunar Calibration Generally, the imaging window of lunar calibration is selected when the phase angle of the lunar is between −90º–+ 90º. Satellites in Sun-synchronous orbit run 13–14 cycles per day. The observation window of each cycle is appeared near the earth’s poles. On this basis, the appropriate imaging time of the camera for the lunar calibration is determined synthetically by taking into account such factors as the satellite tt&c plan, the earth imaging mission plan, the availability of satellite sensitivity, and the satellite energy situation. The STK simulation results of visibility between the satellite with the sun, moon and TT&C ground stations are shown in Fig. 1.

Fig. 1. The STK Visibility between the satellite with the sun, moon and TT&C ground stations

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2.2 Satellite Attitude for Lunar Calibration The attitude angle of satellite in J2000 coordinate system is calculated by attitude matrix. The attitude angle will produce singular points when using Euler axis. Therefore, this paper adopts quaternion method, and according to the simulation scene, MATLAB program is written to solve attitude matrix, which can further solve the satellite attitude four elements in the simulation scene time. It can be seen from Fig. 2 that the quaternion is smooth without singularities.

Fig. 2. Attitude quaternion simulation curve

The four elements are written into.a file and imported into STK, the simulation scene is shown in Fig. 3. The white vector is the direction of the moon, and the red one is the Z axis vector of the satellite body. It can be seen that the satellite can ensure that the Z axis of the satellite points to the moon during operation, and the attitude matrix and attitude angle calculation process are correct.

Fig. 3. Simulation of three-axis attitude angle for lunar calibration

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2.3 Imaging Parameters of the Camera To avoid the influence of image motion on imaging quality, high-resolution optical satellites usually need to match the camera’s integral time. The moon has a small field of view to the detector, which leads to a short time for the camera detector to sweep across the lunar. Therefore, this paper proposes a new integral time matching strategy with adjustable over-sampling coefficient. Equation (1) is the calculation method of the camera integral time during the lunar calibration. Tint_moon =

IFOV k · ωsat

(1)

where IFOV represents the instantaneous field of view, k represents the over-sampling coefficient, ωsat represents the angular velocity of the satellite around the pitching axis. Equations (2) and (3) are respectively represent the influence of drift angle matching error and attitude stability on MTF (Nyquist frequency): MTFdrift angle = sinc(N/2 · tanβ)

(2)

2 2 2 MTFzt = e−2π (ω·f ·N ·Tint_moon ) υ

(3)

where N represents the camera integral series. It can be seen that when the drift angle has matching error and the attitude stability is certain, increasing the integral series of the camera will lead to the decrease of the image MTF. Usually, the image motion generated by the attitude should be no more than 0.5 pixel. Therefore, the 6-stage integration for lunar calibration is advised when the satellite attitude stability is below 5 × 10–4 °/s.

3 Imaging Stability Monitoring Experiment 3.1 Experimental Condition On July 23, 2021 (close to the full moon), based on three low-orbit remote satellites launched simultaneously in 2018, satellite lunar calibration and imaging stability monitoring experiments were carried out to evaluate the imaging stability of the three satellites after three years in orbit. The specific experimental information is shown below (Table 1). Table 1. The experimental information Date

The time that camera points to the center of the lunar/(BJT) lunar phase angle/(°)

Satellite 01 2021/7/23 21:44:18

7.667°

Satellite 02 2021/7/23 21:13:30

7.892°

Satellite 03 2021/7/23 22:20:00

7.360°

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3.2 Lunar Calibration The telemetry data of the angle between the pointing of the star sensor and the sun, the earth-atmosphere radiation is shown in Fig. 4. It shows that during the lunar calibration of the three satellite, the angles between the star sensor and the sun, the earth-atmosphere radiation are all greater than 30°, which effectively avoids stray light entering the satellite sensors, and meets the constraint condition that at least two star sensors are available, ensuring the attitude control accuracy in the process of the lunar calibration. Therefore, the implementation of satellite attitude is consistent with the designed.

Fig. 4. The available star sensor in lunar calibration process

The original image data can be reconstructed into a lunar disk by the ground system. The image data obtained from the three satellites in this lunar calibration experiment is shown in Fig. 5. The spatial resolution of the lunar observation is better than 1.18 km, which has better index characteristics for China’s geostationary and polar-orbit satellites such as FY-2 and FY-3.

Fig. 5. The lunar calibration images

As can be seen from Fig. 6, the on-orbit dynamic MTF at Nyquist frequency calculated by slanted-edge method is 0.1557. Moreover, the satellite telemetry of each subsystem works well, which further verifies the correctness of the lunar calibration method proposed in this paper.

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Fig. 6. The dynamic MTF at Nyquist frequency calculated by slanted-edge method

3.3 Imaging Stability Monitoring The three-dimensional distribution of the lunar surface DN value of the three Satellite’s lunar calibration images is shown in Fig. 7. The average lunar surface DN value of Satellite 01 is 568.16, that of Satellite 02 is 578.07, and that of Satellite 03 is 585.43. It can be seen that under the similar lunar phase conditions, the radiation characteristics of the lunar images obtained by the three satellites are very close, which is within 20 DN values. Therefore, it can be judged that the three satellites after three years in orbit have good imaging stability.

Fig. 7. Statistics of radiation characteristics of lunar surface images of 3 satellites

4 Conclusion A satellite imaging stability monitoring method based on lunar calibration technology is proposed for low-orbit remote satellites. The imaging window, satellite attitude and camera imaging parameters are investigated in detail. On July 23, 2021, by using the method

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proposed, the satellite lunar calibration and imaging stability monitoring experiment were implemented based on three low-orbit remote satellites launched simultaneously in 2018. The experimental results suggest that the satellite attitude executes correctly. The reconstructed lunar images obtained have clear texture, and the dynamic MTF at Nyquist frequency is 0.1557. Under the similar lunar phase conditions, the radiation characteristics of the lunar surface images obtained are very close (better than 20 DN values). The results verify the correctness of lunar calibration and imaging stability monitoring method proposed.

References 1. Zhang, Z., Wang, W., Wang, H., et al.: On-board vicarious calibration of FY-3C UV total ozone unit. Opt. Precis. Eng. 27(2), 61–68 (2019) 2. Kieffer, H.H., Stone, T.C.: The spectral irradiance of the Moon. Astron. J. 129(6), 2887–2901 (2005) 3. Niu, M., Chen, F.: Methods of on-orbit calibration of satellite radiometer reflective solar bands using the moon. Remote Sens. Technol. Appl. 33(2), 337–341 (2018) 4. Zhang, L., Zhang, P., Hu, X., et al.: Comparison of lunar irradiance models and validation of lunar observation on Earth. J. Remote Sens. 1(6), 864–870 (2017) 5. Eplee, R.E., Sun, J.Q., Meister, G., et al.: Cross calibration of SeaWiFS and MODIS using on-orbit observations of the Moon. Appl. Opt. 50(2), 120–133 (2011) 6. Hooker, S., Mcclain, C.R., Holmes, A.: Ocean color imaging CZCS to SeaWiFS. Mar. Technol. Soc. J. 27(1), 3–15 (1993) 7. Lebegue, L.: Using exotic guidance for Pleiades-HR image quality calibration. In: Heipke, C. (ed.) The 21st ISPRS, Germany, pp. 13–18. ISPRS (2008) 8. Wu, R., Zhang, P., Yany, Z., et al.: Monitor radiance calibration of the remote sensing instrument with reflected lunar irradiance. J. Remote Sens. 20(2), 278–289 (2016) 9. Chen, L., Zhang, P., Wu, R., et al.: Monitoring radiometric response change of visible band for FY-2 geostationary meteorological satellite by lunar target. J. Remote Sens. 22(2), 211–219 (2018) 10. Niu, M.: Research on the lunar calibration technologies of optical remote sensing radiometers, pp. 7–8. Shanghai Institute of Technical Physics, Shanghai (2018)

Design and Verification of mK Temperature Fluctuation Control for Gravity Gradiometer Wei Liu(B) , Yupeng Zhou, and Tinghao Li Beijing Key Laboratory for Space Thermal Control Technology, Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. Aiming at the requirement of high temperature stability of 10 mK/200 s of the gradiometer, a thermal control design is taken with the establishment of component-level resistance-capacitance filtering network, system-level enhanced thermal insulation design and high precision temperature control technology. The thermal analysis and the thermal balance test results show that the temperature stability of the gradiometer is lower than 8 mK/200 s and meet the required precision. The design provides a reference for the thermal control design of the other parts that require high-precision and high-temperature stability. Keywords: Gradiometer · mK-level high temperature stability · Resistance-capacity filtering network

1 Introduction The gravity gradiometer consists of three pairs of orthogonal high-precision electro-static suspension accelerometers. The high-precision measurements of global gravity field and steady ocean circulation can be obtained by combining the differential modes of the three pairs of accelerometers [1]. The stability of the relative position of the sensor head is a necessary condition to ensure the accuracy of the gravity difference measurement, and the thermal deformation caused by the temperature difference between different parts is the main reason for the change of the relative position of the sensor head. Therefore, the temperature stability index of gravity gradiometer is very stringent. The temperature gradient of the gradiometer sensor heads of same axis the ESA’s GOCE was required to be less than 0.5 °C [2]. The temperature fluctuation of the sensor on a satellite was controlled within ±1 °C [3], The temperature stability requirement of one instrument of the Taiji-1 satellite was T ± 0.1 °C/1000 s [4], The temperature stability requirement of the core load of the Tianqin-1 satellite was better than ±50 mK/orbit [5]. The gradiometer of a satellite requires that the temperature stability is less than 10 mK/200 s, which is several times higher than the requirements of the spacecraft components currently developed in China. According to the characteristics, configuration layout and orbital attitude of the gradiometer, this paper proposes the construction of a component-level thermal resistancecapacitance filtering network, a system-level thermal reinforced insulation design and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 581–589, 2023. https://doi.org/10.1007/978-981-19-9968-0_70

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a high-precision multi-stage active thermal control design, which realizes the mK-level high-stability thermal control for the gradiometer, and the effect is verified by thermal analysis and thermal balance test.

2 The Gradiometer The gradiometer consists of the ultra-stable structure, where 6 gradiometer sensor heads mounted on, and electronic equipment, shown in Fig. 1(left). The ultra-stable structure is composed of the gradiometer substrate, X-axis panel and Y-axis panel. The temperature requirement of the ultra-stable structure is 20 °C–25 °C, its temperature stability is better than 10 mK/200 s. It is connected to the adjustment panel and the support tray through three X brackets. Electronic equipment with high heat consumption is installed on the electronic equipment board, and transmits the signal to the sensor head through the cable. Gradiometer

X X-axis Panel Gradiometer sensor head

Z

Y

Electronic equipment

Y-axis Panel Gradiometer-frame Gradiometer substrate

X Bracket ··

Adjustment Panel Support Tray Electronic equipment Electronic Panel

Fig. 1. Structure of the gradiometer (left) and its distribution (right).

The gradiometer is installed near the center of mass of the satellite, and the sun-facing deck of the satellite is arranged with body-mounted solar cell shown in Fig. 1(right). The orbit is a Sun-synchronous dawn-dust orbit with an altitude of 250 km. The thermal control design in this paper mainly involves the gradiometer composed of six gradiometer sensor heads and the ultra-stable structure.

3 Methods of Thermal Control Design for the Gradiometer The gradiometer sensor head has no power consumption, so the thermal control design of the gradiometer needs to take measures from increasing the thermal capacity and reducing the boundary flux. Increasing the thermal capacity starts from increasing the mass of the components; reducing the boundary flux starts from increasing the component thermal resistance and reducing boundary temperature fluctuations [6]. Increasing thermal capacity and the thermal resistance are achieved by component-level thermal control design, and reducing boundary temperature fluctuations is achieved by system-level thermal control design.

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3.1 Build a Component-Level Resistance-Capacitance (RC) Filtering Network In order to ensure the high temperature stability, the heat exchange between the gradiometer and the environment should be minimized. Multi-level reinforced thermal insulation design is carried out from radiation and conduction to establish a large thermal resistance of the gradiometer. In case of satisfying mechanical conditions, the thermal capacity of the components should be increased as much as possible, and the thermal resistance capacity filtering network is built, which can effectively filter the effects of temperature fluctuations in the cabin, structure, and electronic equipment. Shown in Fig. 2.

Satellite Deck Aluminized Film MLI (Radiation Resistance Network) Thermal Control Cover Adjustment Panel Conductivity Insulator (Conductivity Resistance Network)

X-axis Plate (Capacity of Network) Gradiometer Substrate (Capacity of Network) X Bracket Conductivity Resistance Network Support Tray (Capacity Network)

Fig. 2. Diagram of component-level resistance-capacity filtering network

Reinforced Radiation Insulation Design • Design the thermal control cover and adjustment plate to establish a closed and independent space for the 6 sensor heads and the ultra-stable structure. The inner and outer surfaces of the thermal control cover and adjustment plate are covered with multilayer insulation (MLI) that has the low-emissivity film, which effectively suppresses the radiative heat exchange of the satellite structure and the support tray. • The support tray, X brackets, the electronic equipment and the installation board facing the gradiometer are also covered with the same multi-layer insulation to suppress the temperature fluctuation of the gradiometer caused by the radiative heat loss, and at the same time reduce the influence from the temperature fluctuation of the cabin.

Reinforced Conduction Insulation Design • The support tray and the satellite structure are connected by corner joints whose two sides are filled with the fiberglass insulator. The screws are made of titanium alloy. The area of the corner joints and the insulator should be as small as possible, and the thickness should be as large as possible [7], and the influence of the temperature fluctuation of the deck on the support tray can be suppressed by increasing the thermal resistance of heat conduction.

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• The main function of the three X-brackets is mechanical decoupling, while taking into account the thermal control requirements. The special connection form makes the contact thermal resistance significantly increased. The configuration of “X” increases the conduction distance. The low thermal conductivity titanium alloy selected greatly increases the thermal conductivity resistance. The support tray has a low conductivity.

Method to Increase Thermal Capacity The ultra-stable structure, adjustment panel and support tray adopts a mechanical and thermal integration design, taking into account the requirements of high thermal stability of the structure and the large thermal capacity of thermal control. The honeycomb sandwich panel adopts a new type of low-expansion coefficient C-C composite. The thickness of the panel is 3 mm/1.5 mm, and the thickness of the honeycomb is 64/37 mm, both are much thicker than traditional ones, so the mass increases by nearly 20 kg, which has a strong damping effect on heat flux changes. 3.2 System-Level Thermal Insulation Design The system-level thermal insulation design can effectively minimize the influence of the external heat flux and a relatively stable boundary temperature is provided for the gradiometer. • For the body-mounted solar cell configuration, a high thermal resistance polyimide foam material (shown in Fig. 3) is used between the solar cell and the honeycomb panel. So even when the temperature of the solar cell changes drastically within ±120 °C in the orbital period, the temperature inside the satellite can be reduced to within ±10 °C. • Low emissivity film is used on the inner surface of the deck to reduce the radiative heat exchange of the satellite deck and the gradiometer.

Fig. 3. Structure profile of solar array mounted body

3.3 High-Precision Multi-stage Active Thermal Control Method From the selection of temperature sensor, temperature measurement acquisition circuit, heating loop control algorithm & control strategy, the system is designed according to the requirements of mK-level high precision.

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• We choose high-precision platinum resistance components with good consistency and high stability (accuracy < ±0.005 °C, at 15 °C–35 °C range) as the temperature sensor. • Develop a high-precision and high-stability temperature measurement acquisition circuit, using four-wire platinum resistance and constant current source temperature measurement system [8], so that the temperature measurement stability is better than 5 mK/90 min, and the resolution reaches 1 mK. • Heating loop control strategy adopts PID closed-loop control algorithm, the output power is adjusted by adopting the 10-bit pulse width modulation (PWM), and the heating power resolution is 1/1024 of the loop power (10 W). • In order to avoid local temperature distortion, the heaters should be mounted on the thermal shield other than directly on the gravity gradiometer and ultra-stable support. In this project temperature control heaters and platinum thermistors are set on the thermal shield, the adjustment panel, X brackets and the sup-port tray (see Fig. 4). And the temperature control threshold is 22.5 ± 0.1 °C.

Thermal Control Cover Heater

Adjustment Panel Heater

X-Bracket Heater Support Tray Heater

Fig. 4. Structure profile of solar array mounted body

3.4 Control Method of Cable Heat Loss Due to the needs of signal transmission and control, cables are needed to connect the gradiometer and the electronic units outside the thermal shield, and heat leakage through cables must not be ignored. The analysis shows that the temperature fluctuation of gravity gradiometer caused by cables untreated may be as high as 0.15 °C/200 s, which is far beyond the index requirement of 10 mK/200 s. Special de-sign is taken for the cables. Firstly, the cables are coiled on the ultra-stable structure before leading out the thermal shield. Then the outer part is wrapped with MLI to reduce thermal coupling with the surrounding environment. In this way, the temperature fluctuation caused by cable heat leakage is well suppressed.

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4 Thermal Analysis 4.1 The Thermal Mathematical Model and Meshing THERMAL DESKTOP software is used for thermal analysis, so a detailed model of the gradiometer is established, shown in Fig. 5.

Fig. 5. Thermal analysis model of the gradiometer

4.2 Thermal Performance According to the space heat flux, the extreme cases of the maximum β angle of 87° (hot case) and the minimum β angle of 58° (cold case) are analyzed for the gradiometer. The calculation results are shown in Table 1. Table 1. Temperature thermal analysis of the gradiometer Device name

Requirements

Minimum β (58°)

Maximum β (87°)

Sensor head

Temperature: +20–+25 (°C)

20.110–20.793

21.854–22.023

Stability: ≤10 (mK/200 s)

2.853

0.252

Temperature: +20–+25 (°C)

20.140–20.765

21.751–22.065

Ultra-stable structure

At the minimum β angle, the satellite is in the orbit with shadow, the heat flux changes relatively greatly, and the temperature stability of the gradiometer sensor heads fluctuates greatly (2.853 mK/200 s), the analysis results are shown in Fig. 6(Left) where G is short for the gradiometer sensor head. At the maximum β angle, the satellite is in the orbit without shadow, the change of the heat flux is relatively small, the temperature stability of the gradiometer is very good, and the fluctuation is quite small (0.252 mK/200 s). The analysis results are shown in Fig. 6(Right). Both meet the requirement of better than 10 mK/200 s.

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Fig. 6. Temperature stability when β = 58º (Left) & when β = 87º (Right)

5 Thermal Balance Test 5.1 Overview The thermal balance test of the gradiometer was carried out in order to verify the thermal design of the gradiometer (see Fig. 7). The test uses a cabin to imitate a satellite deck whose inner surface is covered by heater loops to simulate boundary temperature. The temperature sensor of the heater control loop adopts platinum resistance, and other temperature measurements use thermocouples. The thermal balance test cases of the gradiometer are consistent with the analysis conditions, including the cold case when β = 58° and the hot case when β = 87°. Thermal Control Cover

Gradiometer Substrate

Cabin

Fig. 7. Module of the gradiometer

5.2 Analysis of Test Results The maximum difference of the six gradiometers at the same time is 0.354 °C, see Table 2 for details. The temperature stability of the gradiometer in the cold case is shown in Fig. 8(Left), the maximum value is 8 mK/200 s; the temperature stability of the gradiometer under the hot case is shown in Fig. 8(Right), the maximum value is 6 mK/200 s, both meet the 10 mK/200 s requirement. The thermal balance test results are in the same order as the thermal analysis results. Compared with the simulation analysis, the temperature stability fluctuation of test is larger, for the influence of various uncertainties such as heat loss during the test.

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Equipment Requirements Cold case of temperature Test results Sensor head

Hot case

Cold case

Hot case

Analysis value

Range: + 20.528–20.882 21.489–21.701 20.110–20.793 21.854–22.023 20 °C–+25 °C Stability: ≤0 mK/200 s

8.00

6.00

2.853

0.252

Fig. 8. Temperature stability of the gradiometer in the cold case (Left) & the hot case (Right).

6 Conclusions This paper proposes the construction of a component-level RC filter network combined with a system-level reinforced thermal insulation design. The design method realizes the high-stability thermal control design of mK level. The results of mathematical simulation analysis and component thermal balance test show that the maximum fluctuation of temperature stability is 8 mK/200 s, which meets the requirement of 10 mK/200 s and fully verified. The correctness and feasibility of the mK-level high-stability thermal control design of spacecraft components can provide a reference for passive high-stability thermal control design.

References 1. Rummel, R.: GOCE gravitational gradiometry. J. Geodes. 85(11), 777–790 (2011) 2. Valentini, D.: GOCE instrument thermal control. In: 36th International Conference on Environmental Systems. LNCS, vol. 2006-01-2044, pp. 17–20. SAE Document, Virginia (2006) 3. Sun, P.: Temperature control method and test verification for integrated star sensor. Spacecr. Eng. 27(2), 119–123 (2018) 4. Liu, H.: Application of precision thermal control technology on Taiji-1 satellite. J. Space Sci. 41(2), 337–341 (2021)

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5. Wei, R.: Ground experimental verification of high-stability temperature control system and flight data analysis. In: The 8th International Conference on Signal and Information Processing, Networking and Computers (ICSINC 2021). LNCS, vol. 1, pp. 117–124. ICSINC committee, Jinan (2021) 6. Miao, J.: Spacecraft Thermal Control Technology, 1st edn. Beijing Institute of Technology Press, Beijing (2018) 7. Tong, Y.: Thermal design and validation for spacecraft high-stability structure. Spacecr. Eng. 27(3), 61–66 (2018) 8. Hu, P.: High precision Pt-resistance temperature measurement system. J. Opt. Precis. Eng. 22(4), 988–995 (2014)

A Perigee Orbit Change Method for Geostationary Satellite ShuHua Zhang(B) , Heng Yao, JianPing Li, and ZhongMou Lei Beijing Institute of Control Engineering, Beijing 100094, People’s Republic of China [email protected]

Abstract. After the separation from rocket, geostationary satellite needs to go through multiple apogee orbit changes and perigee altitude needs to be raised to synchronous orbit altitude. However, if the rocket fails, the apogee altitude of the satellite will be much lower than the synchronous orbit altitude. To solve this problem, this paper proposes a method of geostationary satellite perigee orbit change, gives the corresponding operation process, and solves the problem of geostationary satellite perigee orbit change. This method has been successfully applied to a geostationary satellite. Keywords: Geostationary orbit · Perigee · Orbit change

1 Introduction After the geostationary orbit satellite is launched, the rocket will send it into a transfer orbit with a perigee of about 200 km and an apogee greater than or equal to the geostationary orbit altitude. In the transfer orbit section, the satellite uses its own orbit change engine to carry out four to five orbit change maneuvers, raising the perigee of the satellite to the height of the geostationary orbit, and reducing the orbit inclination to near zero [1–5]. Figure 1 shows the orbit change process of a geostationary satellite under normal conditions. If there is a problem in the flight control of the launch vehicle during the active phase, the apogee altitude of the satellite will not reach the synchronous orbit altitude after the separation of the satellite and the rocket. At this time, the satellite needs to use its own orbit change engine to carry out perigee orbit change and raise the apogee altitude to the geostationary orbit altitude. Then the apogee orbit change is carried out according to the predetermined schedule, and the satellite orbit can be changed into a geostationary orbit. In previous launch missions at domestic and abroad, the failure to send satellites into the predetermined orbit due to rocket failure occurred from time to time. For example, the Indonesian PALAPA-D satellite launched in 2009 has only 21000 km apogee due to rocket failure, which is far below the synchronous orbit altitude. The satellite reached the geostationary orbit only after implementing the perigee orbit change strategy for many times. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 590–597, 2023. https://doi.org/10.1007/978-981-19-9968-0_71

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Orbit after 1st ~5th maneuver

1st ~5th apogee maneuver Separation & Sun acquisition

Sun

Orbit before 1st ~5th maneuver

Fig. 1. Orbit change process of geostationary satellite under normal conditions

If the satellite is equipped with a star sensor, it can easily establish the ignition attitude and implement the perigee orbit change, that’s because the star sensor has the ability to capture the whole celestial sphere. However, if the star sensor fails or the satellite is only equipped with earth sensor, it is very difficult to establish the ignition attitude, which may lead to the failure of orbit transfer. This paper provides a perigee orbit change method for geostationary satellites, which is applicable to geostationary satellites that are not equipped with star sensors or whose star sensors fail, and can effectively raise the satellite apogee height to the geostationary orbit height if they do not enter the predetermined orbit after launch.

2 Geostationary Orbit Satellite Working Condition Analysis 2.1 Satellite Profile Taking a satellite as an example, the basic situation of the control object is introduced. The control system adopts the full orbit three-axis stabilization mode, and adopts the two-branch unified propulsion system and V+L momentum wheel as the actuator. The control computer is used to control the attitude and orbit of the satellite. The orbit change engine is located on the −Z plane of the satellite and generates thrust in the +Z direction. By controlling the attitude of the satellite, the thrust direction of the orbit change engine is the same as or opposite to the direction of the satellite movement, so as to accelerate or decelerate the satellite. In the transfer orbit, the control modes used by the satellite mainly include the following four modes: (1) Sun acquisition mode: after the separation from the rocket, the satellite will automatically switch to the sun acquisition mode and use the two sun sensors installed on the satellite-Z plane to capture the sun to ensure the satellite energy supply. (2) Earth acquisition mode: in the earth acquisition mode, attitude and angular velocity offset data are injected from the ground, so that the satellite changes from the cruise state to an axis in the XOZ plane of the satellite pointing to the sun, and the satellite slowly rotates around the axis to search the earth. When the earth sensor on the satellite +Z plane sees the earth, it turns to the earth pointing attitude.

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(3) Earth pointing mode: in the earth pointing mode, the earth sensor measures the roll and pitch angles, and the sun sensor measures the yaw angle. Generally, the ignition attitude of the orbit control engine is established in the earth pointing mode. (4) Apogee firing mode: the apogee firing mode is used to keep the satellite attitude stable when the orbit control engine is ignited and overcome the interference torque generated by the orbit control engine. 2.2 Routine Working Process of Apogee Orbit Change The orbit change engine is installed on the satellite −Z plane. When the satellite changes from the earth pointing mode to the apogee firing mode, the apogee firing attitude must be established first. In the earth pointing mode, first rotate the satellite around the Z axis so that the yaw angle is equal to the apogee ignition attitude, and then measure the three-axis attitude with gyro integration. The satellite rotates 90° around the Y axis so that the −X axis points to the ground. Figure 2 shows the process of the satellite establishing the apogee firing attitude. Satellite move direction

Equator Plane

-Z X X

Z

Z

X Y

Y

Z Y

Fig. 2. The satellite establishing the remote ignition attitude under normal conditions

In the apogee firing mode, the satellite uses two sun sensors on the −Z plane to measure the attitude around the X-axis and Y-axis, and uses the gyro integral as the attitude reference around the Z-axis, or uses the gyro rate integral as the attitude reference for all three axes. The constant drift of the gyro rate integral output and the influence of the orbital angular velocity are solved through the calibration and compensation of the gyro constant drift. After the orbit control engine completes ignition, it will switch to the sun acquisition mode or the earth pointing mode.

3 Perigee Orbit Change Control Method The perigee orbit change method of the geostationary satellite platform provided in this paper needs to complete the following steps:

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(1) After entering the transfer orbit, the control system works in the sun acquisition mode to maintain the sun orientation. In order to use the earth to establish the ignition attitude and obtain the accurate gyro constant drift, the first step is to switch to the earth acquisition mode, calibrate the gyro drift in the earth acquisition mode [6], and determine the gyro constant drift. (2) When the satellite is close to the apogee, confirm that the orbit height meets the availability conditions of the earth sensor. Before the corresponding positions (X0, Y0, Z0) as shown in Fig. 4, the earth sensor starts up, establishes the pitch angle offset and the roll and yaw axis angular velocity offset according to the orbit position of the satellite, and starts the earth search. (3) After the earth sensor searches for the earth signal, sends a command to change the working mode to the earth pointing mode. At this time, the roll and pitch angles are measured by the earth sensor, and the Z axis of the satellite points to the earth center. The yaw angle is measured by the sun sensor and points to the direction of the sun. As shown in Fig. 4 (X0, Y0, Z0). (4) In the earth pointing mode, the gyro drift calibration is performed [7]. After calibration, the calibration results of ground gyro drift, earth acquisition mode and earth pointing mode are compared. (5) In the earth pointing mode, commands are sent to inject the yaw sun attitude coefficient, and adjust the XOZ plane of the satellite body parallel to the orbit plane shown in Fig. 4 (X1, Y1, Z1). At this time, Y1 is just perpendicular to the orbit plane. (6) Inject the gyro drift compensation value, which includes the gyro’s own constant drift and the influence of orbital angular velocity. Switch to earth pointing mode using gyro to measure three-axis attitude and prepare to establish the ignition attitude. At this time, the three-axis attitude angles of the satellite are measured by gyro angular rate integration. It is necessary to ensure that the earth sensor can output the correct attitude during step (4)–(6). The calculation method of gyro drift compensation value is as follows. The drift caused by the orbital angular velocity (ωby ) is: ⎡

⎤ ⎡ ⎤ Bgx1 0 ⎢ Bgy1 ⎥ = Cbo · ⎣ −ωby ⎦ ⎢ ⎥ ⎢B ⎥ 0 gz1  Euler angle ϕ θ ψ corresponds to the three-axis attitude angle of the body system relative to the orbital system, and in the above equation Cbo is: ⎡

Cbo

⎤ cosθcosψ − sinϕsinθsinψ cosθsinψ + sinϕsinθcosψ −cosϕsinθ =⎣ −cosϕsinψ cosϕcosψ sinϕ ⎦ sinθcosψ + sinϕcosθsinψ sinθsinψ − sinϕcosθcosψ cosϕcosθ

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The Cbo expression is substituted into the drift caused by the orbital angular velocity to obtain: ⎡ ⎤ ⎡ ⎤ Bgx1 cosθsinψ + sinϕsinθcosψ ⎢ Bgy1 ⎥ = −ωby ⎣ ⎦ cosϕcosψ ⎢ ⎥ ⎢B ⎥ sinθsinψ − sinϕcosθcosψ gz1

Therefore, the compensation value is calculated as follows (the first term on the right of the equation is the constant drift of the gyro itself): ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ Bgx0 Bgx1 Bgx ⎢ Bgy ⎥ = ⎢ Bgy0 ⎥ + ⎢ Bgy1 ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢B ⎥ ⎢B ⎥ ⎢B ⎥ gz gz0 gz1 (7)

(8)

(9)

About 4–5 min before the satellite arrives at the apogee, conduct pitch attitude offset to make the satellite rotate 90° around the −Y axis to complete the establishment of ignition attitude. When the satellite arrives at the apogee, it is just in the attitude shown in Fig. 4 (X2, Y2, Z2), that is, the −Z axis points to the orbital forward direction and +X points to the earth. Reinject the gyro drift compensation value, which only includes the constant drift of the gyro itself. The main purpose is to keep the satellite attitude unchanged in the inertial coordinate system. As shown in Fig. 4, during the whole process of satellite movement from apogee to perigee, the attitude always maintains the direction of (X2, Y2, Z2) in inertial space. When the satellite arrives near the perigee, the ground station injects the Start

Gyro calibration in earth acquisition mode

Rotate 90 degrees around -Y axis and establish the ignition attitude 4~5 minutes before apogee

Earth acquisition Transfer to earth pointing mode

After rotating to the right position, inject the gyro drift compensation(only the gyro constant drift)

Gyro calibration in earth pointing mode Inject the yaw sun coefficient to rotate the yaw angle, making the XOZ plane in the orbit plane

Transfer to earth pointing mode and inject gyro drift compensation (including the gyro constant drift and orbit velocity drift)

When reaching the perigee almost, inject gyro drift compensation (including the gyro constant drift and orbit velocity drift)

Transfer to apogee firing mode and start firing at perigee Transfer to sun acquisition mode End

Fig. 3. Flow chart of perigee orbit change

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drift compensation value again, which includes the gyro constant drift and the orbital angular velocity. (10) At the ignition point of the engine, the operating mode of the control system changes from the earth pointing mode to the apogee firing mode. At this time, the three-axis attitude angle is still measured by the gyro angular rate integration. After the propellant sinks to the bottom, the orbit change engine starts to ignite. (11) After the ignition, the satellite will switch to the sun acquisition mode to ensure energy security. The perigee orbit change process is shown in Fig. 3 [8–11]. Apogee +Z2 +Y1

+X 1

+X2

+Z 1

+X 0

Satellite

+Z 0

+Z2 +X2

+Z2

Orbit Plane Earth

+X2

-X2 Start IgniƟon

+Z2

Stop IgniƟon

IgniƟon period Perigee

Fig. 4. Perigee orbit change process diagram

4 Perigee Orbit Change Effect Taking a geostationary satellite as an example, due to the failure of the carrier rocket, the satellite entered a transfer orbit with a perigee height of 196 km, an apogee height of 16420 km and an inclination of 25°, and the apogee height was about 26000 km lower than expected. In order to facilitate satellite measurement and control, a apogee orbit change was carried out at the apogee, raising the perigee height of the satellite to 1000 km. Subsequently, the satellite used the perigee orbit change method described in this paper to carry out five perigee orbit changes, lifting the satellite apogee to the geostationary orbit altitude. Table 1 shows the orbital parameters before and after perigee orbit change. After the five perigee orbit changes, the apogee height of the satellite orbit was 35541.243 km, reaching the height of the geostationary orbit, in line with expectations.

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Perigee orbit change times

Time

Semi major Eccentricity axis (km)

Inclination angle (degree)

R.A.A.N. (degree)

Aug. perigee (degree)

Mean anomaly (degree)

1

Before control

15075.711

0.5114

24.33

145.52

179.18

351.98

After control

16099.699

0.5419

24.16

145.49

179.57

9.64

Before control

16160.485

0.5441

24.34

144.84

180.36

348.01

After control

18001.281

0.5902

24.04

144.73

180.34

10.32

Before control

18035.270

0.5916

24.22

143.72

182.32

348.79

After control

19987.527

0.6305

24.01

143.81

181.56

4.11

Before control

19943.293

0.6303

24.06

142.93

182.83

348.81

After control

22008.227

0.6638

23.91

143.08

181.53

0.45

Before control

21976.652

0.6635

23.98

141.91

183.38

341.41

After control

24730.438

0.6950

23.91

142.16

179.91

355.12

2

3

4

5

5 Conclusion Aiming at the problem that the geostationary satellite fails to enter the predetermined orbit due to the launch vehicle failure, this paper proposes an orbit control method of perigee for orbit lifting. Through the practical verification and application of on orbit satellite, this method can effectively increase the apogee altitude of satellite orbit to the height of geostationary orbit, reduce the consumption of propellant, and prolong the service life of satellites. The method is easy to implement, and has universality and portability.

References 1. Zhang, R.: Satellite Orbit Attitude Dynamics and Control. Beijing University of Aeronautics and Astronautics Press, Beijing (2006). (in Chinese) 2. Tu, S.: Satellite Attitude Dynamics and Control. China Aerospace Publishing Press, Beijing (2001). (in Chinese)

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3. Xie, Y., Lei, Y., Guo, J.: Spacecraft Dynamics and Control. Beijing University of Science and Engineering Press, Beijing (2018). (in Chinese) 4. Li, H.: Geostationary Satellite Orbit and Common Position Control Technology. National Defense Industry Press, Beijing (2010). (in Chinese) 5. Lv, Z., Lei, Y.: Satellite Attitude Measurement and Determination. National Defense Industry Press, Beijing (2013). (in Chinese) 6. Sun, B., Li, T.: Gyro calibration of three - axis stabilized communication satellite in earth search mode. Control Eng. 4, 1–5 (1994). (in Chinese) 7. Sun, B., Li, T.: Gyro calibration of three - axis stabilized communication satellite with earth pointing. Control Eng. 3, 23–26 (1994). (in Chinese) 8. Wu, H., Shen, S.: Application and theoretical basis of PID control. Control Eng. 10(1), 37–42 (2003). (in Chinese) 9. Xie, Y., Wu, H., Lv, Z.: Adaptive control of satellite attitude during operation of liquid apogee engine. Aerosp. control 11(4), 32–38 (1993). (in Chinese) 10. Wu, H.: The research and prospect of control theory and method in engineering practice. Control Theory Appl. 31(12), 1627–1633 (2014). (in Chinese) 11. Soop, E.M.: Handbook of Geostationary Orbits. National Defense Industry Press, Beijing (1999). Wang Zhengcai, Translated. (in Chinese)

Optimal Design of Power Drive Unit Layout Based on Improved Discrete Particle Swarm Optimization Yufan Wei1 , Chunyun Dong1 , Meng Nan2 , Xiaolong Chen1(B) , and Yuheng Lu1 1 Xi’an Key Laboratory of Intelligent Instrument and Packaging Test,

Xidian University, Xi’an Shaanxi 710071, China [email protected] 2 Qingan Group Co., Ltd, 17, Xi’an Shaanxi 710071, China [email protected]

Abstract. In the aircraft cargo loading control system, the layout planning and control logic of the power drive units are crucial to the whole system, which determines the loading efficiency of the system and the safety of the loading process. At present, there is no systematic method for the research on this issue, and there are few reference materials. In this paper, taking the large cargo during aircraft loading as the object, an optimization method based on the discrete particle swarm algorithm to realize the layout of the power drive units of the large cargo during loading is proposed. A mathematical model with the goal of using the fewest power drive units was created. The inertia weight of discrete particle swarm optimization is further improved, and the global optimal solution is obtained faster by using the adaptive inertia weight. It is verified by simulation that the resulting layout meets the requirements for power drive units during loading, and the resulting power drive unit layout with the improved discrete particle swarm uses fewer drive units. Keywords: Power drive units layout · Aircraft cargo loading control system · DPSO algorithm

1 Introduction The great demand for air transport in the freight market has promoted the development of aviation equipment [1]. The aircraft cargo loading control system is a key system to realize automatic transportation and loading [2]. The system has three subsystems: control system, mechanical system and drive system. The drive system is mainly composed of hundreds of power drive units (PDUs). Through the rotation of its internal motors, the electrical energy is converted into mechanical energy to drive the movement of the Unit Load Device (ULD). The ULD whose dimensions exceed the width of the freighter, a turn is required to access the hull. China’s PDU manufacturing process can only achieve single-degree-offreedom operation and has no turning function. It needs to cooperate with horizontal and vertical PDUs to achieve ULD turning. At present, PDUs are mostly evenly distributed © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 598–605, 2023. https://doi.org/10.1007/978-981-19-9968-0_72

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on the turning trajectories of large ULDs. When loading, workers rely on experience to control the PDU to complete the loading. On the one hand, PDU resources are wasted. On the other hand, some PDUs have overheated due to continuous use for too long, which greatly increases the probability of failure. Therefore, it is of great significance to study the rational layout and the control logic of PDUs.

2 Establishment of PDU Layout Optimization Model Figure 1. The gear transmission system is used in the PDU to rotate in both directions to complete the two functions of entering and exiting the cabin. According to the placement position, PDUs can be divided into two types: horizontal and vertical.

Fig. 1. Schematic diagram of PDU structure composition

Figure 2 shows the loading route of a small ULD. The small ULD can be loaded to a designated position by driving the PDU in one direction.

Fig. 2. Small ULD Loading Route

Figure 3 shows the relative positions of the large ULD at the cargo hatch before and after turning. For a large ULD, its motion process can be modeled to obtain the number of PDUs and specific positions covered by the large ULD when it enters the cabin [3].

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Fig. 3. Large ULD’s fore and aft positions at the hatch door

Note the number of horizontal and vertical PDUs covered by ULD are respectively xt , yt (t = 1, 2, 3 . . . . . . ) and t is each time period when the ULD turns. According to the changes of ULD speed and acceleration, the number of horizontal and vertical PDUs required can be obtained, denoted as S1t , S2t (t = 1, 2, 3 . . . . . . ). PDU layout optimization is to select as few PDUs as possible. Assuming that the total number of PDUs is n, note the particle velocity and position as Vi = [v1 , v2 , v3 . . . . . . vn ](i = 1, 2, 3 · · · M )

(1)

Xi = [x1 , x2 , x3 . . . . . . xn ](i = 1, 2, 3, . . . M )

(2)

xn ∈ {0, 1}, M is the number of particles. Assuming the cost of each PDU is c, the total cost is: C=

n 

c · xn

(3)

1

Requires ULD to cover more PDUs than needed when turning mathematically described as:  xt ≥ S1t (t = 1, 2, 3 . . . . . .) (4) yt ≥ S2t (t = 1, 2, 3 . . . . . .) The PDU at the door of the cabin also needs to meet the straight-forward requirements of small ULDs. Note that the number of PDUs required by small ULDs during operation is Li (i = 1, 2, 3 . . .), i is the type of small PDU. The demand for PDUs for small ULDs is mathematically described as: zti ≥ Lit

(5)

t represents each moment in the loading process, and zti is the number of PDUs covered by each ULD at each moment. In order to prevent the PDU from failure after being used for a long time, the layout of the PDU should ensure that different PDUs can be used in two adjacent time periods

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when turning. The mathematical description is as follows: ⎧ ⎪ ⎨ Ut = {u1 , u2 , u3 . . .} Ut+1 = {u1 , u2 , u3 . . .} ⎪ ⎩ Ut ∩ Ut+1 = ∅

601

(6)

Ut , Ut+1 represent the PDU sequence numbers used in two adjacent time periods. To sum up, the optimization model of PDU layout is to make the objective function (3) take the minimum value under the condition that the constraints (4), (5), (6) are satisfied.

3 Solution of PDU Optimal Layout Model 3.1 Description of Discrete Particle Swarm Optimization DPSO (Discrete Particle Swarm Optimization) is a discrete improvement of the basic particle swarm algorithm [4]. In DPSO, each particle exists in D-dimensional space. The particles in the particle swarm are composed of binary codes of length n. At the 0 }, beginning of the algorithm, the population is initialized as S(0) = {S10 , S20 , . . . , SM 0 where M is the size of the population, and Si (i = 1, 2, . . . , M ) is the ith particle, and 0 , S 0 , . . . , S 0 ] [5]. its corresponding binary code is Si0 = [Si1 i2 in In this paper, the velocity of particles is updated in the same way as the basic PSO (Particle Swarm Optimization) algorithm [6]. The method of velocity mapping is used to update the particle position. This judgment method is to map the velocity into the interval [0,1]. Mathematically described as: ⎧ t+1 t t t t t vi,d = ωvi,d + c1 r1t (Pbest_i,d − xi,d ) + c2 r2t (Gbest_i,d − xi,d ) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪  ⎪ t+1 t+1 ⎪ 1, δi,d < sigmoid (vi,d ) ⎪ ⎪ t+1 ⎪ ⎨ xi,d = t+1 t+1 0, δi,d ≥ sigmoid (vi,d ) (7) ⎧ ⎪ ⎪ ⎪ 0.98, v > 4 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ 1 ⎪ ⎪ sigmoid (v) = , −4 ≤ v ≤ 4 ⎪ ⎪ ⎪ 1 + e−v ⎪ ⎪ ⎪ ⎪ ⎩ ⎩ 0.02, v < −4 t+1 t+1 and vi,d are the d-dimensional components of the position and Among them, xi,d velocity of particle i at the t + 1th iteration, respectively; ω is the inertia weight; c1 , c2 t+1 is the acceleration factor; r1t , r2t and δi,d are randomly generated positive real numbers t is the individual extremum found by the d-dimensional compobetween (0,1); Pbest_i,d t is the global extremum found from nent of particle i up to the t-iteration; and Gbest_i,d the d-dimensional component of particle i until the t-iteration [7].

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3.2 Algorithm Improvement Strategies PSO algorithm is easy to find the local optimal solution too early, which leads to the loss of the global optimal solution [8]. By adjusting the inertia weight to change the generation of population and adjust the fitness of particles, the problem of local optimal solution can be solved. However, if the same operation is used for the entire population to change the inertia weight, it will increase the probability of good particles being destroyed and the performance of the algorithm will decrease. This paper proposes a method to dynamically adjust the inertia weight by using population precocity and particle adaptivity [9]. The degree of premature convergence of particle swarms can be described by the following indicators:  = |zg − zap |

(8)

zg is the fitness of the global optimal particle, and zap is the fitness average of the particles whose fitness is better than the average fitness zgp of all particles.  can be used to evaluate the degree of premature convergence of the particle swarm, the smaller the , the more the particle swarm tends to converge prematurely. For particles with different fitness, the strategy for adjusting their inertia weights is: Higher than zap . At this time, the particle is relatively close to the global optimal value, which is an excellent example in the group. Its inertia weight should take a small value to avoid being far from the global optimal value.    fi − fap   (9) ω(t) = ωs − (ωs − ωmin ) ·    Lower than zap Higher than zgp . At this time, the particle is a general particle in the swarm with good global and local optimization ability, and the inertia weight is adaptively adjusted by the nonlinear decreasing strategy shown in the following formula. 2 t (10) ω(t) = ωmax − (ωamx − ωmin ) · T Lower than zgp . At this time, the particle is the poor particle in the group, and a larger inertia weight should be given. The adaptive adjustment method is as follows: ω(t) = 1.5 −

1 1 + k1 · exp(−k2 · )

(11)

Among them, k1 >1, the upper limit of ω can be controlled, the larger is k1 , the larger the upper limit of ω; k2 >0, the adjustment ability of ω can be controlled [10].

4 Application Case Analysis In order to verify the effectiveness of the DPSO algorithm and the improved strategy, the layout optimization of the uniformly distributed PDU shown in Fig. 2 is carried out.

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Modeling of PDU and large ULD, according to the traction provided by PDU, analyze the changes of ULD speed and acceleration in each time period during the turning process, and obtain the PDU covered by large ULD and the required quantity in different time periods when turning. It is assumed that the number of PDUs required by a small ULD during operation is known. The PDU layout optimization model is established for the above system. Through analysis, one layout selection of PDU can be used as a particle in the DPSO algorithm, and the dimension of the particle is the number of PDUs. When Sij0 = 1 the jth particle is selected, Sij0 = 0 it means the jth particle among the particles is not selected. Using the algorithm improvement strategy proposed in Sect. 3.2, the inertia weight of the DPSO is adaptively adjusted to. The simulation results are shown in Table 1. Control logic of PDU when turning. Table 1. Control logic of PDU when turning Frequency

Time

PDU serial number

First

0 − t1

40,45,51,52,57

t1 − t 2

1,2,53,58,64,65

t2 − t 3

4,7,19,26,40,45,51

t3 − t 4

5,27,1,2,52,53

t4 − t 5

20,4,7,19,26,48

0 − t1

40,45,46,51,52

t1 − t 2

1,2,53,58,64,65

t2 − t3

4,7,19,12,40,45,46

t3 − t 4

5,25,1,2,51,52

t4 − t 5

20,4,7,19,22,48

0 − t1

39,40,45,46,51

t1 − t 2

1,2,52,53,58,65

t2 − t3

4,7,22,25,39,40,45

t3 − t 4

5,26,1,2,46,51

t4 − t 5

26,4,7,22,25,8

Second

Third

By analyzing the optimization results, it can be seen that the fitness of the three optimization results is the same, that is, the number of PDUs is the same, but the layout of the specific performance is different, as shown in b, c, and d of Fig. 4. Considering the optimization objectives and constraints comprehensively, it is because the layout optimization problem is not an analytical solution, so the obtained layout is not unique, but the fitness of the obtained layout, that is, the number of PDUs, is the same. Compared with the uniformly distributed PDU layout, the PDU layout obtained by the improved DPSO algorithm greatly saves the cost of the PDU.

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a)

c)

No optimized PDU layout

Algorithm-optimized PDU layout 2

b Algorithm-optimized PDU layout 1

d Algorithm-optimized PDU layout 3

Fig. 4. PDU layout

Figure 5 shows the number of PDUs covered at each moment when the large ULD turns on the optimized PDU layout. Comparing with the demand for PDU at each moment when ULD turns, it can be seen that it is consistent with the demand, that is, the PDU layout obtained by improving the DPSO algorithm meets the loading requirements.

Fig. 5. PDU coverage under optimized layout

5 Conclusion The layout optimization of the PDU is the basis for the normal loading of the air cargo loading control system, and it is the global optimization of the layout configuration of the PDU at the beginning of the system design. Adopt an economical and reliable layout to drive the normal loading of cargo to improve air cargo loading efficiency. In this paper, when building the PDU and ULD models, two different types of ULDs are considered, one is a large ULD that needs to be turned at the door, and the other is a small ULD that

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does not need to be turned at the door. The cost of the PDU is taken as The objective function is optimized, considering the different requirements for PDU in the loading process, and a new adaptive adjustment method of particle inertia weight is designed by using the improved discrete particle swarm algorithm. The final simulation result verifies the applicability of this method, and has certain reference value for the layout planning of air cargo loading PDU.

References 1. Bolanakis, G., Machairas, K., Koutsoukis, K.: Automating loading and locking of new generation air-cargo containers. In: MATEC Web of Conferences, 304, 04019 (2019) 2. Tao, Z.: An experimental and modeling study of heat radiation characteristics of inclined ceiling jet in an airplane cargo compartment. Fire Mater. 43(7), 794–801 (2019) 3. Hewitt, M.: Enhanced dynamic discretization discovery for the continuous time load plan design problem. Transp. Sci. 53(6), 1731–1750 (2019) 4. Zhan, Z.-H., Zhang, J.: Adaptive particle swarm optimization. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 227– 234. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87527-7_21 5. Zebin, W.: Bilateral matching optimization of industrial chain financial products based on 01 knapsack strategy and improved discrete particle swarm optimization algorithm. Comput. Integr. Manuf. Syst. 25(12), 3279–3288 (2019) 6. Zhao, D.: A binary discrete particle swarm optimization satellite selection algorithm with a queen informant for Multi-GNSS continuous positioning. Adv. Space Res. 68(9), 3521–3530 (2021) 7. Qiu, C.: Feature selection using a set based discrete particle swarm optimization and a novel feature subset evaluation criterion. Intell. Data Anal. 23(1), 5–21 (2019) 8. Parsopoulos, K.E.: On the computation of all global minimizers through particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 211–224 (2004) 9. Hsieh, S-T.: Efficient population utilization strategy for particle swarm optimizer. IEEE Trans. Syst. Man Cybern. B Cybern. 39(2), 444–456 (2009) 10. Ma, L.: Optimal sensor placement based on improved discrete PSO algorithm. Acta Elesctronica Sinica 43(12), 2408–2413 (2015)

Preliminary Discussion on the Application of Digital Thread in the Model-Based Spacecraft Development Lingfeng Zhao1(B) , Ziwei Wu1 , Lingyun Cheng2 , and Guoxiong Zhan2 1 Beijing Institute of Spacecraft System Engineering, Beijing, China

[email protected] 2 Shanghai ATOZ Information Technology Ltd., Shanghai, China

Abstract. In recent years, the number of spacecraft development tasks are increasing significantly. Meanwhile, the task performance requirements are constantly improving, the complexities of the spacecraft systems are increasing, and the traditional document-based development mode is gradually transforming to the model-based development mode. However, it brings problems such as numerous data types, complex association relationships, and difficulties in sharing at each stage of the development process. For the above, we propose to solve the problem of correlation and tracing of complex relationships of lifecycle data by adopting digital thread technology. Specifically, we identify the main structure and data elements of the digital thread for the spacecraft development process by analyzing the key business links and their corresponding business data in the model-based spacecraft development process. Moreover, we focus on building a digital thread that runs through various business domains such as requirements, functions, logical architecture, physical architecture, interface data, and product data, and initially form a global unique truth source and comprehensive view of data for the whole lifecycle of spacecraft development, which will strongly support the realization of the model-based spacecraft development mode. Keywords: Digital Thread · Model-Based Systems Engineering · Spacecraft · Lifecycle Management

1 Introduction With the rapid development of science and technology, spacecraft development tasks are increasing significantly, the performance requirements are constantly improving, and the complexities of the spacecraft systems are increasing, which brings a severe challenge to the development of spacecraft. Thus, the traditional document-based systems engineering approach is unable to effectively meet the spacecraft development requirements, and model-based systems engineering (MBSE) becomes an important development approach for subsequent spacecraft [1–4]. However, in the forward development of spacecraft based on MBSE, a large amount of data is generated in requirements analysis, system design, detailed design, and other stages. And there are complicated correlations © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 606–613, 2023. https://doi.org/10.1007/978-981-19-9968-0_73

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between the data at each stage [2]. How to effectively control the data in MBSE and provide effective support for the subsequent development process becomes a critical challenge in spacecraft development Therefore, it is necessary to apply digital thread technology to decouple, reconstruct and reuse the data in the spacecraft MBSE development process with business as the core, to build a full chain of correlation relationships between data in each stage, and to interface with the data in the stages, such as downstream process, manufacturing, and service. Finally, the digital thread throughout the spacecraft lifecycle is constructed to form a unique and authoritative data source, to effectively support the data control during the development of spacecraft based on MBSE, and improve the efficiency and quality of spacecraft development.

2 Analysis of the Model-Based Spacecraft Development Mode Compared with the traditional document-based development mode, the model-based systems engineering mode focuses on using formal models to achieve the whole process, from conceptual design, project design, test verification to engineering implementation. And the core of this mode is to establish its data-centered systems engineering management system. It has significant advantages such as non-duality of knowledge representation, high efficiency of communication and exchange, integration of system design, high capacity of knowledge acquisition and reusability, and multi-perspective analysis [3, 4]. Therefore, this mode can effectively help to complete collaborative design, integrated simulation, and digitalization of the whole process in spacecraft development, and it is deeply researched and applied in the field of spacecraft development in recent years. In the model-based spacecraft development process, a complete model system including requirement model, function model, architecture model, interface model, and product model, must be built in the forward design stage. In the requirements analysis stage, the analysis of system requirements, usage patterns, and constraints can be achieved by building a spacecraft requirements model. In the project design stage, an accurate description of the spacecraft’s structure, functions, and function assignments is achieved by building a functional and architectural model of the system, and full-system functional simulation verification can be carried out [3]. In the stage of detailed design, the interface model of equipment is built to standardize and clarify the interface information of equipment mechanics, thermal control, telemetry, remote control, and others, which is used as the basis of equipment design. Meanwhile, the spacecraft product model is built to realize the design and analysis of the whole satellite mechanics, electronics, information, and other domains, which can be used as the basis of downstream equipment production [2].

3 The Digital Thread and Its Construction Method 3.1 Overview of the Digital Thread To support the development and management of complex systems, the concepts of digital thread and digital twins were first proposed in the analysis report ‘Global Science and

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Technology Vision Report’ released by the United States in June 2013. The digital thread is a communication framework that allows for connecting data streams and physical asset data throughout the lifecycle, and building systems integration data views (the sum of data for digital links) to form a single continuous definition of all value-added decisions for a complex system [5]. Digital thread seamlessly accelerates the interaction of authoritative data, information, and knowledge in an enterprise data-information-knowledge system, which can be transformed into information actionable capabilities by accessing and integrating data. By decoupling and reconfiguring data scattered at various stages of the product development process, the data link through the entire product lifecycle is opened [6]. Digital thread can horizontally cover the whole process of requirement, design, manufacturing, test, operation, and maintenance, and can vertically run through multiple professional domains of architecture, mechanics, electronics, and others, which forms a complete global unique source of truth for a complex equipment system, a comprehensive view of data, and a digital tapestry of the entire value chain. It transcends the traditional isolated function perspective to manage and trace the entire lifecycle process of a complex equipment system [7]. 3.2 The Construction Method of Spacecraft Digital Thread In the construction of spacecraft digital thread, two aspects should be considered: main structure and data elements. The main structure refers to the collection and order of node objects related to the development process, business domain, and project management. Data elements refer to the virtual and real data corresponding to nodes, such as models and parameters. In the main structure, the spacecraft digital thread should be related to the entire process, including requirement analysis, system design, detailed design, product manufacturing, assembly and integration, system test, and in-orbit operation & maintenance, as well as all professional domains such as architecture, mechanics, energy, information, and thermal control. At the same time, it should be related to management activities such as project planning, product assurance, resource planning, supplier management, and others. Thus, the main structure of the spacecraft digital thread is not a conventional concept of a line, like Requirement-Function-Logical-Physical-Plan-Process-Resource. If the data in various fields generated by the spacecraft in the development process based on systems engineering is separately regarded as structured “data tree”, the digital thread is to build a network through the spacecraft development “data forest”, as shown in Fig. 1. In terms of data elements, it contains the models generated by the nodes corresponding to the activities, the parameters involved, the actual measurement data, and others. For example, requirement analysis nodes include requirement items, graphical requirement architecture, operational scenarios model, and others; the system design nodes include the model of system logic composition, sub-system interface, sub-system dynamic behavior, and others; system validation nodes include mechanics, energy, information and other professional function & performance models; professional simulation nodes include space environment, electromagnetic compatibility, mechanical-thermal analysis, reliability and safety, and other models.

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Fig. 1. Main architecture of spacecraft digital thread

By using the digital thread as a comprehensive communication framework to organize and manage the main architecture and data elements, a system integrated data view of spacecraft development can be established to correlate, manage, and trace all linked data.

4 Realization and Application of Digital Thread 4.1 Realization of Digital Thread in the PLM System From the perspective of information system implementation, the digital thread needs to connect with product lifecycle management (PLM), enterprise resource planning (ERP), and customer relationship management (CRM). Sometimes, when it connects different systems representing different perspectives within a digital enterprise, there is some overlap [8]. As an important part of the digital thread, PLM systems provide an enterprise-level comprehensive product-related view and reflect product information and processes. PLM systems integrate information about the entire process of a product from conceptual design to manufacturing, deployment and maintenance, and end-of-life, as shown in Fig. 2. This embodies the process of product definition, product variation, and traceability while forming a whole of mechanical, electronic, and software components to achieve the traceability of data, processes, decisions, and results throughout the product lifecycle [8, 9]. Traditional PLM systems can only manage domain specific models, and the information between models needs to be developed independently. With the popularization of MBSE, the information integration between various models has become a key problem to be solved. Therefore, using digital thread technology has become a very effective way of data integration. Therefore, it is necessary to build the digital thread with the PLM system as the core for the spacecraft development business. Better data flow from conceptual design to detailed design and feedback from later stages of product development are needed [10].

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Fig. 2. Engineering business related to the PLM system [8]

During the construction of the digital thread, the system functions can be developed based on the business data of the PLM system, according to the three-layer architecture of the data layer, relationship layer, and view layer, as shown in Fig. 3.

Fig. 3. The three-layer architecture of digital thread realization

The data layer is used to connect the data in various processes and specialties, such as system architecture design data, system simulation data, 3D collaborative design data, professional simulation data, process design data, test data, in-orbit data, and others. The relationship layer is based on the underlying data and reconstructs its data relationship according to the business logic of spacecraft development and management activities, such as the relationship between requirements and functions, and the relationship between system architecture and interfaces. The view layer further analyzes the data set of the relational layer according to the needs of specific business scenarios.

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For example, according to the change of a certain system requirement, a global impact analysis is performed to accurately determine the affected range of sub-systems. 4.2 The Construction Practice of Spacecraft Digital Thread In the forward design phase of the model-based spacecraft development process, the various business models in the PLM system are used to correlate the data related to the requirement model, functional model, architecture model, interface model, and product model. Moreover, we carry out data association, relationship management, and process traceability for business activities such as system architecture design, system simulation verification, interface design, 3D detailed design, and multi-professional co-simulation, and use a comprehensive view to display the above processes to complete the construction of the digital thread, as shown in Fig. 4.

Fig. 4. Realization of digital thread in spacecraft design

Among them, the traceability relationship constructed by the system designer when designing the system architecture includes the satisfaction relationship between functions and requirements, and the distribution relationship between functions and logical architecture. After integrating the system architecture, the interface information such as mechanical characteristics, electronic characteristics, thermal characteristics, and others is transferred from the unit object to the interface system. Then the correlation between the system architecture and the stand-alone mechanical interface object is established. The unit solidifies the interface information of its mechanical properties in a 3D model, and the detailed design process builds the product engineering bill of materials (EBOM) based on this. Then the traceability relationship between the interface and the EBOM is built. Based on the above business logic, the digital thread can be built. After that, all kinds of model development personnel, including requirement engineers, architecture design engineers, and 3D design engineers, can trace the data in other business links in real-time

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upstream or downstream based on the digital thread. In a word, the construction of a digital thread can promote integrated management and analysis of development data, give play to the real value of data, and greatly improve the efficiency of collaborative research and development. The specific benefits are as follows: (1) Traceability Analysis: The digital thread constructs the traceability relationship between the data entities in each development stage, which is convenient to locate and search the data links that run through multiple business links, as shown in Fig. 5. (2) Impact Analysis: When the data in the upstream link is changed, the affected downstream business data can be quickly analyzed through the digital thread. For example, when the requirements are updated, the affected functional structure, logical structure, detailed design, and other information can be quickly identified. (3) Professional Analysis: Based on the digital thread, the associated data can be professionally analyzed, including simulation verification analysis, mathematical analysis, and others. (4) Statistical Analysis: Based on the digital thread, statistical analysis of the correlation between different types of data can be performed, such as the coverage of requirements, the realization of functions, and others.

Fig. 5. Relationship between spacecraft digital thread and PLM data

5 Conclusion In this paper, we propose to use digital thread to realize the association and tracing of complex relationships of lifecycle data, because of the problems of numerous data types and complex relationships in sharing in each stage of the model-based spacecraft development. Specifically, we clarify the digital thread architecture and data elements for the spacecraft development process by analyzing the key business links and their corresponding business data. In the process of building the digital thread, we propose to develop the system functions based on PLM system business data according to the threelayer architecture of the data layer, relationship layer, and view layer. Meanwhile, in the

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forward design phase, we correlate the data related to requirement model, functional model, architecture model, interface model, and product model in the PLM system. And we carry out data association, relationship management, and process traceability for business activities such as system architecture design, system simulation verification, interface design, 3D detailed design, and multi-professional co-simulation. In this paper, by presenting the above processes in an integrated view, a digital thread is constructed to support the development of model-based spacecraft. The global unique truth source of the complete lifecycle of spacecraft development is preliminarily formed to ensure the digital continuity of spacecraft development. It can be predicted that in the future, digital thread technology will serve as a core technology to coordinate the information flow among all ecosystem participants and form a complete digital development system in an open collaborative spacecraft development environment.

References 1. Fengyu, H., Yiming, L., Haitao, F.: Research and practice of model-based systems engineering in spacecraft development. Spacecraft Eng. 23(3), 119–125 (2014) 2. Bainan, Z., Faren, Q., Tao, X., et al.: Research and practice of manned spacecraft development method based on the model. Acta Aeronautica Et Astronautica Sinica 41(7), 72–80 (2020) 3. Hongchen, J., Yong, L., Hongyu, Z., et al.: Exploration of MBSE-based spacecraft system modeling analysis and design development method. Syst. Eng. Elec. 43(9), 2516–2525 (2021) 4. Zhiang, L., Xia, L., Yinxuan, M., et al.: Application practice of model-based system engineering method in satellite integrative system design. Spacecraft Eng. 27(3), 7–16 (2018) 5. AIA. Life Cycle Benefits of Collaborative MBSE use for Early Requirement Development (2016) 6. Jianguang, G., Dalin, L.: Research and application of product digital space management framework supporting digital clues. Mod. Manufact. Eng. (2) (2022) 7. Zhigang, F.: Digital Twinning of Complex Equipment Systems (Enabling Model-Based Forward Development and Collaborative innovation). China Machine Press (2021) 8. Kuhn, W.: Handbook of Digital Enterprise Systems Digital Twins, Simulation and AI. University of Wuppertal, Germany (2019) 9. Delin, L., Wenhao, B., An, Z., et al.: MBSE-based process management in the development of civil aircraft. Syst. Eng. Elec. 43(8), 2209–2220 (2021) 10. Menshenin, Y., Moreno, C., Brovar, Y., et al.: Integration of MBSE and PLM: complexity and uncertainty. Int. J. Prod. Lifecycle Manag. 13(1), 66–88 (2021)

Study on the Construction Method of Spacecraft System Model Based on Meta-model Jiangdong Ruan1 , C. G. Li2(B) , Yuxuan Li3 , and Xinwu Chen1 1 China Academy of Space Technology (CAST), Beijing, China 2 Hangzhou Dianzi University (HDU), Hangzhou, China

[email protected] 3 Hangzhou Hope System Technology Co. Ltd, Hangzhou, China

Abstract. Model-based systems engineering approach is a key technology in the transformation of spacecraft digital design. Based on the meta-model theory, this paper proposes a new method of designing spacecraft MBSE system models, which introduces the MOF to ensure the logicality of the domain meta-model through the modeling of the domain ontology concept, explores how to customize the domain meta-model with modeling tools, and realizes the construction of the system model based on the spacecraft domain meta-model. At last, a spacecraft case study is developed to verify the proposed method. It is expected that this paper will provide the necessary reference for other complex equipment to build system models with domain meta-model. Keywords: Spacecraft · Model-Based Systems Engineering · Domain Meta-model · Ontology Modeling · System Model

1 Introduction Model-based systems engineering (MBSE) is an advanced method for processing and managing the complexity of system that conforms to the principle of systems engineering. At present, in the field of spacecraft design, the application of MBSE is mostly focused on the design and simulation of model. The practice of MOF (Meta object facility, a standard of OMG) and meta-model is still relatively rare. In specific designing, especially in the specific field of MBSE, the extensibility of the system modeling language is required. And the framework of domain specific modeling should be suitable with the present working circumstances and scenario in spacecraft design. In the practice of meta-model, researches had been done into several areas. Zhan Guoxiong [1] discussed the use of data meta-model to realize the completeness, consistency and correlation of equipment system data in each stage of MBSE under the weapon equipment system, and used it as a common and consistent data dictionary and system modeling for all stakeholders. Wang Xichao [2] and others proposed a new open toplevel modeling method for complex systems. Based on the meta-modeling framework of MOF, the basic morphological extension of SysML (Systems modeling language) and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 614–621, 2023. https://doi.org/10.1007/978-981-19-9968-0_74

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the morphological extension of Simulink are applied to the field of UAV system architecture. Zhou Jun [3] et al. analyzed various enterprise modeling methods, applying concepts and methods such as meta-model and model-driven, a model-driven enterprise modeling method is proposed. Based on the meta-meta model, the identifiers in UML (Unified modeling language) are used for enterprise modeling. Ren Luying [4] et al. taking the complex products widely existing in the aerospace manufacturing field as the object, the meta-model, modeling method and multi-disciplinary modeling process are studied and analyzed, and the multi-disciplinary optimization design and the meta-model modeling method are combined to establish a virtual simulation of complex products. Prototype meta-model, and the conversion relationship between meta-model and discipline meta-model was constructed, and a multi-disciplinary modeling optimization design scheme for virtual prototyping was formed. Through the in-depth discussion of the MOF framework [5], the construction of the spacecraft system model based on the domain meta-model will be more in line with the new design requirements of today’s continuous implementation, adapted to the expression of complex mission objectives in many situations, as well as the existing system. The synergy ability of each supporting product in combination with each other provides an idea for finding the best practice in the field of MBSE spacecraft design.

2 The Construction Method of Spacecraft Domain Meta-model Meta-model represents the abstract concept of model. When the system model needs to be expanded and co-simulated, the meta-model plays a role of bridge to communicate models in different phases and with different goals. The creation of the domain meta-model is inseparable from the abstract design combining domain concepts and the operational mechanism of the meta-model. At present, the extension mechanism of meta-model based on SysML is often used to complete the creation of meta-model, and the sorting and abstract extraction of domain knowledge in the field requires the use of ontology and semantic concept theory. 2.1 Meta Object Facility The meta object mechanism of OMG (Object management group) is a specification that describes the modeling concepts of a model and the relationships between modeling concepts under MDA (Model driven architecture). MOF specifies a four-layer model architecture, as shown in Fig. 1. In the spacecraft designing field, these four-layer models are analyzed as follows: M0 layer is the instance model, corresponding to the model design scheme; M1 layer is the model, which is the system model established by SysML; M2 layer is the domain meta-model obtained by extension of SysML in specific domain; M3 layer is the meta meta-model layer, which is the modeling language specification of the M2 layer meta-model, and it is used to communicate and understand the elements and rules for building the metamodel.

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Fig. 1. MOF in spacecraft designing field

2.2 Spacecraft Ontology Concept Modeling The foundation for understanding the domain meta-model is the precise expression of domain knowledge, for which the theory of semantics needs to be introduced. Meaning triangle theory is a general method for concept description and interpretation [6]. This method believes that meaning includes three elements: referents, symbols described by referents, and concepts represented by referents. If there is a consistent interpretation and understanding of an entity existing in the real world, it is necessary to have a unified understanding of both the concept of the object and the syntax of symbols in a specified context. The expression of semantics and the use of symbolization must be established under a unified standard in order to effectively transmit complete semantic information [7]. Ontology is a model that describes knowledge in a specific domain, which is the embodiment of semantic understanding [8]. On the one hand, it is based on description logic and has certain formal characteristics, and it uses concepts and relationships in the field to describe facts, which is close to human thinking and easy to be understood; on the other hand, it improves the abstract level of fact description, which regulates the representation of multiple facts, is a metatheory. Integrating ontology and meta-model together to form the theoretical system of ontology meta-modeling will make the metamodel have efficient modeling and expression capabilities, and promote the semantic combination of simulation modeling resources [9, 10]. Using semantic description, MOF and ontology logic verification method, as shown in Fig. 2, the domain meta-model creating process is divided into the following steps: (1) Collect Domain Concepts; (2) Create a meta-model system; (3) Create systems model with meta-model; (4) Iterate and practice.

Fig. 2. Meaning triangle model with MOF

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2.3 The Customization of Domain Metal-Model on Modeling Tools According to the extension of the MOF in the SysML meta-model, the creation of the domain meta-model will be extended to the spacecraft domain by establishing generalization and expansion. The basic technical implementation is to create a domain “stereotype” on the “profile diagram” view, build generalizations, extend relationships to the original SysML meta-model, and then add specialized properties to the new stereotypes. New stereotypes can also be quickly selected through functions such as customization toolbars, which play a role in aided design.

3 The Realization of Spacecraft System Model Based on Domain Meta-model 3.1 Ontology Modeling Based on the existing spacecraft design practice, the following levels of processes are summarized: (1) requirements analysis, decomposes the requirements into different types by designers in different system layers, including functional requirements, constraint requirements, and interface requirements; (2) functional analysis, is used to express the initial system functions and decomposed functions. Including system functions, subsystem functions, and unit functions; (3) architecture analysis, combines the component modules and sub-system divisions generated by the decomposition of different layers, which can be divided into system, sub-system, and unit. Each layer of the meta-model maintains independency, but the meta-models between the same layers have a certain logical relationship. For example, in the architecture analysis layer, the system is composed of subsystems, and the subsystems are composed of equipment; the same element of model cannot be both a subsystem and an equipment. This type of constraint description that conforms to semantic concepts can be verified by ontology logic through the ontology editing tool Protégé, as shown in Fig. 3(a).

(a) Ontology relationships

(b) Results of inference in Protégé

Fig. 3. Ontology modeling

Run the verification and inference of the constraints, and get the inference results of the verification constraints as shown in Fig. 3(b). The results show that the above meta-model and its corresponding semantics are in line with the original actual concept expression, and the domain meta-model meets the actual operation requirements in terms of semantics.

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3.2 View Template Modeling After determining the content of the domain meta-model, referring to the extension of MOF, and the basic meta-model of SysML, the meta-models of the levels are created in CSM. The meta-model of the design content, such as the architecture analysis metamodel shown in Fig. 4, inherits the original attributes by establishing generalization relationships on the basis of the original meta class model, ensuring that the method is satisfied with SysML and systems process and syntax; then add domain-specific information and features, such as domain names and attributes, to make it more relevant to the spacecraft design field than SysML. The attribute added to the stereotype can be any feature of the model element, mainly to serve the designer to quickly apply the corresponding attribute content to the model element, as well as to standardize the characteristics of the model element.

Fig. 4. Domain meta-model in architecture design

In the application of metamodel, the extended meta-model can be directly displayed on the diagram toolbar by binding the stereotype, so that design modelers can directly display meta-models or elements on the canvas, such as drag and drop for quick modeling. As shown on the left side in Fig. 5, common stereotypes, including domain metamodels, can be selected in the diagram toolbar.

Fig. 5. Customize diagram toolbar with architecture model

After completing the creation of the domain meta-model, the practice from the M2 meta-model to the M1 model could be started, that means, the created domain metamodel is used as the model element to construct the spacecraft design domain system model. The spacecraft architecture model as shown in Fig. 6 is a model established by directly using the domain meta-model and the constituent associated elements in SysML. Each level

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module in the model inherits the attributes added from the stereotype, and in the structure, the unique graphics for each meta-model can be set in the model, which furtherly reduces the difficulty of understanding. In the follow-up, the model and meta-model can be verified and iterated along with the actual scenario, enhancing the rationality and practicability of the meta-model design.

4 Case Study Taking the processes in real case, the four-level meta-model is created on a modeling tool, as shown in Fig. 6. For each domain meta-model, add attributes that conform to operational characteristics. For instance, such as “manufacturer” and “development stage” applied by the stereotype of the subsystem. This attribute information extends the usefulness of the model in specific domain.

Fig. 6. Domain meta-model in spacecraft designing

Using the customization, the created meta-model can be bound to the diagrams, so that designers can directly drag and drop elements for modeling after creating a diagram, which simplifies the operation. The following diagram templates are extracted in processes of spacecraft designing: stakeholder analysis, mission use case analysis, system context analysis, requirements analysis, spacecraft architecture analysis, interface analysis, retrospective analysis, system function analysis, etc. Compared with the SysML model, the model built with domain meta-model has the following advantages: (1) The system model based on the domain meta-model can accurately reflect the real content in the expression of the specific domain, and have more practical advantages than the abstract expression of SysML; (2) The establishment of the domain meta-model can include specific information at the relevant operation levels, and have the ability to expand within the mission scope, enriching the capability coverage of the system model in the specific field;

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(3) The application of domain meta-model will improve the unity of system model, be upward compatible with SysML model, and more standardized and controllable than general SysML model; (4) The system modeling carried out in combination with the domain meta-model will help to realize the modular utilization of system elements, functions and structures, and encapsulate the combination of models with specific design patterns or design purposes, so as to achieve the effect of rapid modeling; (5) The diversity and versatility of the SysML language affects its application in the field of spacecraft system modeling to a certain extent. The extension mechanism of the domain meta-model improves the fit of its model content and reduces the difficulty of use. All in all, system modeling based on metal model can bring up the advantage of high-degree reuse, efficiency and good quality.

5 Conclusion Based on MOF extension mechanism, starting from the design elements of the spacecraft design domain, the meta-model of the relevant domain is extracted and designed, and the semantic logic verification of the meta-model is completed through the ontology editing tool. According to the domain meta-model, the spacecraft system model is explored. Through this work, we can confirm that the extended domain meta-model based on MOF can meet the design requirements of the spacecraft mission expression, simplify the modeling process of SysML model, reduce the possibility of human error in the early stage of designing, and improve the integrity of the system model. Domain concept interpretation and ontology constraint rules based on semantics can verify the logic of the domain meta-model, unify the interpretation of related concepts in real world, and consolidate the rationality of the meta-model in application.

References 1. Guoxiong, Z.: Research on Meta-Modeling and Analysis Method of Weapon Equipment System Based on MBSE. National University of Defense Technology, Hunan (2015) 2. Xichao, W., Yunfeng, C., Meng, D., et al.: Open top-level modeling of complex systems driven by meta-object mechanism. Univ. Elec. Sci. Technol. China 41(4), 9 (2012) 3. Jun, Z., Lin, X., Zheng, L.: Metamodel-driven enterprise modeling. Comput. Eng. Appl. 041(027), 215–217 (2005) 4. Luying, R., Qingguo, W., Qian, M., Haifeng, Z., Weiwei, X.: Research on the modeling method of complex product virtual prototype based on metamodel. Syst. Eng. Elec. Technol.1001506X (2022) 5. OMG Meta Object Facility (MOF) Core Specification_v2.5.1 6. Ogden, C.K., Richards, I.A.: Meaning of Meaning. Harcourt, Brace & Company, New York (1927) 7. Keqin, H., Yangfan, H.: Ontology Metamodeling Theory and Method and Its Application. Science Press, Beijing (2008)

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8. Djuric, D., Gasevic, D., Devedzic, V.: Ontology modeling and MDA. J. Object Technol. 4(1), 109–128 (2005) 9. Chen, K., Sztipanovits, J., Neema, S.: Toward a semantic anchoring infrastructure for domainspecific modeling languages. In: Fifth ACM International Conference on Embedded Software, pp. 35–43 (2005) 10. Wencai, X., Xueshan, L., Aimin, L.: Meta-model based modeling of military information system architecture. J. National Univ. Defense Technol. 1001–2486 (2012) 01-0082-06

The Design and Implementation of Magnetic Control of Ultra Quiet Control System in Gravity Satellite Bin Guan1(B)

, Yiwu Liu1,2

, Qirui Liu1,2

, Yan Li1

, and Jiakun Fan1

1 Beijing Institute of Control Engineering, Beijing 100190, China

[email protected] 2 Science and Technology on Space Intelligent Control Laboratory, Beijing 100190, China

Abstract. In order to obtain gravity field data with high-precision, ultra quiet control should be applied in the attitude control system to reduce the influence of micro vibration caused by attitude control, since the measurement accuracy of the high-precision acceleration of the key load of the gravity measurement satellite has reached the level of 10−10 m/s2 . A set of ultra quiet control system of magnetic control with high-precision is proposed in the paper, which makes the magnetic control accuracy reach the high-precision level of 0.01 mNm and establishes an “extremely quiet” working environment for high-precision acceleration load by improving the geomagnetic field acquisition accuracy and high-precision magnetic torquer control. According to the data analysis of orbit, the result shows that the micro vibration environment of the magnetic control system has reached the ug level. Keywords: Gravity measurement · Ultra quiet · Magnetic control · Design and implementation

1 Introduction In recent years, foreign gravity measurement satellites have been successfully applied in orbit, which has aroused extensive research on the cutting-edge technology of gravity measurement both at home and abroad [1, 2]. Precision accelerometer is the core payload of gravity measurement satellite, which has high requirements for the ultra quiet level of satellite platform. Therefore, to design a set of ultra quiet control platform is one of the important tasks of gravity measurement satellite for the sake of working environment requirements with precision accelerometer. The control technologies now applied on LEO (low earth orbit) small satellites with high control accuracy mainly include momentum wheel control technology and control moment gyro control technology. Because the momentum wheel and control moment gyro will bring high vibration acceleration and high-frequency noise to the body of satellite in the process of attitude control, which will seriously affect the measurement accuracy of payloads such as precision accelerometer of gravity measurement satellite. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 622–629, 2023. https://doi.org/10.1007/978-981-19-9968-0_75

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Therefore, the satellite platform for gravity measurement needs magnetic torquer as the main actuator rather than momentum wheel and control moment gyro [3–5]. In order to ensure that the satellite can reach the level below the angular acceleration urad /s2 , one of the important aspects is that the magnetic field measurement accuracy and the output accuracy of the magnetic torquer need a high level, so as to obtain accurate control torque.

2 Principle of Designing Magnetic Control of Ultra Quiet Control System with High Precision The magnetic control torque acting on the satellite can be expressed as: T = M × B. Where T represents the torque generated by the magnetic torquer, M represents the magnetic moment output by the magnetic torquer, and B represents the local geomagnetic field strength of the satellite [6]. The onboard computer uses the star sensor to determine the attitude and calculate the required control torque Tc. According to formula M = B × Tc/|B|2 calculates the required magnetic moment and outputs the control signal through the magnetic torquer driving circuit to generate the required magnetic moment. It is obvious from the above that it is required to improve the ability to obtain the precise magnetic field strength of the local geomagnetic field where the satellite is located, and the magnetic moment generated by the magnetic torquer should be accurate enough so as to obtain the control effect of high-precision magnetic torquer.

3 Measurement Technology of Magnetometer with High Precision The acquisition accuracy of magnetometer on orbit is affected by the factors such as remanence and temperature. In order to improve the measurement accuracy of the magnetometer under the whole satellite layout, it needs to be systematically calibrated to eliminate the errors caused by design, installation and other links in the ground stage. 3.1 Temperature Compensation Technology of Magnetometer The measurement principle of magnetometer follows Faraday’s law of electromagnetic induction, which detects the DC magnetic field through the saturation excitation of alternating magnetic field by making use of its saturation characteristics of materials with high permeability. The temperature drift characteristics of magnetometer are mainly affected in high permeability materials, enamelled wire wound coil and so on. In order to reduce the influence of ambient temperature on the measurement accuracy of magnetometer, it is necessary to operate temperature calibration test and real-time compensation on orbit. A special non magnetized high and low temperature test chamber is used to calibrate the temperature of the magnetometer. The relationship between the measured magnetic field value of the magnetometer and the analog output of the magnetometer can be expressed as: B = K(T )x + b, where B is the magnetic field strength applied to the magnetometer (unit nT ), x is the analog output value of the magnetometer (unit V ), b is

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the constant coefficient, and K(T ) is the conversion coefficient related to temperature. After calibration, the K(T ) coefficient can be expressed as the following formula (1): K(T ) = (109 × K0)/(109 + Kt(T − T 0) × K0)

(1)

T is the head temperature of magnetometer (unit ◦ C), K0, T 0, Kt is a constant coefficient in this formula. The onboard control computer collects the temperature data of the magnetometer head in real time through the CAN bus, and compensates the temperature of the magnetic field data according to the above formula. 3.2 Whole Satellite Compensation Technology of Magnetometer It is necessary to carry out the whole satellite magnetic calibration test to eliminate the influence of the whole satellite installation, the whole satellite remanence and the orthogonality of the three measurement axes of the magnetometer on the measurement accuracy. In the whole satellite state, the magnetometer is calibrated under the zero magnetic environment. The magnetometer measurement can be expressed by the following formula (2): BM = BS + BM 0 + KMN CNS CSc Cbb0 BEM + B˜ n

(2)

Here BM is the actual measured value of the magnetometer, BS is the magnetic field of the satellite equipment, BEM is the known standard magnetic field, B˜ n is the measurement noise of the magnetometer, CSc is the installation error matrix of the magnetometer, Cbb0 is the error matrix of the satellite coordinate system relative to the actual magnetic field coordinate system, CNS is the 3-axis non orthogonality matrix of the magnetometer, BM 0 and KMN the diagonal matrix of the constant term and proportional term respectively representing the magnetic induction coefficient of the magnetometer. Where CNS and KMN are generally not orthogonal matrices. The comprehensive coefficient calibration comprehensively considers the influence of factors such as the magnetic induction coefficient of the on-board magnetometer, installation error and the non orthogonality of the three axes of the magnetometer, and adopts the following formula (3) for the comprehensive coefficient calibration. ⎡

⎤

⎡

X0 KXX KXY XM ⎣ YM ⎦ = ⎣ Y0 KYX KYY ZM Z0 KZX KZY

⎡

⎤ ⎡ ⎤ 1 X˜ n KXZ ⎢ ⎥ X n⎥ ⎢ ⎣ ⎦ KYZ ⎣ ⎦ + Y˜ n ⎦ Yn KZZ Z˜ n Zn ⎤

(3)

T T   is the component representation of BM , X˜ n Y˜ n Z˜ n is the Here XM YM ZM  T component representation of B˜ n , Xn Yn Zn is the component representation of Cbb0 BEM , The data of Cbb0 is obtained by precise measurement in the laboratory. ⎡ ⎤ X0 KXX KXY KXZ ⎣ Y0 KYX KYY KYZ ⎦ is the parameter matrix to be calibrated. Z0 KZX KZY KZZ

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In this way, the measurement accuracy of the magnetometer is controlled within 100 nT in the ground test. The curve of residual error after calibration (error between calibration correction value and nominal value) is shown in Fig. 1. x-axis calibration residuals

y-axis calibration residuals

magnetic field intensity/(nT)

z-axis calibration residuals

200.00 100.00 0.00 -100.00 -200.00 3500

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time/s Fig. 1. Residual curve of magnetometer after calibration.

4 Magnetic Torquer Control Technology with High Precision Gravity satellites keep up relying on magnetic torquer for attitude control. Generally, at least three magnetic torquer are configured to complete the attitude control requirements. The magnetic torquer is composed of a body and a driving circuit. The body is wound with multi-layer coils on a round and straight soft magnetic core rod to generate magnetic moment through exciting current. The driving circuit is used to receive the control signal, output a specific excitation current for the magnetic torquer body according to the state of the control signal, and output a telemetry signal reflecting the state of the excitation current. In order to improve the control accuracy of the magnetic torquer, there are two important aspects: one is to control the magnetic induction of the magnetic torquer itself to reduce the influence of the magnetic torquer on each other. Second, it is necessary to improve the output accuracy of the excitation current of the control circuit. 4.1 Induced Magnetic Moment Control of Magnetic Torquer Body The difference in the design of gravity satellite compared with traditional satellite is that the selected products are required to have the characteristics of low power consumption, light weight, low remanence and induced magnetic moment. Compared with iron nickel alloy soft magnetic material, iron cobalt alloy soft magnetic material is selected as the soft magnetic material in the body of the magnetic torquer, which is mainly characterized by small remanence and induced magnetic moment, small volume and light weight [7]. Aiming at the low induced magnetic moment performance, the induced magnetic moment experiments between three iron cobalt alloy magnetic torquer were carried out according to the orthogonal layout of three magnetic torquer. The three magnetic torquer are recorded as magnetic torquer x, magnetic torquer y and magnetic torquer z

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magnetic moment/(Am2)

respectively. Test according to the conditions of setting the input to 5 Am2 , 10 Am2 , 20 Am2 and 30 Am2 respectively, and measure the size and linearity of the actual magnetic moment output. The experimental results are shown in Fig. 2, which shows that the magnetic torquer has less induced magnetic moment. theoretical value

magnetic torquer x measuring value

magnetic torquer y measuring value

magnetic torquer z measuring value

35 30 25 20 15 10 5 0 0

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Fig. 2. Output magnetic moment curve of magnetic torquer under whole satellite layout.

4.2 Driving Circuit Technology of Magnetic Torquer with High Precision In order to meet the control requirements of ultra quiet, the magnetic torquer circuit adopts the control mode of continuous analog linear form (the magnetic torquer control signal is –5 v ~ + 5 v continuously adjustable analog voltage signal, including both amplitude and direction). Compared with the switch control mode, it can avoid the impact of discontinuous magnetic torque output on the high-precision accelerometer to the greatest extent. The driving circuit of magnetic torquer mainly includes triangular wave generation circuit, preamplifier circuit, power amplifier circuit, current feedback, telemetry circuit and power supply and power off circuit. Its schematic diagram is shown in Fig. 3.

Triangular wave circuit

Control voltage signal

Preamplifier circuit

Current feedback circuit

Power amplifier circuit

Magnetic torque converter

Telemetry circuit

Fig. 3. Schematic block diagram of driving circuit of single channel magnetic torquer.

The drive control circuit adopts the mode of pulse width modulation (PWM) + pushpull drive, and controls the current on the magnetic torquer by adjusting the duty cycle of

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PWM signal. The driving circuit generates a triangular wave signal with fixed frequency and amplitude, which is the working frequency of the driving line. The selection of working frequency is related to the devices selected by the power amplifier driving circuit and the electrical parameters of the magnetic torquer body. The working frequency of the circuit is designed to be 2.1 kHz. When the given analog voltage signal is unchanged, it can output approximately constant magnetic moment and realize the linear controllability of magnetic moment. After the triangular wave signal is compared with the given analog voltage signal, a PWM signal is generated, which is used to control the push-pull drive circuit. The PWM signal is designed as a bipolar signal. When the duty cycle of the PWM signal is 50%, the push-pull circuit turns on for half a cycle in both positive and negative directions, and the output magnetic moment is 0; When the duty cycle of PWM signal is greater than 50%, the forward magnetic moment is output; When the duty cycle of PWM signal is less than 50%, the negative magnetic moment is output. Using this control method, the output dead zone near zero magnetic moment can be effectively reduced, and the control resolution of small signal and the output ability of small magnetic moment can be improved. At the same time, the driving circuit uses highprecision control voltage, excitation current closed-loop control and other technologies to comprehensively improve the output accuracy of magnetic moment.

5 Flight Results of Magnetic Control in Orbit

attitude angular velocity/(deg/s)

The magnetic control scheme proposed in this paper has been applied to a gravity measurement satellite. Its attitude stability curve during in orbit operation is shown in Fig. 4, and the angular velocity of magnetic control attitude is better than 3e−4 ◦ /s. The control voltage curve of the magnetic torquer is shown in Fig. 5, and the corresponding output magnetic moment curve of the magnetic torquer is shown in Fig. 6. The output resolution of the magnetic control voltage is better than 0.01 V, and the output accuracy of the corresponding magnetic moment is better than 0.06 Am2 . The flight data show that the system design meets the established requirements of high-precision magnetic control index, and provides a reference for the control system with high pointing and high stability by using magnetic torquer.

3.00E-04 1.00E-04 -1.00E-04 -3.00E-04 6:25:55 PM

6:54:43 PM

7:23:31 PM

7:52:19 PM

time/s Fig. 4. Attitude angular velocity curve of magnetic control.

The three-axis theoretical angular acceleration of the satellite is calculated by using the control voltage data of the on-board magnetic torquer and the geomagnetic field

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0.04 0.02 0.00 -0.02 -0.04 6:54:43 PM

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Ɵme/s

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Fig. 5. Control voltage curve of magnetic torquer.

0.00 -0.03 -0.06 -0.09 -0.12 6:54:43 PM

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Ɵme/s Fig. 6. Output magnetic moment curve of magnetic torquer.

angular acceleraƟon(deg/s2)

data of the satellite, combined with the environmental interference torque given by the environmental interference model. The three-axis theoretical angular acceleration of the satellite is shown in Fig. 7 below. Through analysis, the maximum angular acceleration of three axes is less than 5.0e5 deg /s2 (8.73e7rad /s2 ). If the residual deviation of satellite centroid is 50 µm, the coupling line acceleration disturbance at the accelerometer is only about 4.365e11 m/s2 , and the noise of the control system fully meets the working requirements of high-precision accelerometer.

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1.00E-04 0.00E+00 -1.00E-04 -2.00E-04 6:54:43 PM

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Fig. 7. On orbit angular acceleration curve of satellite.

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6 Conclusion In this paper, a set of “ultra quiet” magnetic control system with high-precision is proposed. The “ultra quiet”control is realized through high-precision measurement of geomagnetic field and high-precision magnetic moment output control of magnetic torquer. Through data analysis in orbit, the environmental micro vibration level at the high-precision accelerometer in the working process of the satellite control system has reached 4.365e11 m/s2 , which is much better than the previous load working environment of other satellites, and the established ultra quiet control goal has been achieved. At the same time, the minimum system response magnetic moment output by the control system is better than 0.06 Am2 , and the absolute accuracy reaches 2‰, which realizes the high control accuracy of the magnetic torquer. Based on the above discussion, the magnetic control scheme proposed in this paper can provide reference for the design of micro magnetic control satellite with high-precision.

References 1. Bandikova, T., Flury, J., Ko, U.-D.: Characteristics and accuracies of the GRACE inter-satellite pointing. Adv. Space Res. 2012(50), 123–135 (2012) 2. Kornfeld, R.P., Arnold, B.W., Gross, M.A., Dahya, N.T., Klipstein, W.M.: GRACE_FO the gravity recovery and climate experiment follow on mission. J. Spacecraft Rockets 56(2),934– 951 (2019)/ 3. Zhang, P., Wang, Y.F.: Application of ultra quiet platform in satellite high precision and high stability pointing. Spacecraft Environ. Eng. 24(3), 174–177 (2007) 4. Wang, B.L., Liao, H., Lin, X.H.: Requirement analysis of electrostatic accelerometer for gravity satellite platform. Spacecraft Environ. Eng. 27(6), 763–767 (2012) 5. Xin, N., Qiu, L.D., Zhou, N., Zhang, L.H.: Algorithm of high precision inter-satellite pointing control for KBR system. Spacecraft Eng. 25(2), 32–38 (2016) 6. Zhang, R.W.: Satellite Orbit Attitude Dynamics and Control, 2nd edn. Beijing University of Aeronautics and Astronautics Press, Beijing (1998) 7. Xiao, Q., Zhang, W.B., Meng, L.F.: Research on application of magnetorquer in magnetic cleanliness satellite platform. J. Astronaut. 37(2), 235–239 (2016)

Intercomparison of On-Orbit Consistency of Radiometric Calibration Between Two Sensor Calibration Spectrometers of HY Satellite Huiting Gao1(B) , Yue Ma1 , and Qingjun Song2 1 Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China

[email protected] 2 National Satellite Ocean Application Service, Beijing 100081, China

Abstract. HY-1C/1D are the latest ocean color satellites of China, SCS(Sensor Calibration Spectrometers) is a hyper-spectral calibrator used for cross calibration with the other remote sensors such as OCT and CZI of the same platform for improving the ocean color product. The On-Orbit radiometric method based on the solar diffuser of SCS is introduced, a novel cross calibration model based spectral reconstruction of SCS and other remote sensors is built, the verified result of the typical data is: the precision of the cross calibration arrived 93% by contrast with the retrieval radiance by SCS and the L1 level production of MODIS in 13 VNIR spectral bands. The calibration consistency between two SCSs is evaluated by the synchronous data, the result shows that the calibration differences among them have been within 5%, which means the SCS can meet the requirement of the cross calibration for other remote sensors on the same or different platform. Keywords: Sensor Calibration Spectrometers · Solar diffuser · Consistency of radiometric calibration

1 Introduction The ocean color remote sensing based on satellite is an important method for environment and disaster monitoring. The quantitative application is influenced by the precision of absolute radiance calibration, a major method for onboard calibration is the cross calibration, SeaWiFS is used for cross calibration with the COCTS of HY- 1A/1B by Jiang and Pan. The opportunity of cross calibration remote sensors of different platform is limited by the different orbit, geometry of observation, atmosphere condition and so on, the main problem of cross calibration is low frequency. HY-1C and HY-1D has been launched successfully in September 2018 and June 2020. In order to realize the high precision and frequency calibration for optical remote sensors in HY-1C/1D satellite, a hyper-spectral calibrator called SCS is carried. SCS is operated in the solar-reflected spectrum with wavelength range from 400 nm to 900 nm,101 spectral channels with a nominal 5nm interval and 11 cross-track pixels, corresponding to spectral and spatial dimensions, the solar calibration modules of SCS support onboard © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 630–637, 2023. https://doi.org/10.1007/978-981-19-9968-0_76

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spectral and radiometric calibration in the form of full bore and full FOV. The solar diffuser, reference solar diffuser and doped diffuser compose solar calibration modules. The calibration consistency between the two satellites need to be evaluated, one reason is the solar calibration modules attenuation is different because the launching interval of the two satellite is about two years, the other reason is the onboard calibration of SCS is operated individually, but the radiation quality of the constellation is represented by the consistency of calibration. MODIS is known as an onboard optical remote sensor with the highest precision of radiance calibration, a lot of work has been done for the consistency of calibration between Terra-MODIS and Aqua-MODIS. The consistency between two satellites was evaluated through the lunar observation, the lunar reflectivity model was modified by the distance between the earth and moon, the distance between the moon and the satellite and the lunar phase, the difference of spectral response was also eliminated, the result showed that the difference of calibration was less than 1% between the two satellite, the stability was better than 0.5%. NOAA-AVHRR was used for comparison of consistency in the band of 0.65 um, 0.85 um and 1.64 um, the result was the difference of calibration is less than 3%. The method and principle of SCS is introduced at first, the onboard modified model of radiance based on the solar diffuser is proposed, the precision of radiance calibration is verified through cross calibration with MODIS and SCS. The interval of local time of descending node of HY-1C/1D is nine hours, the sub-satellite point of two satellite is nearly the same at 70° north, the transformation model of radiance between the two SCS is built based on the spectral reconstruction, it is testified that the consistency was high through cross calibration of two satellites.

2 Onboard Calibration of HY-1C(1D)/SCS 2.1 Calibration Principle of SCS The solar calibration modules of SCS supports onboard spectral and radiometric calibration in the form of full bore and full FOV. A solar diffuser, a reference solar diffuser, a doped diffuser and a blank board are installed in a spinning plate with 90° between each other, the camera can image at different position while rotating the plate counterclockwise. The solar diffuser is used for modified the radiance calibration model, the reference solar diffuser is used for monitoring the attenuation of BRDF of the solar diffuser, the doped diffuser is used for onboard wavelength calibration. The sunlight is as the stabilized light source while solar calibration is operated, the reflectance of solar diffuser is much higher than the terrain especially the ocean, which means that the response of SCS to solar diffuser and terrain is very different. An attenuation screen is used for reducing the luminous flux that the solar diffuser received by the solar diffuser, so the energy of terrain is equal to that of the solar diffuser (Fig. 1). Combining the mathematical model of sensor response based on the laboratory data and the DN of solar diffuser, the real spectral radiance of solar diffuser is computed: Llab (λi ) = k1 (λi ) · k2 (λi , θj ) · (ξi (dn(λi )) − b1 (λi ))

(1)

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Fig. 1. Principle of solar calibration with solar diffuser.

where Llab (λi ) is the equivalent spectral radiance, k1 (λi ) and b1 (λi ) are the calibration coefficient of the ith spectral band, k3 (λi , θj ) is the uniform correction coefficient of the ith spectral band, ξi (dn(λi )) is the nonlinear correction factor of the ith spectral band, dn(λi ) is the response of the ith spectral band. The theoretical spectral radiance is calculated:     Es λj cos θCAL   LCAL θCAL , φCAL ; θi , φi ; λj = × fCAL θCAL , φCAL ; θi , φi ; λj × τ (θSAS , φSAS ) ρ(t)

(2)

  where LCAL θCAL , φCAL ; θi , φi ; λj is the reflective spectral radiance of solar diffuser at the calibration moment, θCAL and ϕCAL are the solar zenith angle and azimuth angle, θi and ϕi are the view zenith angle and azimuth angle of diffuser, Es (λj ) is the solar spectral  radiance out of the atmosphere, fCAL,t θCAL , φCAL ; θi , φi ; λj is the BRDF of the solar diffuser which changed with the solar incident angle, τ (θSAS , φSAS ) is the transmittance of the attenuation screen, because the radiance of FOV is in inverse proportion to the distance between the earth and the moon, which need to be modified by the factor ρ(t), it is calculated: ρ(t) = 1.000423 + 0.032395 sin θ + 0.000086 sin 2θ − 0.008349 cos θ + 0.000115 cos 2θ where θ = 2π(N − N0 )/365.2422 and N0 = 79.6764 + 0.2422 ×  (year−1985)−Int (year−1985)/4 . The modified factor of radiance of the ith pixel and the jth spectral band Fi,j is the ratio of the theoretical spectral radiance and the real spectral radiance: F=

LCAL (θCAL , ∅CAL , θi , ∅i , λj )   Llab DNCALi,j , DNCALi,j,0, Gain,CAL , tint , TFP,CAL

While Fi,j is fixed, the real radiance of target is calculated by:   LTG,i,j = Fi,j × Llab DNTGi,j , DNTGi,j,0, Gain,TG , tint,TG , TFP,TG

(3)

(4)

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2.2 Cross Calibration Method Based on Spectral Reconstruction The most important step of cross calibration with hyper-spectral sensor is the spectral radiance recovery from the spectral resolution of 5 nm to 1 nm. Given the spectral response function, the continuous spectral radiance with 1nm step is computed by a deconvolution method. The output equivalent spectral radiance and SRF of SCS is used for constructing the spectral radiance of FOV, the cubic spline interpolation and iteration successive approximation method are used for deconvolution: L0 = L = {Li,0 |(i = 1, 2, · · · , n)} L0 (λ) = spline_interp(L0 )

(5)

where L0 (λ) is the initial continuous spectral radiance with 1nm step after the cubic spline interpolation, the continuous spectral radiance after k-time cubic spline interpolation:  k L (λ)Si,0 (λ) k  Li = Si,0 (λ) Lk = {Lki |(i = 1, 2, · · · , n)} k+1

L

k+1

L

=

Lki

(6)

+ a(L − L ) k

(λ) = spline_interp(Lk+1 )

where a is set as 1, the iteration ended while the equation below is satisfied:     k L − L ≤ c 2

(7)

where 2 is the 2-norm, c = 10−6 . While the continuous spectral radiance L is obtained, the equivalent spectral radiance is calculated by convolution with L and response function. In order to evaluate the precision of cross calibration, the relative error is calculated by: ε=

Lonboard − Lcross × 100% Lonboard

(8)

3 Experiment and Analysis 3.1 SCS and MODIS The information of synchronous data of HY-1C/SCS and Terra/MODIS was shown in Table 1.. The image after registration of SCS and MODIS is shown (see Fig. 2).

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H. Gao et al. Table 1. Information of synchronous data.

Camera

Area of cross calibration Time Latitude

Longitude

SCS

130.0696

38.9554

MODIS

130.0696

38.9554

Solar elevation angle

Zenith angle

View angle Azimuth angle

22 May 2019 02:16:30

25.5008

162.501

0.277

22 May 2019 02:16:01

25.4934

114.596

1.508

Fig. 2. The image after registration of SCS and MODIS.

Obviously, because the two satellites arrived the cross point at the same time, the solar elevation angle is nearly the same, the difference of atmosphere is also neglected. The uniformity of cross area is better than 2% in thirteen bands. The inversed equivalent radiance and the radiance production of MODIS is compared in Table 2.. The largest error is less than 7%, which means that the onboard high precision of radiance calibration has been realized in SCS. 3.2 HY-1C/SCS and HY-1D/SCS On July 28th 2020, the HY-1C and HY-1D passed the cross point within 5 min, so the observation conditions are nearly the same, which means the apparent radiance was equal. The images of two SCS are shown in Fig. 3.

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Table 2. Difference of equivalent radiance Range of band (nm)

MODIS (mw/cm2/sr/um)

SCS (mw/cm2/sr/um)

Relative error (%)

620–670

2.5488

2.6407

– 3.6058

841–876

1.3019

1.3568

– 4.2175

459–479

7.1222

7.1026

0.2754

545–564

3.9573

4.2014

– 6.1679

405–420

8.2095

8.0485

1.9617

438–448

7.5305

7.3116

2.9064

483–493

5.9866

6.1228

– 2.2755

526–536

4.3871

4.6535

– 6.0708

545–556

4.1131

4.3684

– 6.2088

662–672

2.4367

2.5152

– 3.2208

673–683

2.3229

2.4295

– 4.5879

743–753

1.8474

1.9477

– 5.4315

862–877

1.2617

1.3316

– 5.5383

Fig. 3. The image after registration of SCS and MODIS

For each SCS, the measured equivalent radiance is calculated through the DN and calibration equations, then the apparent radiance of HY-1D is obtained by deconvolution method, so the equivalent radiance of HY-1C/SCS is computed by convolution with the apparent radiance and the spectral response function (see Fig. 4). By contrast, the average relative error is 4.26%.

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Fig. 4. The contrast with SCS of HY-1C and HY-1D.

4 Conclusion The absolute radiance calibration of HY-1C/1D-SCS is verified through cross calibration with MODIS, the difference of spectral response function in the modified model is modified by the spectral reconstruction method, the consistency of the atmosphere and the observation geometry is assured through near synchronous observation of two satellites. The difference of equivalent radiance between two satellites is less than 5%, which means the consistency of HY-1C/1D-SCS is well although they operate onboard calibration separately, so these two latest ocean satellites can have identical ability of radiance measurement.

References 1. Xiong, X., Sun, J., Barnes, W.: Intercomparison of on-orbit calibration consistency between Terra and Aqua MODIS reflective solar bands using the moson. IEEE Geosci. Remote Sens. Lett. 5(4), 778–782 (2008) 2. Wu, A., Xiong, X., Cao, C.: Terra and Aqua MODIS inter-comparison of three reflective solar bands using AVHRR onboard the NOAA-KLM satellites. Int. J. Remote Sens. 29(7), 1997–2010 (2008) 3. Xiong, X., Chiang, K., Sun, J., et al.: NASA EOS Terra and Aqua MODIS on-orbit performance. Adv. Space Res. 43(3), 413–422 (2009) 4. Chang, T., Xiong, X., Angal, A.: Terra and Aqua MODIS inter-comparison using LEO-GEO double difference method. Sensors, Systems, and Next-Generation Satellites XXII, Berlin, Germany, pp.107851G-1–107851G-10 (2018) 5. Aldoretta, E.J., Twedt, K.A., Angal, A., et al.: On-orbit performance of the Terra and Aqua MODIS solar diffuser stability monitor. Earth Observing Systems XXIII, San Diego, California, United States, pp. 107641O-1–107641O-10 (2018) 6. Guenther, B., Sun, J., Moeller, C.: MODIS cross-talk effects and areas of potential performance differences for Terra from Aqua characteristics. Earth Observing Systems XXIII, San Diego, California, United States, pp. 1076411-1–1076411-11 (2018)

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7. Xin, L., Liming, Z., Xiaolong, S., Hongyao, C.: Accuracy verification of on-board radiometric calibration. Acta Optica Sinica 40(9), 0928001-1–9 (2020) 8. Huang, W., et al.: Influence of Non-uniformity of light attenuated by solar attenuation screen on on-board calibration. Acta Optica Sinica 40(4), 0929002-1–7 (2020) 9. Huang, W., et al.: On-orbit performance evaluation of on-board calibration component of GF5 visible and infrared multispectral imager. Acta Optica Sinica 40(20), 2029001-1–2029001-8 (2020) 10. Du, L., et al.: Multi-sensor cooperative radiometric calibraion method of mapping satellite-1. Acta Optica Sinica 39(4), 0401001-1–8 (2019)

Loose Formation Keeping Control for Low-Earth Orbit Satellite Cluster Considering the Interaction Between Cluster Satellites Huang He1,2(B) , Yang Xu3 , Zhang Qi3 , and Jing Baohong4 1 Northwestern Polytechnical University, Xian 710072, China

[email protected]

2 Ningbo Institute of NPU, Ningbo, China 3 State Key Laboratory of Astronomic Dynamics, Xian 710043, China 4 Troops 63755 PLA, Xian 710043, China

Abstract. Satellite clusters are required to keep the satellites within the given distance bounds for the entire mission lifetime. The loose formation maintenance and collision avoidance problem is decomposed into two control problems: One is distance keeping in the along-track direction while another is collision avoidance control in the radial/cross-track (xz) plane. A nonlinear optimal control method is proposed based on the relative orbital elements. The average semi-major axis and cross- track velocity increment are selected as the optimal variables; the maximum/minimum relative distances between the cluster satellites are considered as constraints, whereas the total fuel usage of the satellite cluster in a long-period time is used as the objective function. The fuel cost in the cross-track direction is relative to the phase angle where the impulse is applied. Simulation results of a low-earth orbit satellite cluster considering the interaction between cluster satellites demonstrate the feasibility of this method. Keywords: Satellite cluster · Loose formation · Collision avoidance · Formation keeping · Optimal control

1 Introduction A satellite cluster is a type of distributed space system, consisting of multiple satellites under minimum and maximum distance constraints over long time intervals [1]. Satellite swarms have recently captured the interest of the scientific community due to their ability to achieve previously unattainable science objectives [2]. One of the main challenges of satellite clusters is to keep the modules within a bounded distance, typically less than 100 km, for the entire mission lifetime. Three typical methods are proposed to generate the control acceleration for cluster flight control, including differential drag [3], intersatellite electromagnetic forces [4], and impulse thrusters [5]. A method for examining differential-drag-based cluster flight performance in the presence of noise and uncertainties was developed [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 638–646, 2023. https://doi.org/10.1007/978-981-19-9968-0_77

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For the satellite cluster-keeping problem, if an orbital component of a single satellite changes, then the distances relative to other satellites will be changed, hence influencing the state of the entire cluster. Thus, the cluster-keeping problem is an implicated control problem. In this study, we will consider the interaction between cluster satellites, the control output of one satellite should have the least influence on other satellites. The goal of this study is to develop a coordination control method to keep the cluster satellite distances in a given zone while considering collision avoidance constraints. Based on the relative orbital element model, the relative-distance-keeping problem in a satellite cluster is transferred to be a nonlinear programming problem in the alongtrack direction, while the collision avoidance problem is transferred to be a nonlinear program problem vertical to the along-track direction. In this way, considering the relative influences among the cluster satellites as the constraints, the control law can be used for loose formation keeping in satellite clusters. The paper is organized as follows. In Sect. 2, the relative motion equation and velocity increment control equation for the satellite cluster are described. In Sect. 3, the satellite boundary flight control method for the cluster is introduced. In Sect. 4, the collisionavoidance constraints are discussed, and the coordination control algorithm is presented. In Sect. 5, the relative motion amplitude suppression method in the cross-track direction is proposed. In Sect. 6, simulation results are discussed.

2 Relative Motion Description of Satellite Cluster The relative orbit coordinate of the reference satellites is defined as Oxyz, where O is the center of mass of the reference satellite, Ox is the radial direction pointing from the Earth’s center to the satellite, Oz is the cross-track direction along the normal direction of the orbit plane, and Oy forms a right-handed triad. When the chaser satellite is controlled by Vx , Vy and Vz , we can obtain [7]: 

2  2    2   4 ae− sin β − + Vx + ae− cos β − + Vy n n 2 xd+ = xd− + Vy n 2 + − yd = yd − Vx n    − 2  − 4 + − − β = atan2 ae sin β + Vx , ae cos β + Vy n n  2    Vz  −  2 − + zmax = cos ψ − + + zmax sin ψ − zmax n      Vz  − − − β+ γ + = atan2 zmax sin ψ − , zmax cos ψ − + n ae+

=

(1)

where ae is the semi-major axis of the projection ellipse in the xy-plane; β is relative true anomaly; n is the orbital angular velocity; (xd , yd ) is the center of the projection ellipse,

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where xd is the radial offset, and yd is the along-track offset; zmax is the maximum cross-track displacement; ψ is the phase angle, ψ = 0 and ψ = π referring to the ascending node and descending node of the relative orbit. The superscript “+” stands for the moment when the velocity impulse is applied, and the superscript “−” stands for the moment before the velocity impulse is applied.

3 Relative Distance Maintenance in the Along-Track Direction For loose formation keeping, the satellites need not keep strict geometry constraints; they are only required to keep in a given zone for satisfying the communication and collision avoidance constraints [8]. The relative position should satisfy the following condition: 2 x2 + y2 + z 2 ≤ Dmax

(2)

where Dmax is the maxim relative distance allowed, Dmax can be within several kilometres or hundreds of kilometres. We found that when a = 0, yd drifts very quickly. If the orbital inclination angles of the cluster satellites are not equal, the change rate of zmax is larger than that of ae . Therefore, to ensure that the relative distance among cluster members is maintained in a given range, it is important to control yd . Thus, we use ae , xd , yd and zmax to describe the distance constraints as follows: |yd | ≤ yd ,lim |xd | ≤ xd ,lim ae ≤ ae,lim zmax ≤ zmax,lim

(3)

where yd ,lim , xd ,lim , ae,lim and zmax,lim are the maximum values allowed. Then, we can transfer the constraint condition in Eq. (2) to Eq. (4).  2 2 2 xd2 ,lim + yd2 ,lim + ae,lim + zmax,lim ≤ Dmax

(4)

As ae and zmax changed very slowly compared to yd , the relative distance keeping problem can be decomposed into two problems: one is relative position keeping in the along-track direction, and another is collision avoidance in the xz plane. If we design the collision avoidance strategy in the xz plane, the minima of ae and zmax should also be preassigned as follows: ae ≥ 2Rcol zmax ≥ Rcol

(5)

where Rcol is the safety distance between cluster satellites. As y˙ d = − 1.5ns a = −1.5ns xd , so yd can be controlled by changing a between cluster satellites. Considering a LEO satellite cluster G = {Ss |s = 1, 2 · · · P }, where Ss stands for the satellites in the cluster. Gs = G − Ss stands for the sub-cluster in which Ss is excluded from G, Gs is the collection of the neighbours of Ss . Taking the interaction between the cluster satellites into account, controlling the semi-major axis of one satellite will inevitably affect the semi-major axis difference between the satellite and all its neighbours, as shown in Fig. 3. If the desired semi-major

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+ axes a+ s and a c are not properly selected, the control frequency of the whole cluster will be increased; some satellites will require more fuel. Therefore, it is important to + optimize the values of a+ s and a c . The optimization method uses the minimum fuel cost required to keep the satellite cluster in a boundary zone. Trest,cs is the required time for yd ,cs to reach the boundary; then for any time t = tn , we can obtain  3  (6) yd ,cs (tn ) − ns acs + a˙ cs Trest,cs Trest,cs = ±yd ,lim 2

For Eq. (6), a feasible solution should be selected according to the actual situation. The value of Trest,cs determines the control frequency; a large Trest,cs can reduce the control times. If the velocity increment Vy is applied to satellite Ss , then   −   +   + 2 + − − a+ cs = a c − a s − a c − a s = − a s − a s = − Vy . ns

(7)

Hence, we can use the average semi-major axis change to evaluate the fuel cost. Now, considering all the satellites in cluster G, satellite s is selected as the controlled satellite, and the velocity increment is Vy . Then we can use Eq. (7) to calculate the residual time between satellite s and all other satellites, which can be recorded as Trest,1s ,Trest,2s

,……, Trest,Ps . Trest = Sc ∈Gs wt,cs Trest,cs , where wt,cs is a positive weighting coefficient. The larger the value of wt,cs , the more important is Trest,cs between satellites s and c. The value of wt,cs is determined according to the space mission or residual fuel of each satellite. Define f2 as the optimization objective function, we are aimed to increase Trest using minimum fuel. Considering the constraints of ae , the along-track relative distance keeping problem can be written as a nonlinear programming problem as follows: As a+ cs is determined by the optimization variable Vy ,   Vy  min f2 =

wt,cs Trest,cs Sc ∈Gs

⎧  wt,cs Trest,cs > 0 ⎪ ⎪ ⎪ ⎨ Sc ∈Gs s.t. + ≤ ae,lim 2Rcol ≤ ae,cs ⎪ ⎪ ⎪ ⎩  +  acs ≥ a

(8)

4 Collision Avoidance in the xz Plane When xd = 0, the relative motions in the radial and cross-track directions are both periodical vibrations. Defining ρ as the relative distance between reference satellite and chaser satellite in the xz plane, we obtain  2 ae − cos β + (zmax sin ψ)2 ρ= 2

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=



−

2 ae cos β + (zmax sin(γ + β))2 . 2

(9)

In the condition without perturbation, γ is constant, and β sweeps linearly over 2π radians during one orbit period. γ determines the shape of the relative motion trajectory in the xz plane. When γ = 0◦ , the relative motion trajectory is far away from the centre point; thus, the collision probability in the xz-plane is small. However, when γ is close to 90° or 270°, the shape of the trajectory becomes narrow; thus, the minimum distance from the centre point to the trajectory becomes small, and the collision probability in xz plane increases. To avoid collision, the relative distance between reference satellite and chaser satellite in the xz plane should be kept larger than the safe distance. x2 + z 2 ≥ R2col

(10)

Therefore, the relative motion trajectory in the xz plane should be kept away from the origin point. First, ae /2 and zmax should be larger than Rcol ; then, γ should not be close to 90° and 270°. Define γsgo is the angle margin of γ to the nearest danger area, which can be called + − − γsgo,cs as the increment of residual γ between residual γ . Define γsgo,cs = γsgo,cs satellite c and satellite s after the impulse is applied. The collision avoidance problem in the xz plane can be transferred to a nonlinear programming problem as follows: min f3 =

|Vz | Sc ∈Gs wγ ,cs γsgo,cs

⎧  wγ ,cs γsgo,cs > 0 ⎪ ⎪ ⎪ ⎨ Sc ∈Gs s.t. γcs+ ∈ ϒsafe,cs ⎪ ⎪ ⎪ ⎩ + Rcol ≤ zmax,cs ≤ zmax,lim

(11)

where wγ ,cs is a positive weighting coefficient, denoting the importance degree of residual γ between reference satellite s and chaser satellite c. The value of wγ ,cs can be selected based on the relative motion state or residual fuel of each satellite. zmax,lim should not be too small, in order to make sure Eq. (11) have solutions. For the along-track relative position keeping, we can only guarantee that ae /2 is larger than the minimum safety distance, the influence on γ is not considered. If the along-track impulse causes γ close to the danger zone, then it can be calculated and evaluated before each impulse is applied. With Eq. (11), an extra normal impulse can be obtained, which can be used to compensate for γ .

5 Relative Motion Amplitude Suppression in the Cross-Track Direction Using the normal velocity increment for collision avoidance will cause the orbital incli˙ = 0, which will nations of the reference and chaser satellites to be different. That is, 

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make the relative motion amplitude zmax have a long-term drift. Although we have considered the maximum boundary constraint in Eq. (11), the collision avoidance control will only be applied when γ is close to the danger zone. If zmax is small, it may exceed the boundary even if γ is far away from the danger zone because of the difference in orbital inclination. Therefore, it is necessary to suppress the relative motion amplitude in the cross-track direction. zmax _sgo,cs = zmax,lim −zmax,cs is the distance between zmax,cs and the maxim boundary, which is called the amplitude margin. f4 is the required cross-track impulse to increase the amplitude margin per unit. Using Vz as the optimization variable, we can build a nonlinear programming model as follows: min f4 =

|Vz | w Sc ∈Gs z,cs zmax _sgo,cs

⎧  wz,cs zmax _sgo,cs > 0 ⎪ ⎪ ⎪ ⎨ Sc ∈Gs s.t. γcs+ ∈ ϒsafe,cs ⎪ ⎪ ⎪ ⎩ + Rcol ≤ zmax,cs ≤ zmax,lim

(12)

+ − Here, zmax _sgo,cs = zmax _sgo,cs − zmax _sgo,cs is the increment of the amplitude margin, and wz,cs is a positive weighting coefficient. Equation (12) has the same optimization variable as Eq. (12); the difference is Eq. (12) is used when zmax exceeds the boundary, and Eq. (12) is used when γ is close to the danger zone. If zmax,lim is large, as Eq. (12) has the constraint of zmax , then it is possible that only Eq. (12) is used during the whole mission.

6 Simulation Results The simulation parameters are set as follows: wt,cs = wγ ,cs = wz,cs = 1, a = 5 m, yd ,lim = 20 km, zmax,lim = 7 km, xd ,lim = 200 m, ae = 14 km, and Rcol = 100 m. Considering 21 order Earth oblate perturbation and atmospheric perturbation. The total simulation time is 1000T.The area-to-mass ratios of each satellite are as follows: Sat1:0.003 m2 /kg, Sat2:0.004 m2 /kg, Sat3:0.002 m2 /kg. Sat4:0.003 m2 /kg, Sat5:0.005 m2 /kg. For long-period time loose formation keeping, there is no constant reference satellite in the cluster, and the absolute orbital elements of each satellite are given in Table 1. The relative position of Sat2, Sat3, Sat4, Sat5 relative to Sat1 is shown in Fig. 1. If the area-to-mass ratio is different, then a˙ = 0; the average semi-major axis difference changes as in Fig. 2. Relative distance keeping in the along-track direction and collision avoidance are considered two independent processes. The fuel costs of relative distance keeping in the along-track direction for both cases are small, and the maxim is 0.0533 m/s. During the entire mission, collision avoidance in the xz plane is considered. In case 2, the fuel cost of cross-track amplitude control is zero for all satellites. In case 1, only

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H. He et al. Table 1. Initial orbit elements of the five satellites in the cluster e

Ω/°

a/m

Sat1

7078137

0

98.1928

0

279.066

0

Sat1

7078164.57

0.000211972

98.1928

330.00

279.053

30.014

Sat3

7078199.36

0.000232994

98.1928

250.05

279.048

109.97

Sat4

7078173.69

0.000211777

98.1928

240.03

279.081

119.99

Sat5

7078156.14

0.000310868

98.1928

79.92

279.079

280.05

(a) History of yd

i/°

ω/°

No.

f /°

(b) Relative motion trajectory in the xz-plane

Fig. 1. Motion of other satellites relative to Sat1

Fig. 2. Average semi-major axis difference between different satellites

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Sat4 used fuel for normal amplitude control. If Vz is applied by a satellite, it means all the other satellites are far from the satellite; then the fuel cost for collision avoidance is small, as observed in Table 3. Furthermore, considering the relative motion in the along-track direction, collision avoidance control is only applied in the xz plane when γ is close to the danger zone, and the fuel cost can be considerably reduced. However, the fuel cost of cross-track amplitude suppression will be increased. Overall, the total fuel cost will be reduced. The fuel cost of each satellite is different; Sat3 uses more fuel than other satellites in both cases. The fuel cost in the along-track direction is related to the initial semi-major axis and area-to-mass ratio. Compared to other satellites, the average initial semi-major axis difference and area-to-mass ratio are much larger. An optimal control method is designed to find the satellite that can reduce the fuel cost; this is the reason why Sat3 uses more fuel than others. The fuel cost in the cross-track direction is relative to the phase angle where the impulse is applied. The control method constraints and orbital motion characters decide the impulse applied time during each control cycle. If the impulse applied time is different, the fuel cost is different; thus, the total fuel cost along the cross-track direction cannot be guaranteed to be optimal.

7 Conclusions In view of the long-term bounded motion maintenance control of the spacecraft swarm, according to the long-term change characteristics of the relative orbit elements, the relative distance keeping and collision avoidance problems were decomposed into two control problems: one is distance keeping along the in-track direction, while another is collision avoidance control in the xz plane. The optimal control problem along the in-track direction was designed according to the minimum fuel consumed per unit time. The collision avoidance constraint was analysed from the geometric point of view. Collision avoidance was realized by controlling the phase angle difference between the relative motion in the orbit plane and normal motion using normal velocity increments. Simulation results indicate an overall reduction in fuel cost, thereby demonstrating the feasibility of this method. The proposed method can be used for maintaining fractionated spacecraft formation in low earth orbit. The limitation is that calculation complexity increases when cluster members increase. Future works should consider the distributed control law that can satisfy the requirements of a satellite cluster with more than 100 satellites.

References 1. Edlerman, E., Gurfil, P.: Cluster-keeping algorithms for the satellite swarm sensor network project. J. Spacecr. Rocket. 56(3), 649–662 (2019) 2. Lippe, C., D’Amico, S.: Autonomous Reference Orbit Refinement for Optimal Swarm Reconfiguration, AIAA Scitech 2021 Forum, 11–15 & 19–21 Jan 2021 (2021) 3. Ben-Yaacov, O., Gurfil, P.: Long-term cluster flight of multiple satellites using differential drag. J. Guid. Dyn. Control 36(6), 1731–1740 (2013)

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4. Huang, H., Yang, L.-P., Zhu, Y.-W., Zhang, Y.-W.: Collective trajectory planning for satellite swarm using inter-satellite electromagnetic force. Acta Astronaut. 104, 220–230 (2014) 5. Zhang, H., Gurfil, P.: Nanosatellite cluster keeping under thrust uncertainties. J. Guid. Dyn. Control 37(5), 1406–1414 (2014) 6. Ben-Yaacov, O., Ivantsov, A., Gurfil, P.: Covariance analysis of differential drag-based satellite cluster flight. Acta Astronaut. 123, 387–396 (2016) 7. Schwartz, J., Krenzke, T., Hur-Diaz, S., Ruschmann, M., Schmidt, J.: The flocking controller: a novel cluster control strategy for space vehicles. In: AIAA Guidance, Navigation, and Control (GNC) Conference, 19–22 Aug 2013, Boston, MA (2013) 8. Freimann, A., Rummelhagen, M., Reichel, F., Marszalek, M., Schmidt, M., Schilling, K.: Analysis of wireless networks for satellite swarm missions. In: 3rd IFAC Symposium on Telematics Applications, 11–13 Nov 2013. Seoul, Korea (2013)

Research on Data Fusion Architecture of GNC Subsystem of China Space Station Jingsong Li, Jing Wang, Haixin Yu, Xiaofeng Li(B) , Ruiming Zhong, Xiaogang Dong, Zhaohui Chen, and Junchun Yang Beijing Institute of Control Engineering, Beijing 100090, China [email protected]

Abstract. Data fusion is a significant approach in GNC subsystem design, which realizes credible selection of multiple-redundant backup data and performance optimization of GNC algorithm. Firstly, the paper outlines miscellaneous data processing problem encountered by GNC subsystem of China Space Station, GNC subsystem is composed of multiple-redundant controllers, communication buses and parts, more than 400 data items with diverse attributes are involved. Therefore, a data fusion architecture based on three-layers of data processing is proposed, which generates couples of key technologies, such as, the spatial dimension data fusion for input data of quad-modular redundant Byzantine fault-tolerant computer, the temporal dimension data fusion for repeated backup data that are received for multiple cycles, and the algorithm related data fusion for attitude and orbit determination. In-orbit application of the architecture performs well and makes GNC subsystem run efficiently and reliably. Subsequently, research on data fusion at higher level of abstraction will be carried out, which will make sufficient preparations for onboard fault autonomous diagnosis and adaptive tolerance of China Space Station in the future. Keywords: Data fusion · Quad-modular · China Space Station · GNC subsystem

1 Introduction As one of the subsystems composing spacecraft, GNC (Guidance Navigation and Control) is the control center and its normal or not will directly affect normal business or safety of spacecraft. It usually be responsible for the measurement and control of spacecraft’s attitude and orbit, moreover, it could be capable of rendezvous and docking control, ensuring energy security and so on. In order to ensure long-term reliability of China Space Station in orbit, the GNC subsystem is equipped with multiple redundant resources, for example, GNCC (Guidance Navigation and Control Computer), sensors, actuators, information interface units and other devices backup in the individual capsule or assembly composed of multiple capsules. Among them, GNCC is a quadmodular redundant Byzantine fault-tolerant computer [1], which has the ability of normal operation without degradation in the worst-case scenario of two modules failure. The communication bus networks of GNC subsystem mainly adopts MIL-STD1553B (subsequent referred to 1553B for short), which is divided into internal and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 647–655, 2023. https://doi.org/10.1007/978-981-19-9968-0_78

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external independent ones. The internal bus network, also known as GNC bus network, takes GNCC as the BC (Bus Controller, a term dedicated to 1553B) which is responsible for communication with various sensors, actuators, information interface units and other devices in GNC subsystem actively. The external bus network, also known as OBDH bus network, takes GNCC as the RT (Remote Terminal, a term dedicated to 1553B) which is responsible for communication with other subsystems or ground passively. As mentioned above, while abundant system resources bring high reliability to GNC subsystem, how to effectively integrate system resources and process miscellaneous data becomes the primary problem to be solved in GNC subsystem design.

2 Data Fusion Related Research Data fusion is an information processing technology that uses computer to automatically analyze and synthesize some observation information obtained according to time sequence, so as to complete the required decision-making and evaluation tasks [2]. Engineering practice shows that due to the influence of measurement accuracy and noise, it is difficult to accurately characterize the real physical state of the measured object by relying on information only from one single device. Therefore, information from multiple devices should be fused in order to reduce measurement error and improve measurement accuracy. The essence of data fusion is to process the gathered data of multi-sources and multi-levels respectively, so as to produce consistent interpretation or description for the measured object. Data fusion has been a research hotspot in academia and industry for decades, and there have been many research and application achievements. Reference [3] reviews techniques of multi-source remote sensing data fusion and discusses their trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. In reference [4], aiming at the problem of missile impact point positioning, a data fusion method based on photoelectric theodolites and sighthearings is proposed, and the impact point positioning accuracy is significantly improved. Meanwhile, the concepts of spatial dimension fusion and temporal dimension fusion of multi-sensor are mentioned. Reference [5] studies several data fusion methods, such as median fusion, moment estimation fusion, weighted least squares fusion and fuzzy relation fusion. Reference [6] introduces a driving assistance system based on data fusion of multiple sensors for autonomous driving, which clarifies kinds of orientation related data fusion algorithm. Reference [7] addresses the fusion processing techniques for multisensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. In Reference [8], for improving the monitoring precision of water quality, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision. Reference [9] studies the optimal online data fusion algorithm for MEMS gyroscope array, which reduces the filtering error and calculation time, and improves the attitude measurement accuracy by an order of magnitude.

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Although the above mentioned literatures are not completely consistent with the method in this paper, they play a certain helpful role.

3 Data Fusion Architecture Data fusion architecture in this paper mainly focuses on data or pixel level, taking into account two dimensions of space and time, to realize credible selection of multipleredundant backup data and performance optimization of GNC algorithm, so as to ensure the validity, unity and synchronization of data processing of GNCC application software. A data fusion architecture based on three-layers of data processing is proposed. Three-layers respectively refer to communication layer, resolution layer and algorithm layer, and data processing measures of each layer are determined according to data attributes. Meanwhile, there should be a data collection buffer pool under three-layers, which serves as critical role of realizing function decoupling between data collection and upper data fusion layers. The paper outlines architecture design with bottom-up sequence (see Fig. 1).

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3.1 Data Pool In order to realize the synchronous operation of four modules of GNCC, the hardware interface status data, GNC bus and OBDH bus communication data collected by

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each module should be exchanged and shared in every control cycle, so that the input conditions of four modules are completely consistent. The mechanism of Data Pool applies centralized management of all input data items of GNC subsystem, abstracts data content and normalizes data format so as to unified storage and extraction data items. To overrides all input data categories, the reserved memory space for Data Pool is about 10KB. Data Pool is often referred in spacecraft flight software design, for example, in the study of AOCS software architecture carried out by ESA, its Data Pool provides shared memory services for each functional component of AOCS software architecture to realize data decoupling between functional components [10, 11]. 3.2 Communication Layer Data Fusion As the initial stage, data fusion at communication layer mainly addresses the preliminary judgment and selection of valid data problem related to device communication and four modules sharing, among them, GNCC quad-modular redundant spatial dimension data fusion is a significant representative. The input data of GNCC need to be fused by voting after two rounds of data exchange to determine whether the data collected by each module can be used as GNCC input data. Since the even-numbered modules does not have voting capability, an independent information unit NE acting as “the fifth module” is added to GNCC to participate in data exchange and voting operation. Illustration as follow, suppose that module A, B, C, and D send data marked as a, b, c, and d respectively, and module C is faulty. Among them, module A, B and D send data normally, while module C sends data abnormally (the abnormal data due to uncertainty expressed in x1, x2 and x3… etc.). In the first round of exchange, each module sends its own produced data to other modules, so that all of four modules and NE have full enveloping data vectors, as follows: A (a, b, x1, d ) B (a, b, x2, d ) C (a, b, c, d ) D (a, b, x3, d ) E (a, b, x4, d )

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In the second round of exchange, each module sends its own fully enveloping data vectors to other modules, then the data owned by each module will form a data matrix,

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which could vote to determine which module is faulty, as follows:

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In the data matrix, fault data is decided according to the majority principle. For each column of the five matrices, the voting result is below, indicating that module C has been diagnosed as a fault by voting, and the data collected by module C is invalid and no subsequent data fusion is required. A : (a, b, uncertainty, d ) B : (a, b, uncertainty, d ) C : (a, b, uncertainty, d ) D : (a, b, uncertainty, d ) E : (a, b, uncertainty, d )

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3.3 Resolution Layer Data Fusion As the intermediate stage, resolution layer mainly solves data convergence of four modules’ hardware interface status, spatial dimension data fusion of data items collected within the same control cycle, temporal dimension data fusion of data items collected within multiple control cycles. First-order predicate logic based data fusion method illustrates the temporal dimension related data fusion in resolution layer. The background of temporal dimension data fusion is as follows. Firstly, due to the limited size of the memory space of input exchanging area, injection instruction data forwarded by TT&C and GNSS data from individual capsule adopt time-sharing multiplexing mechanism. Secondly, since GNCC four modules executing cannot be completely consistent, there is bound to be a time gap in executing time sequence, so that broadcast messages (broadcast command and broadcast time information) by four modules may receive in adjacent control cycles. Thirdly, in order to ensure the telecommand uplink reliability, usually the bus command and injection instruction data sent from the ground will be repeated for three consecutive times, so GNC subsystem will receive repeated data in multiple control cycles. Based on the above kinds of circumstances, requirement for integration of data received within multiple control cycles. To

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be more precise, symbolic representation of temporal dimension data fusion based on first-order predicate logic proposition: Predicate Definition: P(x) : x is the latest received data. Q(y) : y is the last received data. C(x, y) : The checksums of x and y are different. T (x, y) : The receiving time gap between x and y is greater than T . E(x) : x is valid and should be processed .

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First-Order Predicate Logic Proposition: (5) Expressed by the above proposition, for any x, if it satisfies the C (x, y) or T (x, y) relationship with existed y, the x is considered valid and should be processed. Meanwhile, a consistent schematic is shown below (see Fig. 2), recording identification(checksum) and time stamp of the first received data, only the first reception of repeated backup data is processed within the limited time(related to the control cycle), and the subsequent receptions are eliminated and cannot be processed. the subsequent receipt of repeated data the subsequent receipt of repeated data the first receipt of repeated data

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3.4 Algorithm Layer Data Fusion As the high-order stage, algorithm level data fusion is mainly responsible for data processing of orbit and attitude determination, which achieves the accuracy of spacecraft attitude and orbit control eventually.

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Significant achievements have been made in the research on the methods of low-orbit satellite orbit determination based on orbital elements [12] and orbit determination based on GNSS (Global Navigation Satellite System) navigation information [13]. The in-orbit autonomous orbit determination means of the space station also mainly rely on these two methods, which are mutually backup and each has its own advantages. The former is a one-step extrapolation calculation based on the 19 orbital elements periodically updated by the ground, which has the characteristics of continuous and reliable. However, if the 19 orbital elements are not updated for a long time, the extrapolation accuracy of the orbit will gradually disperse with time. The latter carries out inertial extrapolation based on position and velocity data collected from GNSS in real time, and supports automatic update of 19 orbital elements. Since based on real time measurement, the extrapolation has the advantage of high accuracy, but the availability is affected by whether GNSS data is updated and effective. In order to realize the high reliability and high precision of orbit determination, 19 orbital elements one-step extrapolation and inertial extrapolation of GNSS data are combined for orbit determination data fusion. When GNSS data is updated and effective, inertial extrapolation method is preferred. The 19 orbital elements updated from GNSS data automatically can be used as the benchmark for 19 orbital elements onestep extrapolation. The data flow of orbit determination data fusion is shown below (see Fig. 3). orbit determination results

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4 Application Validation The data fusion architecture described in this paper has been applied in the GNC subsystem of the launched core capsule of China Space Station, and its in-orbit application performed well, which makes GNC subsystem run efficiently and reliably.

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According to in-orbit supervisor information, the temporal dimension data fusion based on first-order predicate logic proposition had eliminated duplicated data items successfully more than thousands of times in past 15 months. Meanwhile, taking the orbit determination data fusion for example, after the data fusion of 19 orbital elements one-step extrapolation and inertial extrapolation of GNSS data, 19 orbital elements frequently updated by the ground is not necessary, and the position deviation will always be maintained at the order of 10−2 km, while the speed deviation will always be maintained at the order of 10−4 m/s, which significantly improves the orbit determination accuracy and stability.

5 Conclusion This paper proposes a data fusion architecture based on three-layers of data processing, which can effectively meet the data fusion requirements of multiple redundant backup systems such as China Space Station. It generates couples of key technologies, such as, spatial dimension fusion of quad-modular redundant Byzantine fault-tolerant computer, the temporal dimension data fusion based on first-order predicate logic and the orbit determination data fusion based on GNSS and elements-extrapolation. This paper mainly focuses on data fusion at the data/pixel level, and mainly solves the problem of data fusion at the low abstract level of GNC subsystem. Subsequently, research on data fusion at higher abstract levels will be carried out, so as to make sufficient preparations for onboard fault autonomous diagnosis and adaptive tolerance of China Space Station in the future, and will facilitate information fusion of domain requirements in intelligent program synthesis research based on software IP [14]. Acknowledgment. This work is supported by the National Natural Science Foundation of China (62192730, 62192735, U21B2015).

References 1. Xiao, A.B., Hu, M.M., et al.: Reliability analysis of the computer with quad-modular redundancy byzantine fault tolerant. Aerospace Control Appl. 40(3), 41–46 (2014) 2. Wikipedia Homepage. http://en.volupedia.org/wiki/Data_fusion. Accessed 17 Jan 2022 3. Zhang, J.X.: Multi-source remote sensing data fusion: status and trends. Int. J. Image Data Fus. 1(1), 5–24 (2010) 4. Jia, W.M.: Impact point positioning method based on multi-sensor data-fusion. Nuclear Electron. Detect. Technol. 24(5), 484–487 (2004) 5. Li, H.W., Liu, Z.D., et al.: Research on multiple source data fusion method. Nuclear Power Eng. 39(3), 77–80 (2018) 6. Yang, J., Liu, S., et al.: Driving assistance system based on data fusion of multisource sensors for autonomous unmanned ground vehicles. Comput. Netw. 21, 108053 (2021) 7. Noh, S: Intelligent data fusion and multi-agent coordination for target allocation. Electronics 9, 1563 (2020) 8. Liu, Q.: Intelligent water quality monitoring system based on multi-sensor data fusion technology. Int. J. Ambient Comput. Intell. 12 (2021)

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9. Chen, W.W., Liu, Y., et al.: Signal fusion of silicon micro-gyroscope array of micro/nano-sate. Opt. Prec. Eng. 27(1), 172–180 (2019) 10. Pasetti, A.: AOCS Framework–Concept Level Description. University of Constance Department of Computer Science (2002) 11. Pasetti, A.: Inter-Component Communication Framelet Concept and Architecture Description. University of Constance Department of Computer Science (2002) 12. Li, D., Yu, Y.: Prediction algorithm of close-orbit satellite based on orbit elements. Opt. Precis. Eng. 24(10), 2540–2548 (2016) 13. Liao, X., He, M., et al.: Research of Quasi-mean orbital elements determination method based on GNNS navigation. In: The first academic exchange conference and the fourth Small Satellite Technology Exchange Conference of Advanced Small Satellite Technology and Application Committee of Chinese Society of Astronautics, pp. 60–64 (2017) 14. Yang, M.F., Gu, B., et al.: Intelligent program synthesis framework and key scientific problems for embedded software. Chinese Space Sci. Technol. 42(4), 1–6 (2022)

Application in Emergency Tasks Schedule with the GFDM-1 Satellite Mingliang Liu, Hu Qiu(B) , and Ziwei Li(B) China Centre for Resources Satellite Data and Application, Beijing, China [email protected]

Abstract. Emergency task has characteristics like suddenness, high efficiency and so on. The GFDM-1 satellite has several favorable features to deal with emergency tasks such as multi-angle image mode, priority setting of telecommands, task reset, data processing on orbit and data transmission to relay satellite. Considering rolling schedule strategy of measurement-controlling windows and data transmission windows, designed emergency tasks schedule process, through actual verification, increased success rate in task execution, improved access to effective data, shorted the time for data playback. Keywords: GFDM-1 satellite · Emergency task · Data transmission to relay satellite · Multi-angle image · Telecommands priority · Rolling schedule

1 Introduction Since the launch of China’s first civilian land observation satellite CBERS-01 in October 1999, China’s land observation satellites have begun to provide emergency services for major natural disasters and emergencies in China and international. Using remote sensing satellites to obtain emergency mission data requires rapid shooting of emergency areas, rapid transmission of emergency observation data to the ground and rapid data processing, so that the observation data of emergency areas can be sent to emergency management departments or related users as soon as possible. GFDM-1 satellite was launched in July 2020. It is China’s first optical agile remote sensing satellite with a resolution of 0.5 m, and it also has several variety working modes. At the same time, GFDM-1 satellite is the first civil satellite with relay data transmission function in China. The satellite will play an active role in emergency tasks. Its characteristics are as follows. 1) There is a priority of the task parameter in the GFDM-1 satellite. The satellite has the ability to schedule on-board, and high-priority tasks can replace low-priority tasks. Based on this feature, the priority of routine tasks are set to be the middle priority, and the priority of emergency tasks are set to be high priority, so that emergency tasks can replace the routine tasks.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 656–662, 2023. https://doi.org/10.1007/978-981-19-9968-0_79

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2) The agile imaging characteristics of GFDM-1 satellite, using multi-angle or stereo imaging we can observe the same position with different attitudes and obtain the observation data under different angles, which can largely avoid the influence of clouds. In the meantime, using multi-angle imaging mode can also continuously observe the same target within a few minutes, and obtain the dynamic changes in a short time. 3) The area extraction data process function of GFDM-1 satellite, which is, taking a specific position as the center, to extract the observation data of a certain size range (5 km × 5 km/10 km × 10 km/15 km × 15 km) for radiation and geometric correction on the satellite. After the data are transmitted to the ground, only the frame decoding is needed, then the data extracted from a separate virtual channel can be provided to relevant emergency users. 4) GFDM-1 satellite is the first civil satellite with relay data transmission function in China [4]. The data transmission opportunity has been greatly improved, and almost every orbit cycle can be transmitted through the relay satellite. Therefore, when the emergency task is unexpected and the ground receiving system cannot arrange the task, the data can also be transmitted back to the ground as soon as possible, which greatly improves the data transmission efficiency. In this paper, combined with the characteristics of the GFDM-1 satellite, the schedule process of the emergency tasks is designed, and the application in the observation of the emergency tasks is studied. Furthermore, the relevant characteristics of the satellite are tested and analyzed.

2 Emergency Task Schedule Process Design and Mode Selection In view of the strong burstiness and high timeliness of emergency tasks [2], the rolling optimization strategy is adopted [3, 4]. Different planning periods are divided by the telemetry and telecontrol time window and the data transmission time window, and the telemetry and telecontrol time window is prior considered in the planning periods dividing. At the same time, the dependence of the original task on the data transmission time window is considered. In the design of emergency scheduling process, the observation requirements in the original plan are considered to minimize the impact on the original task plan [5], so that the system can maximize the overall benefit of the task as far as possible when dealing with emergency tasks dynamically [6]. Combined with GFDM-1 satellite task priority setting, multi-angle imaging mode, on-board area extraction data fast processing mode and relay data transmission mode, the emergency task schedule process is designed. The emergency task scheduling process is as follows. 1) 2) 3)

Receiving emergency observation task requirements. According the choice of area extraction mode or multi-angle imaging mode, preliminary analysis is carried out to determine the shooting time of the satellite. Read the telemetry and telecontrol time plan, the current time after and the emergency task time before must have a time window to ensure that the telecommands

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can be upload [7]. If there is no telemetry and telecontrol time window, application is needed. Judging whether the task reset is needed according to the time of the upload windows(the task reset is not needed when the upload time is completed 200 s earlier than the start time of the task orbit where the emergency task is belonged); considering the reserve time of emergency task schedule, plan sending and telecommands compiling and sending, when the emergency task analysis is completed, it is judged that when the time between the start time of the task orbit and the uploaded time of the telecommands is longer than 600 s, the task reset is not needed, otherwise the task reset is needed. As task reset will carry out safely shutdown, delete the original task, and reupload the new task telecommands, it is required to include other non-emergency tasks as much as possible under meeting the requirements of emergency tasks. For the new emergency tasks schedule, the other tasks before the start time of the telecommands uploading orbit cannot be adjusted, and the tasks between the start time of the telecommands uploading orbit and the emergency task imaging time no need to be adjusted or can be adjusted as required. Choose the data transmission window based on whether the emergency tasks data needs to be downloaded as soon as possible [8]. If the emergency task data is no need to download as soon as possible, and there is also no conflict between the emergency imaging task and the other imaging task, adds the emergency task and the corresponding data transmission task directly with higher priority. If the emergency task data is no need to download as soon as possible, but the emergency task conflicts with the existing imaging tasks, because of the high priority telecommands will replace the low priority telecommands, high priority new emergency task and corresponding data transmission task telecommands will be uploaded. If the emergency task data needs to download as soon as possible, and there is also no conflict between the emergency imaging task and the other imaging tasks, judge whether the latest data transmission can be added after the emergency task. a) If the latest data transmission does not conflict, the emergency imaging task with high priority new task number and the corresponding data transmission task are directly added. b) If the data transmission conflict occurs, use the task reset function and send the emergency task and the data transmission task, or upload the emergency task telecommands with high priority and the data transmission task telecommands with high priority.

10) If the emergency task data needs to download as soon as possible, but the emergency task conflicts with the existing imaging tasks, judge whether the latest data transmission can be added after the emergency task. a) If the latest data transmission does not conflict, the emergency task with high priority and the corresponding data transmission task are directly added.

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b) If the data transmission conflict occurs, follow step9)b). 11) In this process, update the receiving plan and data processing plan.

3 Test Results and Analysis Combined with the emergency mission planning process in the first section and the related unique mode, the high-priority imaging task to replace the low-priority imaging task, the multi-angle imaging to avoid the influence of the cloud on the imaging area, the area extraction data processing, and the data transmission by the relay satellite was carried out during the GFDM-1 satellite on-orbit operation. 3.1 High Priority Tasks Replace Low Priority Tasks The emergency task is carried out according to the above emergency task schedule process, in this test only imaging tasks conflict, and use the function of high priority task replaces low priority task which has confliction. Figure 1 is the imaging area diagram of the original imaging task. Adjust the satellite position and task priority(high priority) to re-inject telecommands. The actual imaging area is shown in Fig. 2, which shows that the high priority task successfully replaces the low priority task. In this way, it is very convenient to replace operations when arranging emergency tasks that conflict with the original task, rather than the traditional way that you have to delete all the telecommands before sending them again, which greatly improves the timeliness of task execution.

Fig. 1. Original task imaging area sketch

Fig. 2. New task imaging area sketch

3.2 Effect of Avoiding Cloud Cover by Multi-angle Imaging The GFDM-1 satellite can image the same target with different satellite attitude through multi-angle imaging mode. As shown in Figs. 3, 4, 5 and 6, the amount of cloud in the data obtained from different angles is different. This feature can be fully used in the

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imaging of emergency tasks to avoid the influence of cloud and obtain effective data to the greatest extent. On the other hand, the multi-angle imaging mode allows the satellite to image the target as many as 12 times in about 3 min from the pitch angle of −45° to 45°. It can observe the target from multiple angles to obtain the full picture of the target as far as possible. In addition, the staring effect is also achieved within few minutes, and the dynamic change of the emergency target can be obtained.

Fig. 3. Image from Canton Tower north

Fig. 4. Image from Canton Tower south

Fig. 5. Image from OPR&T Tower south

Fig. 6. Image from OPR&T Tower north

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3.3 Area Extraction Data Processing On-Board to Accelerate Data Delivery to Users Area extraction data processing mode is to extract a certain size of the region and process directly on the satellite with a certain position as the center point from the acquired observation data. As shown in Fig. 7, the data has already passed radiation and geometric processing and can be used by emergency-related users. This provides a new idea for satellite remote sensing data delivery in emergency, that is, the data extracted and processed on-board are provided to users in advance for preliminary judgment of the emergency region. In this process, the ground data processing system carries out more advanced radiation correction processing and geometric correction processing, and then provides users for further analysis and judgment.

Fig. 7. Data of area extraction data processing on orbit

3.4 Data Transmission to Relay Satellites The GFDM-1 satellite is the first civil satellite with relay data transmission function in China. The data are transmitted from the GFDM-1 satellite to the relay satellite and then transmitted to the relay ground receiving station in real time, as shown in Fig. 8, and then transmitted synchronously to the ground processing system for processing.

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Fig. 8. GFDM-1 satellite data transmission to relay satellite sketch

4 Conclusion In this paper, a feasible emergency mission schedule process is designed based on the characteristics and unique working modes of the GFDM-1 satellite, and the process and working modes are tested. Through the on-orbit operation, the replacing low-priority with high-priority command is actually tested, which greatly simplifies the operation steps of satellite to perform emergency tasks and increases the success rate of emergency tasks execution. The multi-angle imaging mode successfully avoids the occlusion effect of the cloud to the observation area probable, and improves the acquisition rate of effective data. The on-board data processing mode of region extraction greatly shortens the time required to provide users with emergency observation data; using relay satellites for data transmission greatly shortens the time from emergency mission imaging to data return without affecting other imaging tasks in the same mission orbit.

References 1. Li, J.: Random access satellite emergency task response technology. Aerospace Electron. Warfare 3, 7–10 (2017) 2. Qiu, D.: Emergency scheduling method of earth observation satellites based on task merging. Syst. Eng. Electron. 35(7), 1430–1437 (2013) 3. He, C.: Emergency scheduling method for imaging reconnaissance satellites based on rolling horizon optimization strategy. Syst. Eng. Theory Pract. 33(10), 2685–2694 (2013) 4. Wu, X.: Satellite Data transmission mode decision and transmission resource allocation method oriented emergency observation. National University of Defense Technology, Changsha (2013) 5. Jin, P.: Two-phase emergency mission planning with satellite instruction release. Syst. Eng. Electron. 41(4), 810–818 (2019) 6. Wang, D.: Research on hierarchical task network based emergency response task planning method. Huazhong University of Science and Technology, Wuhan (2015) 7. Peng, S.: Multi-satellite collaborative planning and activity sequence adjusting for emergency observation missions. National University of Defense Technology, Changsha (2014) 8. Guo, C.: Mission planning of satellite emergency observations based on priority and time margin degree. Telecommun. Eng. 56(7), 744–749 (2016)

Emergency Scheduling Process Design for ZY303 Satellite Ziwei Li(B) , Mingliang Liu, and Hu Qiu China Centre for Resources Satellite Data and Application, Beijing, China [email protected]

Abstract. Different from common missions, emergency missions are characterized by abruptness and high timeliness. Rapid response is required but it has always been a difficulty in satellite mission scheduling. Combined with ZY3-03 satellite priority setting and mission reset design, different yaw angles and load combinations, this paper proposed a 3-step model and various scenarios of emergency task insertion to improve the efficiency of the urgent mission scheduling process. The satellite in-orbit verification shows that the proposed strategy can improve the efficiency of emergency mission scheduling and emergency data acquisition rate. Keywords: Satellite emergency mission scheduling · Satellite schedule strategy · Satellite task insertion

1 Introduction With the continuous development of satellite technology and user demand for remote sensing data, earth observing satellite (EOS) plays an increasingly important role in disasters such as war, earthquake, flood, volcanic eruption, forest fire, and so forth. Satellite imaging has become an important means of accessing information of ground surface. Emergency scheduling of EOS currently is a great challenge as well as research emphasis of satellite mission scheduling. Different from conventional mission planning, emergency mission planning needs to respond quickly to collection needs to its abruptness and high timeliness. At the same time, the emergency scheduling needs to complete the scheduling of tasks and data transmission before a particular deadline and upload the new instruction codes to the satellite in a control window. These requires an emergency task scheduling procedure with high efficiency. Civil imaging satellite emergency scheduling refers to the process of rapidly calling available imaging satellites to observe and image the specific areas and complete data transmission when an emergency outbursts, so the ground investigators can quickly obtain relevant information. ZY3-03 satellite was designed and produced by China Academy of Space Technology (CAST), China Aerospace Science and Technology Corporation (CASC). It carries three linear-array cameras, multi-spectral camera and laser altimeter, to obtain high resolution stereo images, panchromatic multispectral image and laser altimeter data. It © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 663–672, 2023. https://doi.org/10.1007/978-981-19-9968-0_80

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provides services in the fields of geographic national conditions monitoring, land and resources investigation, disaster prevention and mitigation. In this paper we describe how to schedule the emergency mission of single satellite, and establish the emergency mission scheduling model. The methods proposed in this paper mainly focus on ZY3-03 satellite.

2 Problem Formulation and Research Background It is a complex process to use the imaging satellite to complete the observation of a specific area. When the observation needs are determined, the control center carries out satellite task planning and resource scheduling, and upload the planned task sequence instructions into the satellite from the ground TT & C station within the time window visible to the satellite. The satellite runs in space with a certain orbit. On the ground, the sub-satellite point track is the axis and the imaging strip is the coverage to the ground target. After the observation is completed, the imaging data will be downloaded to the ground receiving station within the time window of ground receiving (see Fig. 1. Satellite Scheduling Process). Single satellite emergency mission scheduling can be described as the completion of task planning, command uploading, satellite imaging and imaged data downloading of emergency mission within the deadline. The whole process should consider the characteristics of satellite payload and satellite maneuvering, and the limited TT & C and receiving resources.

Fig. 1. Satellite scheduling process

The current research on satellite task scheduling is mainly oriented to the static scheduling algorithm of conventional tasks [1, 2]. For example, the optimal or nearoptimal task scheduling scheme is obtained through the genetic algorithm in reference [3], the improved ant colony algorithm designed in reference [4] and the improved tabu search algorithm and other optimization algorithms in reference [5]. This method does

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not consider the uncertainty factors in the process of satellite mission scheduling and mission execution, and has been unable to meet the high timeliness requirements of emergency missions. In order to solve the influence of uncertain factors on the satellite planning sequence, scholars have carried out a lot of research on the dynamic scheduling of imaging satellites [6]. Reference [7] established a multi-satellite dynamic scheduling model based on the minimum disturbance measure for the arrival of new tasks in the initial scheme, and proposed a heuristic algorithm to solve the model. Reference [8] proposed a heuristic algorithm based on two heuristic factors, namely, time window congestion and task overlap, to schedule dynamic tasks of different scales. Reference [4, 5] considered the impact of different disturbances on satellite task scheduling, and attributed the dynamic scheduling problem under different disturbances to a class of task insertion problems under complex constraints, which were solved by designing different heuristic algorithms. These articles have solved the influence of uncertain factors on the original mission planning sequence, but they have not considered the deadline requirements of emergency mission observation. Considering the expected completion time of the task, reference [3] proposed a multi-star heuristic scheduling algorithm based on the minimum resource competition degree on the basis of the number of resources, task requirements and environment state under uncertain conditions. Reference [1] proposed a multi-satellite emergency scheduling method based on dynamic synthesis, which synthesizes the tasks in the same field of view according to the task demand degree, and inserts the tasks that cannot be synthesized, without distinguishing the emergency tasks from the conventional tasks in detail. In addition, few studies have discussed the classification of emergency tasks, but the actual on-orbit operation of satellites will receive many simultaneous and conflicting requirements, so it is necessary to determine the priority. At this time, if there is an emergency demand, it is necessary to make the next judgment according to the priority level of the emergency. Therefore, this paper makes a distinction between the emergency task levels and the emergency task insertion scenario, which provides a basis for the subsequent algorithm research. In the past one or two years, domestic satellite engineers have also considered task reset in the early stage of design. At present, many satellites can complete task replacement as follow: tasks that have been uploaded but not executed on the satellite can be replaced by emergency tasks by setting file numbers or task start time. This could reduce the time for deleting tasks and shorten the completion cycle of emergency task scheduling. For particular, ZY3-03 satellite can complete task replacement and reset, including imaging tasks and data transmission tasks, by annotating the same file number. For the task priority, the strategy of ZY3-03 is designed as follows: for two tasks are with the same file number or are conflicted, with the same priority, the original tasks will be retained and the new tasks deleted; for different priorities, the tasks with higher priority will be retained and the ones with lower priority would be deleted.

3 Mission Scheduling Model and Task Insertion Strategy All strategies need to be customized according to the actual state and the applications of the target. During the on-orbit operation of ZY3-03 satellite, the main source of emergency tasks is disasters, so this paper considers the situation that the conventional

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tasks, shooting requirements and emergency tasks are simultaneously used as observation requirements in the emergency scheduling process, which is also the most common situation in the on-orbit operation of the satellite. Accordingly, different scheduling strategies are designed to reduce the impact on other demands on the premise that the emergency demand is met, so as to maximize the efficiency of the satellite. Considering that the result can be observed only after uploading the new mission to the satellite through TT & C station and after downloading data from the ground transmission station, the whole process of emergency mission scheduling includes three steps: emergency need analysis, task insertion and TT & C window selection. Emergency need analysis includes prioritize the emergency need, calculate visible windows, and tasks follow-up. The priority is according to the importance and urgency of the need, and the visible windows is calculated using the in-orbit parameters of the satellite. Because the emergency task requires rapid response, the strategy designed in this paper will judge whether there is an available TT & C window in advance in the need analysis node. If there is no available window, the task will be abandoned, and this action is also conducive to the rapid emergency scheduling of other satellites. The task insertion part includes the insertion of imaging tasks and data transmission tasks. See 2.2 Emergency Scheduling Strategy Design for the specific description. The TT & C window is the difficult to control and change in satellite operation. Therefore, the task scheduling process can only be carried out when the nearest available TT & C window lays before the task deadline. The model proposed in this paper mainly includes Need Analysis module (emergency priority defining, task planning, emergency task tracking), Task Insertion module, and Mission Uploading module. The three parts form a closed-loop, as shown in Fig. 2. Emergency Scheduling Model.

Fig. 2. Emergency scheduling model

3.1 Emergency Need Analysis Module This module includes priority level defining, task planning and task follow-up.

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Priority Levels. The priority can be divided into two levels according to the importance and urgency, as shown in Table 1. During planning process, the priority of emergency need shall be considered to adjust the mission sequence. In case of emergency demand with high priority, the principle is: observing and downloading as soon as possible; if the need is low priority, it should be imaged first while the downloading is not as urgent as the high priority ones. Table 1. Detailed descriptions of different priorities Priority Definition High

Highly targeted urgent tasks Not to consider the efficiency of satellite use, must image and transmit as soon as possible

Low

Temporary important requirements Should consider the efficiency of satellite and other collection needs. Imaging as soon as possible

Task Planning. In this section, we adopt one-click process to reduce manual operation time: from receiving the emergency need, to calculating the visible windows, to the analyzed result sending (to mission sequence), to the task insertion part. The whole process will be automated, no manual intervention required. Besides, there are different planning strategies for point targets and area targets. For needs with high priority, the analysis result will show the nearest observation for both point targets and area targets, neglecting the cover percent of area targets. But for needs with low priority, the results with largest coverage for area targets will be selected. At the same time, the task planning section also has the function of analyzing and applying TT & C resources. By calculating the time window of the analyzed need and the time window of TT & C, it can be judged whether the task can be send in time. When judging whether the task can be uploaded, the preorder time of the mission should also be taken into account. Task Tracking. The task tracking part is an important part of the closed-loop, it records the situations of the task for its plan, upload, observe, download and process. The nodes to be tracked mainly include the planning node, which shows which satellites have accepted the demand and how the task is executed; the planning node, which shows the specific imaging window, imaging angle, imaging load, etc.; the TT & C uploading node, in combination with the TT & C window, shows whether there are opportunities and conditions for task sending, and shows the upload times for the uploaded tasks; the receiving node displays the detailed receiving window and receiving resources of the ground station; the processing node displays the processing status.

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3.2 Emergency Scheduling Strategy Design Scheduling strategy determines the efficiency of mission planning, and a reasonable scheduling strategy is the key to emergency mission planning. This section will focus on strategies for conflict resolution during task insertion. Referring to LAS strategy and the rolling window method proposed in reference [9], at each emergency decision time Pu , which is the time earlier than the start time of TT & C window in the planning period, the judgment and decision should be made, and all the new arriving emergency tasks before each Pu time are adjusted in the original task planning sequence  to form a new planning one and then upload. The purpose is to minimize the disturbance of , and to complete the contingency of uncertain arrival of emergency tasks. Here define time period [λistart , λiend ] as the emergency task upload through ith TT & j j C window, and time period [θstart , θend ] as the execute time of jth task. Task scheduling at the time of scheduling Tk , classify the tasks in the original j sequence , here j ∈ . If θend ≤ Tk , the observation task is the finished task FT; j j if θstart < Tk < θend , the observation task is the executing task ET. At the same time, the tasks that have been uploaded to the satellite between Tk and the start time of nearest first j first TT & C window λstart will not be affected: if Tk ≤ θend ≤ λstart , the task is still ET; j

first

if θend > λstart , the task is the scheduled while unfinished one PT; if the task was not scheduled in the last period and listed in the sequence, the task is regarded as unscheduled one NT. Besides, if a task arrives between the last sequence and the new one, the task is defined as new arrival BT. See Fig. 3.

Fig. 3. Task status and scroll window update

The scroll window is to carry classified tasks as PT, NT and BT, and only the tasks in the scroll window are taken into consideration of task insertion, keeping other tasks in current sequence unchanged. In this paper, the impact of executing tasks on scheduling is not considered. In order to meet the requirements of earliest transmission of high-priority emergency tasks, in this paper, the start time of the nearest data transmission station is taken as the k , ωk ] as the time window for downloading the emergency task. Define time period [ωstart end j j k k data transmission time of station k. t = θ end − ωend , one the premise of ωstart > θend , the smaller t, the sooner the task will be completed. See Fig. 4.

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Fig. 4. Distance between task and transmission window

The following will discuss the strategy of inserting tasks in the rolling window and selecting the data transmission time window. It should be noted that all cases discussed j below are on the basis of Tk < θstart , i.e. the scheduling time is earlier than the start time of emergency task, no need to stop the currently executing task, considering that the function of stopping current task was designed protectively, so the process is time consuming. All possibilities for task insertion are listed below. No Conflict, Insert Task Directly. In this scenario, there is no existed observation task arranged in the emergency task time period, and the emergency task does not conflict with any existed task. The process is shown schematically in Fig. 5. The strategy of adding new tasks (including imaging and corresponding data transmission tasks) is adopted. The preferred transmission modes are ‘real-time imaging’ and ‘recording and playback meantime’. In order to avoid error replacement, it is necessary to ensure that the task number of the emergency task is not repeated with the existed tasks. Therefore, the task numbers of the conventional tasks are designed to be 0–250, and 251–255 are used for the emergency tasks. The number of the task to be inserted is set from 255 to 251 with reverse order. When sending the task, three parts should be involved: sending data transmission plan to the ground station, sending data processing plan to the ground processing system, and sending control commands to the satellite.

Fig. 5. No conflicted tasks, emergency task could be inserted directly

Tasks Conflicted, While the Existed Task Swath Covers the Emergency Target. In this scenario, the swath of existed task covers the emergency target, and the duration of current task contains emergency task. The process is shown schematically in Fig. 6. It is necessary to judge whether the corresponding data transmission window needs to be adjusted. In general, if the original task is real-time imaging or record and playback meantime, which is the most common situation (considering that the emergency task of the satellite is generally only required in China, and the in domestic area, these two modes are adopted with during on-orbit operation), there is no need to adjust the data transmission window; If the original task is a record and playback task, and there is

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an earlier transmission window before the existed playback task, the data transmission window needs to be adjusted. No Need to Adjust the Data Transmission Window. The existed task has covered the emergency target, and the corresponding data transmission window does not need to be adjusted. The orbit number of data transmission corresponding to the emergency target will be given to the ground station, and the receiving and processing tasks corresponding to the emergency target would be manually reminded to improve the priority. Need to Adjust the Data Transmission Window. If the existed task has covered the emergency demand, but the corresponding data transmission window needs to be adjusted, the nearest available transmission window will be determined according to the principle of transmitting the emergency task using the nearest transmission window, as mentioned before. Here are the steps to replace an existed task with an emergency task. Step 1: Set the priority of the new task to High (for regular tasks is Routine). Step 2: Determine the task number. Set the task number of the emergency task as the task number of the conflicted task. Step 3: Set the parameters. Step 4: Send the new plan to ground station, processing system and satellite.

Fig. 6. Emergency task conflicts with an existed task and the emergency task is covered (over both observation window and swath)

Tasks Conflicted, the Existed Task Swath Does Not Cover the Emergency Target. In this scenario, observing area of the existed task does not cover the emergency target area, or the loads of existed task does not include the load required to the emergency observation, but the time period is completely covered, and there is no need to adjust the data transmission window. Here a new imaging task should be inserted, and the steps are as follows: Step 1: Set the priority of the emergency task to High (for regular tasks is Routine). Step 2: Determine the task number. Set the task number of the emergency task as the task number of the conflicted task. Step 3: Set the angel and payload of the emergency task. Step 4: Send the new command to the satellite, manually remind the receiving station and processing system to improve the priority of the receiving and processing tasks corresponding to the emergency task (Fig. 7).

Fig. 7. Emergency task conflicts with an existed task and the emergency task is covered (over observation window only)

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Task Conflicted, the Existed Task Does Not Cover the Emergency Target in Both Swath and Time Period. In this scenario, the whole process is required to replace the routine task with the emergency task. See Fig. 8 for illustration. The task insertion part in the Emergency Need Analysis Module shall be carried out according to the following steps: Step 1: Set the priority of the emergency task to High (for regular tasks is Routine). Step 2: Determine the task number. Set the task number of the emergency task as the task number of the conflicted task. Step 3: Determine task duration. In general, the duration of the emergency task is shorter than that of the regular task, so the original data transmission window can be used to meet the download requirements. However, it is necessary to judge whether the current data transmission window can meet the principle of downloading as soon as possible. Step 4: Compare the difference between the emergency mission and the regular mission in their payload and angel. In general, the regular tasks are observed with all payload power-on, and the two transmission channels are used. This means the data download channel used by the regular task can cover the channel that may be selected by the emergency task. Then set the angel and payload of the emergency task. Step 5: Check whether the data transmission window needs to be modified due to the two points mentioned in Step 3 and Step 4. If modification is required, repeat step 1–2, raise the priority of the data transmission task for downloading emergency needs, and set the task number according to the data transmission task to be replaced. Step 6: Send the new plan to ground station, processing system and satellite.

Fig. 8. The emergency target is missed by the existed tasks

3.3 Mission Uploading Module In this module, automatic processing is designed for the emergency task, that is, the four steps of 1) receiving the plan XML, 2) compiling the instruction code, 3) selecting the upload window and 4) sending the command are combined into one, which shortens the time of manual operation. For the judgement of available TT & C window is designed in Need Analysis Module, and the satellite operation and control unit barely has authority for controlling TT & C windows, the function of this module is relatively simple and convenient.

4 Conclusion In this paper, a 3-part-model for scheduling temporarily received emergency tasks is put forward, and several situations that may be encountered in task insertion are analyzed. Different from the previous task rearrangement procedure of deleting sent tasks

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and resend the whole sequence, task replacement involves fewer tasks, requires fewer changes, and can reduce or even avoid repetitive work and notifications. Therefore, task replacement is a practical function especially for emergency missions, and the strategy proposed in this paper would be a reference for future emergency mission scheduling. Through the in-orbit operation, it is proved that this strategy can meet the intention of the task replacement design, improve the response speed of emergency tasks, and increase the effective emergency data acquisition rate.

References 1. Zhang, Z., Hu, F., et al.: Ant colony algorithm for satellite control resource scheduling problem. Applied Intelligence the International Journal of Artificial Intelligence Neural Networks & Complex Problem Solving Technologies (2018) 2. Jiang, W., Hao, H.C., Li, Y.J.: Review of task scheduling research for the Earth observing satellites. Syst. Eng. Electron. 35(9), 1878–1885 (2013) 3. Zhang, Z., Na, Z., Feng, Z.: Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst. Appl. 41(6), 2816–2823 (2014) 4. Han, S.M., Beak, S.W., Cho, K.R., et al.: Satellite mission scheduling using genetic algorithm. In: SICE Annual Conference. IEEE (2008) 5. Hao, H.C., Jiang, W., Li, Y.J.: Solving on agile satellites mission planning based on tabu searchparallel genetic algorithms. In: 2013 International Conference on Management Science and Engineering (ICMSE). IEEE (2013) 6. Zhiliang, L., Xiaojiang, L.: Current status and prospect of imaging satellite task dynamic scheduling methods. In: International Conference on Intelligent Human-machine Systems & Cybernetics, pp. 436–439. IEEE (2016) 7. Liu, Y., Chen, Y.W., Tan, Y.J.: A modeling and algorithm for the new tasks’ arriving in multisatellites dynamic scheduling. Systems Engineering-theory & Practice (2005) 8. Wang, M., Dai, G., Vasile, M.: Heuristic scheduling algorithm oriented dynamic tasks for imaging satellites. Math. Probl. Eng. 2014(5), 1–11 (2014) 9. Jin, P., Wang, C., Xia, W., et al.: Two-phase emergency mission planning with satellite instruction release. Syst. Eng. Electron. 41, 810–818 (2019)

Research on the Application of TDRSS for Manned Spacecraft Da Tang(B) , Tang Li, Peng Wan, Zhisheng Wang, and Yushu Feng Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China [email protected]

Abstract. Manned spacecraft has diverse needs such as high reliability, multitarget TT&C, image and voice communication. Compared with ground-based TT&C, TDRSS with advantages such as high TT&C coverage, high data transfer rate, diverse link, and outstanding economic benefits has become the main means for spacecraft TT&C and communication. In this paper, the characteristics of manned spacecraft TT&C and TDRSS are summarized, and the applications of TDRSS for manned spacecraft are studied from three aspects including multi-garget TT&C, key event support, capability improvement and optimization. Specifically, a TT&C method with two TDRSS following is proposed to provide dual target continuous tracking in match docking, and a TT&C access method combining single-site and multi-site is proposed to increase the TT&C coverage by 64% based on a single star-to-star antenna. Furthermore, two feasibility studies are conducted, one is to achieve 100% TT&C coverage by adjusting the installation direction of terminal antenna, the other is to replace parabolic antenna with phased array antenna. Keywords: Tracking and data relay satellite system (TDRSS) · Manned spacecraft · TT&C and communication · TT&C coverage rate

1 Introduction As the lifeline of spacecraft, TT&C link is the only means of data exchange between spacecraft and underground after spacecraft entering the orbit. Comprehensive support such as measurement, remote control, telemetry, image, voice and load test are provided through the construction of TT&C network and TDRSS in the past. In particular, the construction and application of TDRSS has expanded the space-based TT&C method for spacecraft, which improved the flexibility of flight control greatly. In view of the unique advantages of space-based TT&C, TDRSS will play more and more important role in manned spacecraft TT&C. The remainder of this paper is organized as follows. The characteristics of manned spacecraft TT&C and TDRSS are summarized in Sect. 2 and Sect. 3, Sect. 4 is devoted to study the applications of TDRSS for manned spacecraft from aspects including multigarget TT&C, key event support, capability improvement and optimization. Finally, there is a conclusion in Sect. 5. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 673–680, 2023. https://doi.org/10.1007/978-981-19-9968-0_81

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2 Characteristics of Manned Spacecraft TT&C 2.1 High Reliability Manned space missions include various stages such as launch into orbit, rendezvous and docking (RD), long-term operation, separation and evacuation, return or de-orbit, etc. Massive key events, complex technical status, and intensive collaboration will occur during above stages, and high reliability is required to make things right. To achieve this, whether mission preparation like link configuration and scheme design, or mission implementation like plan arrangement, spacecraft control, strict quality control and precise operation are needed. 2.2 Multi-target TT&C The space station is regularly visited by manned spacecraft and cargo spacecraft during its orbital operation. In the process of RD, long-term operation, separation and evacuation, dual target parallel control is needed when manned or cargo spacecraft and the space station locate in the same antenna beam coverage of the ground station or TDRSS. Furthermore, when necessary, ground stations and TDRSS can also be coordinated to achieve multi-objective parallel control, which means better electromagnetic compatibility and multiple access adaptability. 2.3 Image and Voice Communication Compared with traditional satellite missions, the biggest feature of manned space mission is that the astronauts are on-orbit for a long time, which leads to the demand for image and voice communication. Voices increase the effectiveness of communication between astronauts and the ground. Relevant status can be transmitted to the ground in time with voices when spacecraft is abnormal or astronauts are in emergency. Images provide a good display interface for the project, the down-link images can be used to monitor the on-orbit status of spacecraft and astronauts, and the up-link images transmit the ground video sources to space, which can reduce astronauts’ sense of isolation effectively.

3 Characteristics of TDRSS 3.1 High TT&C Coverage TDRSS locates in the geosynchronous orbit, has the advantages of wide coverage of beams, which can provide a much higher TT&C coverage than that of a single ground station or a combination of multiple ground stations. The TT&C coverage of LEO satellite can reach 85% based on three TDRSS, compared with about 15% based on ground stations [1]. One step further, 100% coverage can be achieved by adding spacecraft relay antennas or oblique installations [2], which can expand information transmission capabilities and rapid response capabilities greatly, and improve the safety and reliability of manned spaceflight TT&C effectively.

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3.2 High Data Transfer Rate TDRSS supports Ka-band data transmission with multiple transmission channels, wide transfer bandwidth, and long continuous transmission, which can meet the requirements of spacecraft platform and payload data, HD images and high quality voices download well. Furthermore, control data, images and voices can be uploaded with a high speed by establishing an uplink channel. 3.3 Diverse Link S-band single access (SSA) links, Ka-band single access (KSA) links [3] and S-band multiple access (SMA) links [4, 5] can be established between TDRSS and spacecraft. Links mentioned above can work simultaneously. Among them, wide-beam SSA (WSSA) link can adapt to the large attitude or rapid attitude change of spacecraft [6], narrow-beam SSA (NSSA) link can be used for measurement [3], and KSA link can be used for highspeed transmission. SMA link can be established between spacecraft and 2nd generation TDRSS, which can provide multi-target TT&C by adopting a phased array antenna. 3.4 Outstanding Economic Benefits For now, TDRSS has achieved 100% orbital coverage of LEO satellites, and this goal can only be achieved by deploying a large number of ground stations. An astronomical cost need to be paid to build and maintain these ground stations without considering the constrained conditions like geographical environment (ocean, desert, polar, etc.) and political factors. With the iterative update of TDRSS, its capabilities in TT&C and communication are continuously improving, which can bring outstanding economic benefits [7].

4 Applications of TDRSS 4.1 Multi-target TT&C TDRSS can perform multiple spacecraft parallel TT&C within the same inter-satellite antenna coverage by means of frequency and spread spectrum code division, which can provide strong support for RD, spacecraft separation and evacuation. Taking dual-target TT&C as an example, TDRSS antenna mainly follows one of the targets, and realizes the other target’s TT&C by utilizing the characteristics of signal attenuation in the halfpower beam range is less than 3 dB. During above period, a single inter-satellite antenna can provide both SSA and KSA link for dual targets at the same time [3]. In view of the anti-interference ability of CDMA and the directivity of the user terminal antenna, relay TT&C of adjacent TDRSS for dual targets can be realized with the comprehensive use of TDRSS links, which improves the continuous TT&C time greatly. The 2nd generation TDRSS has two inter-satellite antennas, while ensuring manned space missions, it can also take into account other satellite TT&C and communications. When necessary, both two antennas can support for manned spacecraft, and the comprehensive benefits of space-based TT&C have been further improved (Fig. 1).

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Fig. 1. Schematic diagram of dual target relay TT&C

4.2 Key Event Support Spacecraft Reach the Orbit. As launch vehicles and spacecraft equipped with relay terminals, space-based TT&C has gradually become the main TT&C method in the late stage of the rocket flight and early stage of the spacecraft into space. Take the Tianzhou cargo spacecraft launch by Long March 7 rocket as an example, the rocket flies away from the land quickly after it took off, and the ground stations cannot cover the entire rocket flight, which means the monitoring and abnormal handling of key processes such as spacecraft separation, initial attitude establishment, solar panels unfold rely on TDRSS only. With eastern node (EN) and middle node (WN) TDRSS, continuous TT&C can be realized since rocket launch to early orbital stage of the spacecraft. Rendezvous and Docking. Since manned and cargo spacecraft adopt autonomous and rapid RD, the RD time has been greatly shortened from nearly 40 h in the space laboratory stage to about 6.5 h [8]. During this period, there is no ground station coverage for over 95% of the time, and TDRSS is used to monitor spacecraft’s orbit change process and send control data. In previous missions, the final docking was completed under the common TT&C coverage of ground stations and TDRSS. Nowadays, ground station is no longer necessary for the final docking, and continuous TT&C for spacecraft and space station can be realized by using a single or two adjacent TDRSS. During the dual-TDRSS relay, the spacecraft and space station establish a SSA link and a KSA link with pre-order TDRSS respectively, and then establish a WSSA link and a KSA link with post-order TDRSS respectively. In the overlapping coverage area of two TDRSS, time-sharing switching between SSA and KSA link is realized by using the anti-multiple access of SSA link and the high pointing accuracy of KSA link. With this method, at least one TT&C link of each of the two spacecraft is in working condition, which takes into account the attitude disturbance caused by the contact of the docking mechanism, real-time image and voice transmission, and the emergency control of the spacecraft (Fig. 2). Long-term Operation. TDRSS is mainly based during long-term operation, and TT&C resources can be applied for as needed according to the importance of on-orbit event. For example, three TRDSS are needed during astronauts emerged from the capsule, and one or two TDRSS are used for status monitoring and load data transmission.

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Fig. 2. TT&C and communication with two TDRSS during RD

The 2nd TDRSS support multiple access means [4, 9]. By using phased array antenna to control beam pointing, it can work in parallel with single-access based on parabolic antennas. Despite the link performance is degraded due to the difference in antenna capability, it can still be used as an effective means of spacecraft monitoring because of parallel access without occupying inter-satellite antennas. Combining single access and multiple access by adding SMA link with other TDRSS on the basis of KSA link, spacecraft status monitoring and payload data transmission can be taken into account effectively (See Table 1). Without adding inter-satellite antennas, TT&C coverage can be increased by 64%, which greatly improves the comprehensive application benefits of TDRSS (Tables 2 and 3). Table 1. Link combination TT&C coverage. Link combination

KSA coverage

Combined coverage

Number of inter-satellite antenna

Coverage improvement

KSA(EN) + SMA(MN)

44% [3]

61%

1

38%

KSA(MN) + SMA(EN 44% and WN)

72%

1

64%

KSA(EN and WN) + SMA(MN)

90%

2

6%

85% [1]

Return or De-orbit. As the orbital height of the manned spacecraft continues to decrease, the visible range of the ground station is gradually reduced, and the deployment of foreign ground stations is limited. Before the manned spacecraft enters the country, it is mainly based on TDRSS, which can cover the return process. Cargo spacecraft de-orbits through two braking. In the early design, the two braking processes were arranged in the domestic continuous TT&C area. The interval between the

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two braking was long, and the entire de-orbit process took more than one day. Following the in-depth application of TDRSS, the TT&C constraints for cargo spacecraft de-orbit have been gradually optimized, and the braking process can be implemented in the overseas area, which greatly reduces the time required for de-orbit.

4.3 Capability Improvement and Optimization TT&C Coverage. Although China’s TDRSS can achieve 100% geometric coverage of LEO, it is still unable to achieve 100% TT&C coverage due to the limitations of the spacecraft attitude and the rotation range of the relay antenna. The feasible methods for improving the TT&C coverage include: 1) increase the rotation angle of the relay antenna in the pitch direction; 2) increase the slanted relay terminal antenna; 3) increase the longitude difference between EN and western node (WN) of TDRSS. Methods above can improve the coverage by improving the visible time of the spacecraft’s relay terminal antenna and TDRSS essentially. For a parabolic antenna, it is difficult to realize the pitch direction greater than 90° in engineering. This paper focuses on the feasibility of the latter two methods. Take the space station as an example, the basic configuration of the space station contains three cabin sections, and relay antennas are deployed in each cabin section towards different directions [2], which can back up each other, and can further improve the TT&C coverage through combined use. Simulation analysis shows that when the antenna of the core cabin is installed upright and the antenna of the experimental cabin is installed at an angle of 10°, TT&C coverage rate can reach 95%. Furthermore, TT&C coverage can reach 100% when the inclined angle is increased to 22°. Table 2. TT&C coverage of different antenna inclined angle. Inclined angle of antenna

TT&C Coverage

0°

90%

10°

95%

20°

99%

22°

100%

The maximum longitude difference between EN and WN of TDRSS is about 166° [4]. Better link visibility can be achieved by moving TDRSS node to make the distribution of TDRSS more uniform. Simulation analysis shows that for a single terminal antenna, after moving the EN by 15°, TT&C coverage is increased by about 5% with three TDRSS. In the above example, TT&C coverage can reach 100% when the inclined angle is 12° (Fig. 3). Phased Array Antenna. The parabolic antenna has the advantages of high transmission rate and large rotation range, but the cost is high and the speed of rotation is slow;

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Fig. 3. Schematic diagram of TT&C coverage when the relay antenna is installed obliquely and the EN is moved eastward Table 3. TT&C coverage of phased array antenna. Beam width

Single TDRSS

Three TDRSS

60°

24%

65%

65°

28%

70%

70°

31%

74%

75°

34%

78%

80°

37%

81%

Phased array antenna’s beam coverage is smaller than that of parabolic antenna, but it has the advantages of low cost and fast beam pointing switching. In addition, since the phased array antenna has no rotating mechanism, it has higher reliability than parabolic antenna. For spacecraft such as manned or cargo spacecraft, which will be docked at the space station for a long time after RD, the independent flight time is short, and the communication rate requirements are not high, phased array antennas can be considered instead of parabolic antennas. Analysis shows that when the beam coverage of the phased array antenna reaches 70°, the TT&C coverage with three TDRSS can reach about 75%; when the beam coverage reaches 80%, the TT&C coverage is very close to the level of the parabolic antenna.

5 Conclusion TDRSS has the characteristics of high TT&C coverage, high data transfer rate, diverse link, and outstanding economic benefits, which can meet the diverse needs of manned spacecraft. With the construction and long-term operation of the space station, TDRSS will play an increasingly important role, especially in response to the massive data transmission requirements put forward by the surver space telescope [10], it is necessary to explore applications such as inter-satellite laser communication, etc.

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References 1. Yuheng, L., Haizhong, S., Jun, Z., et al.: Using tracking and data relay satellite for low and middle earth orbit satellite launch and on-orbit control. Chin. J. Space Sci. 35(5), 611–617 (2015) 2. Yusheng, Y., Yi, X., Lu, Z., et al.: Method to improve TT&C coverage rate of relay antenna for Chinese space station. Spacecraft Eng. 23(2), 7–11 (2014) 3. Zhonggui, W.: Design of a space-based dual-object TT&C system for China’s first rendezvous and docking mission. J. Spacecraft TT&C Technol. 31(3), 1–5 (2012) 4. Zhengrui, C., Baosheng, S., Yanjun, Y., et al.: TDRS SMA for spacecraft on-orbit control. J. Astronaut. 41(11), 1434–1429 (2020) 5. Baosheng, S., Zhengrui, C., Hua, D.: A concept of satellites’ health management based on TDRS SMA. Trans. Beijing Inst. Technol. 39(11), 1203–1206 (2019) 6. Zhonggui, W., Peng, W., Ding, S., et al.: Research on the wide-band data relay technology of the TT&C support for the space station missions. J. Spacecraft TT&C Technol. 34(2), 140–146 (2015) 7. Jiasheng, W., Xin, Q.: China’s data relay satellite system served for manned spaceflight. Sci Sin Tech 44(3), 235–242 (2014) 8. Mingsheng, B., Yong, J., Jianyu, L., et al.: Research and development of Tianzhou-1 cargo spacecraft. Manned Spaceflight 25(2), 249–255 (2019) 9. Changsheng, S., Yuheng, L., Haizhou, S., et al.: Tracking and data relay satellite system for huge number satellite control. Chinese Space Sci. Technol. 37(1), 89–96 (2017) 10. Hu, Z.: The wide-field multiband imaging and slitless spectroscopy survey to be carried out by the surver space telescope of China manned space program. Chin. Sci. Bull. 66(11), 1290–1298 (2021)

Modeling and Analysis of Satellite with a Large Inertia Rotating Load Guiming Li1 , Shixuan Liu2(B) , Zhihui Li1 , Xinyan Wu2 , Kui Zou1 , and Rui Liu1 1 Beijing Institute of Control Engineering (BICE), Beijing 100190, China 2 Harbin Institute of Technology, Harbin 150080, China

[email protected]

Abstract. With the development of space technology, satellites with large inertia rotating loads have been paid more and more attention. At the same time, the payload of this kind of satellite has the characteristics of large inertia, three-axis coupling and large mass, which make the analysis of dynamic or static imbalance very difficult. In this paper, based on SimMechanics, a satellite dynamics model with a large inertia rotating load is established, and the influence of the dynamic unbalance, static unbalance and disturbance torque of the load platform on the satellite platform is analyzed by simulation, the mechanism and characteristics of the load unbalance are studied. The law of the influence of the unbalance of the load platform on the attitude characteristics of the satellite platform is obtained. The motion law of the center of mass of the satellite and load platform is investigated. It lays the foundation for further in-depth research later. Keywords: Large inertia rotating load · SimMechanics · Modeling · Imbalance

1 Introduction With the progress of aerospace technology, the types of spacecraft are becoming more and more diverse for the needs of various space missions. Satellites with payload are an important branch of many types of spacecraft, as shown in Fig. 1 [1–3]. They can achieve specific functions through their own active loads, such as cameras, scanning radiometers, and large windsurfing panels for atmospheric vertical detectors. This satellite has a wide range of applications. The Haiyang 2 satellite launched from the Taiyuan Satellite Launch Center in 2011 carried a microwave radiometer and a microwave scatterometer, and its payload platform mass was one-tenth of the total mass of the satellite [4–6]. The Global Precipitation Measurement Satellite (GMS) jointly developed by the United States and Japan also carried the Global Microwave Imager, and its payload needs to operate at a constant speed for three years while in orbit [7–9]. In recent years, as requirements for spacecraft missions and technical indicators have increased significantly, satellites with large inertia rotating payload have been paid more and more attention [10–12]. The payload of this kind of satellite has the characteristics of large moment of inertia, dynamic three-axis coupling, and large mass, which makes the overall motion attitude of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 681–688, 2023. https://doi.org/10.1007/978-981-19-9968-0_82

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the satellite very sensitive to the dynamic and static imbalance of the rotating load [13– 15]. When the large inertia rotating load is working, since its own inertial force cannot be cancelled, the force and torque generated by the rotation will act on the satellite platform and affect the attitude of the satellite. In order to offset this part of the impact, the satellite must constantly adjust its attitude, which will increase the burden on the satellite’s actuators and affect the satellite’s on-orbit life.

Fig. 1. Satellite with payload platform

At present, the research on satellites mainly focuses on small inertia, high-speed systems such as reaction flywheels and gyro flywheels. In contrast, less research has been done on satellites with large inertia rotating loads. Therefore, it is very important to model the satellite with a large inertia rotating load, to analyze the influence of the load dynamic and static unbalance, interference force, and interference torque on the satellite, and to verify the three-axis coupling of system dynamics and the coupling characteristics of the load and the satellite. This paper is organized as follows. Model establishment and simulation analysis are respectively in Sect. 2 and Sect. 3. Conclusion is drawn in Sect. 4.

2 Model Establishment Using SimMechanics Satellite Dynamics Model with the large inertia rotating payload is established by SimMechanics in this article. The model can be simplified as a system consisting of satellite platform, shaft and load platform. The satellite platform and the load platform are rigidly connected through shaft. The load platform can rotate with one degree of freedom along the axis according to the input angular velocity. According to the structure simplification, three coordinate frames as portrayed in Fig. 2 are defined in this paper as follows: Fb (Oxb yb zb ) - Satellite platform centroid coordinate system; Fc (Oxc yc zc ) - Load platform centroid coordinate system; Fs (Oxs ys zs ) - Satellite overall centroid coordinate system. SimMechanics is a multi-body dynamics simulation platform. It can be used to model quickly rigid and flexible systems, and can also be used for joint analysis with simulation systems. It provides plenty of practical components, such as basic blocks, packaged mature systems and links with constraints. The joints with different degrees of freedom and basic blocks are combined into the desired model. In addition, the attitude information of the established model can also be observed and analyzed through the measurement module in the software.

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Fig. 2. Coordinate frames

Satellite Dynamics Model with the large inertia rotating load consists of two parts: satellite platform and load platform. The satellite model with large inertia rotating load based on SimMechanics is shown in Fig. 3. In the modeling process, both the load platform and satellite platform are designed to have one degree of freedom, while the overall satellite is designed to have six degrees of freedom. We consider that both the angles between the rotation axis and the load platform and between the rotation axis and the satellite platform are non-ideal, that is, not right angles.

Fig. 3. Satellite model with large inertia rotating load based on SimMechanics

3 Simulation Analysis 3.1 Simulation Setup Under reasonable assumptions, the model parameters are listed in Table 1. 3.2 Results and Analysis In this paper, the satellite with a large inertia is modeled and analyzed, and the influence of the load platform static unbalance and dynamic imbalance is mainly analyzed. Assuming that the satellite is not subjected to the external forces. Conclusion 1: The overall center of mass of the satellite is in a stationary state. According to the Newton’s first law and system input conditions, the satellite should remain stationary which can also be proved in the simulation analysis, so as to further

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Parameters

Numerical value

Payload platform mass

1595 kg

Satellite platform mass

1666.2 kg ⎡ ⎤ 1750 1.5 1.5 ⎢ ⎥ ⎢ 1.5 2474.4 −42.7 ⎥ kg · m2 ⎣ ⎦ 1.5 −42.7 2088.6

Payload platform inertia

⎡ Satellite platform inertia

⎢ ⎢ ⎣

⎤ 1251.9 20 8.3

20

8.3

⎥ 2 1134.6 −70 ⎥ ⎦ kg · m −70 1174.5

Load platform centroid offset distance

0.0027 m

Load platform centroid offset distance

0.0012 m

The centroid coordinates of the load blocks in the overall centroid coordinate system

[1.1715, 0, 0] m

The centroid coordinates of the satellite blocks in the overall centroid coordinate system

[−1.1214, 0, 0] m

Initial angular velocity of satellite platform

[0, 0, 0] T º /s

Initial attitude angle of satellite platform

[0, 0, 0] T º

Working angular velocity of load platform

−16 º /s

Angle between load platform and axis of rotation

[−2, 0, 0] º

Angle between satellite platform and axis of rotation

[−2, 0, 0] º

study the motion characteristics of the center of mass of the payload and satellite platforms. In the inertial coordinate system, the motion characteristics of the load platform centroid and the satellite platform centroid can be obtained, as shown in Fig. 4 and Fig. 5, respectively. Conclusion 2: The load and satellite platforms are swaying around the point of the overall center of mass. Because the influence of dynamic and static imbalance, the x, y and z-axis coordinates of the center of mass of the payload and satellite platforms will fluctuate as shown in Fig. 4 and Fig. 5, and the fluctuations change in the opposite direction, which proves that the load and satellite platforms are swaying around the point of the overall center of mass. Apply static imbalance, dynamic unbalance and dynamic and static unbalance to the system respectively. The model parameters are shown in Table 1. The motion characteristics of the load platform and the satellite platform are analyzed, and the force and torque applied by the unbalance of the load platform to the satellite platform are

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Fig. 4. Movement of the center of mass of the satellite and load platform

Fig. 5. Movement of the center of mass of the satellite and load platform on the x-axis

obtained. Based on the above conditions, the angular velocity of the load platform in the inertial coordinate system is shown in Fig. 6. The first to third columns in the figure are the angular velocity curves of the load platform under the conditions of static unbalance, dynamic unbalance and dynamic and static unbalance, respectively, and the first to third rows are the angular velocity curves of the x, y and z axes respectively. Based on the above conditions, the angular velocity of the load platform in the satellite platform’s center of mass coordinate system is shown in Fig. 7. Conclusion 3: There are two periods in the angular velocity curve of the satellite platform. Because the two periods are related to the state of the system. FFT analysis of the curve can obtain the large period as 120 s, and the small period as 22.5 s. The small period

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Fig. 6. Angular velocity of load platform

Fig. 7. Angular velocity of satellite platform

is related to the rotation speed of the payload platform, and the large period is related to the overall rotation tensor of the satellite which will be verified in the mathematical formula. Conclusion 4: The attitude movements of the load platform and the satellite platform are related to each other. Because the rigid connection of the load and satellite platforms will cause the unbalance of the load platform to affect the movement of the satellite platform. The unbalance of the load platform will generate unbalanced forces and torque on the satellite platform. Based on the above conditions, the unbalanced forces and torques are shown in Fig. 8 and Fig. 9, respectively.

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Fig. 8. Unbalance force

Fig. 9. Unbalance torque

Conclusion 5: The unbalanced force and unbalanced torque provide the motion of the satellite platform’s center of mass and the attitude angular velocity characteristics of the satellite platform. Static unbalance has a greater impact on the satellite platform than dynamic unbalance. Because static unbalance will bring about the effect of disturbing force, while dynamic unbalance will bring about the effect of disturbing torque. Unbalanced forces and torques lead to the complex translational and rotational characteristics of the satellite platform, respectively. The unbalance torque when there is only static unbalance and the unbalance force when there is only dynamic unbalance are generated by the motion of the load platform.

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4 Conclusion In this paper, based on SimMechanics, a satellite model with a large inertia rotating load is established. Due to the dynamic and static imbalance of the load platform, load and satellite platforms will sway around the point of the overall center of mass. Unbalanced force and torque will be generated by the dynamic and static unbalance of the load platform to act on the satellite platform, which will affect the attitude of the satellite platform. Additional momentum wheels are required to control the attitude of the satellite platform.

References 1. Cheon, D.I., Choi, D., Jang, E., et al.: Disturbance reduction on the small satellite actuator. In: Proceedings of 2011 2nd International Conference on Instrumentation Control and Automation, pp. 31–34., Bandung, Indonesia (2011) 2. Lau, J., Joshi, S.S., Agrawal, B.N., et al.: Investigation of periodic-disturbance identification and rejection in spacecraft. J. Guid. 29(4), 792–798 (2006) 3. Masterson, R.A., Miller, D.W., Grogan, R.L.: Development and validation of reaction wheel disturbance models: empirical model. J. Sound Vib. 249(3), 575–598 (2002) 4. Kim, D.K.: Micro-vibration model and parameter estimation method of a reaction wheel assembly. J. Sound Vibrat. 333(18), 4214–4231 (2014) 5. Bishop, R.E.D.: The vibration of rotating shafts. J. Mech. Eng. Sci. 1(1), 50–65 (1959) 6. Foiles, W.C., Allaire, P.E., Gunter, E.J.: Review: rotor balancing. Shock Vibrat. 5(5–6), 325– 336 (1998) 7. Saito, S., Azuma, T.: Balancing of flexible rotors by the complex modal method. J. Vib. Acoust. 105(1), 94–100 (1983) 8. Goodman, T.P.: A Least-squares method for computing balance corrections. J. Manufac. Sci. Eng. 86(3), 273–277 (1964) 9. Parkinson, A.G.: The vibration and balancing of shafts rotating in asymmetric bearings. J. Sound Vibration 2(4), 477–501 (1965) 10. Han, D.J.: Generalized modal balancing for non-isotropic rotor systems. Mech. Syst. Sig. Process. 21(5), 2137–2160 (2007) 11. Liao, Y., Zhang, P.: Unbalance related rotor precession behavior analysis and modification to the holobalancing method. Mech. Mach. Theory 45(4), 601–610 (2010) 12. Bukley, A.P.: Hubble Space Telescope pointing control system design improvement study results. J. Guid. Control Dyn. 18(2), 194–199 (2012) 13. Masterson, R.A., Miller, D.W., Grogan, R.L.: Development and validation of reaction wheel disturbance models: empirical model. J. Sound Vibrat. 249(3), 575–598 (2002) 14. Hara, S., Yamamoto, Y., Omata, T., et al.: Repetitive control system: a new type servo system for periodic exogenous signals. IEEE Trans. Autom. Control IEEE Trans 33(7), 659–668 (1988) 15. Guvenc, L.: Repetitive controller design in parameter space. In: American Control Conference, pp. 2749–2754. Arlington, VA, USA (2001)

Research on Satellite-to-Ground Integration Joint Mission Planning System for Mega-constellation Multi-user Remote Sensing Information Resource Guarantee Shaoyu Zhang, Xu Yang, Ruolan Zhang(B) , Keqiang Xia, Xiaobo Hui, Haipeng Liu, and Xing Wang State Key Laboratory of Astronautic Dynamics, Xi’an 710043, China [email protected]

Abstract. In order to meet the growing demands for earth remote sensing observation of the commercial space companies, promote the ability of rapid access to remote sensing product, further optimize resources using process and maximize the effect of satellite-to-ground resources usage process and integration joint guarantee, this paper based on the quick access to remote sensing products, satisfies the requirements of multi-user remote sensing information resources guarantee, by the means of enhancing overall usage effectiveness of the space-based and ground-based TT&C and operation control. This paper first sorts through the current status and problems of multi-user observation mission, then puts forward the satellite-to-ground integration joint mission planning system and designs the system components and the functional requirements and finally provides the process of joint mission planning system and presents the system operating scenarios. Keywords: Mega-constellation · Earth remote sensing observation · Joint planning · Satellite-to-ground integration · Overall scheme

1 Introduction In order to meet the growing demands for earth remote sensing observation of the commercial space companies, promote the ability of rapid access to remote sensing product, further optimize resources using process, this paper focuses on maximizing the effect of satellite-to-ground resources integration joint guarantee. In the current traditional mode, there exists deficiencies in the implementation process of earth remote sensing observation task, such as difficulty in overall arrangement and usage of resources, resources coordination of space-based and ground-based TT&C and data transmission and low efficiency of system operation. This paper proposes a satellite- to-ground integration joint mission planning system for multi-user remote sensing information resource guarantee, adopting modular design. The design includes five subsystems of demand summary and scheduling, mission planning, space-based and ground-based resource dispatch management, spacecraft operation management and space-based data distribution management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 689–695, 2023. https://doi.org/10.1007/978-981-19-9968-0_83

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Also, the system operation scenario of daily acceptance and analysis against earth remote sensing observation, satellite resource analysis, integrated dispatching of space-based and ground-based TT&C and data transmission resources and unified task preparation and planning are described.

2 Present Situation and Deficiencies of Earth Remote Sensing Observation Task 2.1 Planning Status In the current task mode, the implementation process of earth remote sensing observation tasks is as follows: users such as the remote sensing image ones put forward product requirements, the satellite operation control center receives the requirements and utilizes its own satellite resources to carry out task planning, formulates payload control plans and generates payload instructions to send to the planning center. And then the planning center carries out satellite management and control planning and ground-based resources guarantee planning according to the payload type. Combined with the requirements of satellite telecommand transmission, it completes the instruction release by using the subordinate stations or applying relay satellites. The stations and relay satellites receive data from satellite data transmission stick to the schedule and send them to the corresponding operation control center. Next the center obtains the corresponding product information by processing the satellite payload data and sends it to the user. The specific process is shown in the figure below (Fig. 1).

Fig. 1. Mission plan of each system and interactive relationship among systems of earth remote sensing observation mission

It can be seen from the figure above that the functions of satellite operation control center and planning center are as follows: Each satellite operation control center receives the requirements and utilizes its own satellite resources to carry out task planning. Then according to its special ground data transmission receiving equipment usage, clear digital receiving scheme (if the ground receiving equipment cannot satisfy the task, the center can directly apply to the relay satellite). On this basis, control center makes TT&C week plan and payload control plan which generates payload instructions to send to the planning center. The planning center conducts satellite management and control planning and groundbased resource guarantee planning according to the tasks, including satellite control, ground-based resource application acceptance, conflict resolution and space-based

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resource application. Combined with the requirements of satellite instruction release, the affiliated stations or relay satellites are used to complete the instruction release of platform and payload instructions. Data transmission receiving stations and relay satellites receive satellite data according to the schedule and transmit it to the corresponding operation control center. 2.2 Deficiencies By analyzing and sorting out the implementation process of earth remote sensing observation tasks, the current earth remote sensing observation tasks mainly have the following shortcomings: Hard to Use Remote Sensing Resources as a Whole. The multi-satellite remote sensing resources belongs to different users. Multi-satellite mission planning belongs to various operation and control centers, so remote sensing resources cannot be shared. Joint task planning is at low level. During the early stages the joint task planning of multi-satellite remote sensing information only realized the summary of various requirements. Difficult to Schedule and Control the Data Transmission Resources of Space-Based and Ground-Based TT&C. The management and control of space-based and groundbased resources are not unified. The resources belong to different commercial companies, so that it is hard to be coordinated. The overall planning and scheduling of space-based and ground-based resources are not unified. When using the space-based resources, users should apply to the communication center, while using the ground-based resources need coordinate with commercial companies. Low Efficiency of the System Operation. The level of system automation is low. The earth remote sensing observation task involves the coordination work of multidepartments and multi-systems, and the specific implementation planning belongs to different operation and control centers, which leads to the complex system information interaction and coordination mechanism and various interfaces and makes it difficult to realize the automatic operation of the task. The top-level overall planning ability is low. Current task implementation process includes various system such as multi-satellite remote sensing information, satellite TT&C platform, ground-based support resources, relay resources application and so on. According to the task requirements, these systems make plans respectively resulting of the complex information interaction between systems and high frequency of repetition planning. Top-level integrated joint mission planning mechanism need to be put in place to enhance the resources use efficiency.

3 Design of Satellite-to-Ground Integration Joint Mission Planning System for Multi-user Remote Sensing Information Resource Guarantee 3.1 General Train of Thought First, being consistent with reality, scheduling the remote sensing satellite resources guarantees the achievement of the remote sensing satellite product information. In order

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to enhance the support capability of the remote sensing satellite resources, multi-satellite joint mission planning standards should be established to standardize the whole process such as the work items, implementation steps and operation requirements from demand acceptance, telecommand generation, delivery and acceptance and so on. Second, based on application requirements, scheduling the space-based and groundbased resources use is coordinated to realize real-time acquisition of the remote sensing satellite resources for the TT&C resources. Meanwhile, the mechanism of a standard space-based and ground-based resources planning and scheduling should be established to realize the integrated scheduling and use of the space-based and ground-based resources and the TT&C resources. Third, focus on future development, joint mission planning meets the needs of multiple satellite types and users. In addition to satisfying the requirements of joint mission planning of remote sensing satellites, the system can also meet the requirements of joint mission planning of civilian-commercial multi-type satellites and multi-user mission planning of military-civilian-commercial users by extending functions, so as to realize the overall efficient use of national space-based resources. 3.2 Construction Goal Based on the mission implementation of requirements scheduling, mission coordination, mission planning, resources assignment, telecommand processing and delivery, orbital maneuver and effect evaluation, in order to attain the quick access to remote sensing products, enhance the effectiveness of the space-based and ground-based TT&C resources and guarantee multi-user demand, the goal is combing the satellite-to-ground integration joint mission planning system architecture and business process, straightening the main links restricting the implementation of joint mission planning and optimizing the interaction mechanism of information transmission process and coordination between systems. 3.3 System Composition and Function Requirements System Composition. In order to improve the utilization efficiency of space-based resources and multi-satellite remote sensing information, a satellite-to-ground integration joint mission planning system was designed. The system adopts the idea of modularization, and the design includes five subsystems, including requirements collection analysis and scheduling, mission planning, space-based and ground-based resources scheduling management, spacecraft operation management and remote sensing information distribution management (Fig. 2). Function Requirements. Requirements collection analysis and scheduling subsystem. This subsystem is mainly responsible for observation requirements collection, requirements coordination, situation display, mission evaluation, payload plan formulation, etc. It makes a preliminary judgment on the observation requirements proposed by all parties, selects and merges the remote sensing observation requirements, puts forward product support requirements to the mission scheduling subsystem, collects the execution process information of other subsystems for centralized display, and evaluates the task execution after the mission is completed.

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Fig. 2. Earth remote sensing observation mission satellite-to-ground integration planning system module

Mission planning subsystem. For single satellite, this subsystem is mainly responsible for payload mission planning, platform platform planning, data transmission resource use demand analysis, TT&C resource use demand analysis, space-based data sharing and distribution requirements, etc. According to the single satellite observation mission list, this subsystem formulates the detailed satellite observation work plan by the spacecraft information and space-ground and ground-based resource usage information provided by the fourth and fifth subsystem, which serves as the basis for the implementation of subsequent missions. Space-based and ground-based resources scheduling management subsystem. This subsystem is mainly responsible for the overall planning and scheduling of spacebased and ground-based TT&C resources, the formulation of equipment use plan and equipment operation plan, and equipment remote monitoring, etc. The subsystem mainly resolves conflicts against the use requirements of space-based and ground-based resources, generates the use plan of TT&C data transmission resources and the operation plan of space-based and ground-based equipment, and sends them to the spacecraft control management subsystem to complete the respective work plans. Spacecraft operation management subsystem. In accordance with the satellite observation work plan, this subsystem is mainly responsible for the formulation of spacecraft control plan, and the implementation of orbit determination, data processing, control calculation, remote control operation, health condition analysis, etc. Remote sensing information distribution management subsystem. This subsystem is mainly responsible for receiving the data sharing and distribution requirements of the mission planning subsystem, formulating data distribution tactics, clarifying the direction of data distribution, and monitoring the formulation of transmission plans, route configuration, dynamic routing planning and transmission quality required in data transmission.

4 Running Scenario Satellite-to-ground integration joint mission planning system aims to realize the comprehensive use of the remote sensing satellite resources and the unified scheduling management of the equipment resources of space-based and ground-based TT&C resources, and enhance the resources use efficiency. Meanwhile, in regard to the mission implementation, the system accepts, screens and merges multi-user product demands, regulates the system interface, simplifies the mission implementation process and improves the overall system operation support capability.

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The operation process of the joint mission planning system is sorted out according to the main functions undertaken by the six modules and the information flow relationship among the modules during the execution of the earth remote sensing observation mission, as shown in the figure below (Fig. 3).

Fig. 3. Satellite-to-ground integration joint mission planning system running scenario

Through the implementation processes of daily earth remote sensing observation mission, such as acceptance and analysis of satellite resources, integrated scheduling of space-based and ground-based TT&C data transmission resources, and unified mission planning, the following describes the operation scenarios of the system: Product demand users put forward product demands uniformly to the requirements collection analysis subsystem, which generates guarantee demand through resolving demand conflicts, screening and merging; According to product support requirements, mission scheduling subsystem analyzes satellite feasibility and equipment availability, then forms optimized observation schemes; According to the observation scheme, mission planning subsystem combines the idle resources of space-based and ground-based resources scheduling subsystem, applies for resource use, formulates single-satellite payload and platform control plan and spacebased and ground-based resources use plan to the spacecraft operation management subsystem, and formulates data distribution plan to the data distribution subsystem; The spacecraft operation management subsystem processes the payload and platform instructions according to the requirements of the mission platform and payload control, combined with the daily management requirements of the satellite platform TT&C, and sends them to the satellite through the space-based and ground-based resources; Data distribution subsystem collects remote sensing data received by data transmission station and sends it to data processing center as needed; Requirements collection analysis subsystem completes mission implementation process monitoring and mission execution effect evaluation by receiving operation situation information of each subsystem.

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5 Conclusion User demand for remote sensing has increasingly systematic requirements of joint, coordination, integrity. The satellite-to-ground integration joint mission planning system designs the system composition, function demands and the process of joint mission planning system, and gives the system running scenario, so that it promotes the ability of rapid access to remote sensing product, further optimizes resources use process, and provides train of thought to maximize the effect of satellite-to-ground resources integration guarantee.

References 1. Wang, M., Yang, F.: Intelligent remote sensing satellite and remote sensing image real-time service. Geodaetica et Cartographica Sinica 48(12), 1586–1594 (2019) 2. Li, D., Ding, L., Shao, Z.: Application-oriented real-time remote sensing service technology. Natl. Remote Sens. Bull. 25(1), 15–24 (2021) 3. Guan, Q., Feng, S., Ma, Y.: Research on mode of space-based intelligence service. J. Acad. Equip. 23(6), 66–70 (2012)

Analysis of the Influence of Satellite Drift Angle Correction Datum on Imaging Quality Chao Wang1,2(B) , Yuze Cao1 , Han Gao1 , Cheng Yan1 , Hongzhi Zhao1 , Keli Zhang1 , Jun Zhu1 , and Lili Wang1 1 DFH Satellite Co., Ltd., Beijing 100094, China

[email protected] 2 Space Information Academic, Space Engineering University, Beijing 101416, China

Abstract. A multi-field-of-view and multi-boresight-pointing remote sensing satellite drift angle correction datum optimization and imaging quality analysis method are proposed for ocean color satellites. In this research, different ground projection positions as the drift angle correction datum are selected. The pixel influence ratios caused by the drift angle residuals are calculated, and combining the payload imaging mode and parameters the effects on imaging quality are analyzed. In the simulation, the drift angle correction residuals and pixel influence ratios of the two types of ocean satellite payloads are obtained. The results show that the proportion of pixels caused by the drift angle correction residual which the datum is center of the boresight of the low-resolution payload reaches in the peak at 37.87% for the high-resolution payload which the Time Delay Integration (TDI) series is 96. When the center of the boresight of the high-resolution payload is used as the datum in the same TDI series condition, the influence ratio of the pixel decreased to 29.63%. The simulation model and method can be used to select the correction datum for the drift angle of the ocean satellite. The ocean satellite takes the center of the boresight of the high-resolution payload as the correction datum which is deviation from sub-point. The influence on the imaging quality of low-resolution and high-resolution imaging payload is acceptable. This research can be used as the principle for the selection of the ocean satellite drift angle correction datum and the design basis for the future ocean satellite imaging modes and payloads. Keywords: Ocean color satellite · Drift angle · Correction datum · Image quality

1 Introduction With the improvement of remote-sensing-satellite construction, Chinese ocean color satellite system has preliminarily possessed the ability of global ocean color temperature monitoring, marine disaster warning and monitoring, polar sea ice and navigation guarantee, coastal zone and island monitoring, and has great potential in the monitoring of oil spill identification and volcanic eruption [1–4]. High precision and high stability imaging quality analysis and assurance are particularly important. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 696–704, 2023. https://doi.org/10.1007/978-981-19-9968-0_84

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The ocean color satellite is equipped with spectrometer, coastal zone imager (CZI) and other loads, which are mainly used to image the deep-sea and coastal area. The Midspectrometer (M-Spec) has a spatial resolution of 100 m and a width of 1000 km, and the optical axis is imaging to the ground. The coastal zone imager is a high-resolution TDI CCD imaging payload with a spatial resolution of 5m/20m and a visual width of 1000 km. The equivalent field center is the visual axis, and the angle between the visual axis and the optical axis is −7°. The drift angle is the vector of the image shift velocity projected by the target in the image plane, and the columnar angle between the detector line arrays [5]. The drift angle causes lateral aberration to make blurred image point imaging, and the system modulation transfer function (MTF) will seriously be affected in terms of the image quality. To obtain high-quality spatial images, the displacement of each point needs to be accurately calculated and compensated for. The more widely used drift angle calculation and compensation methods mainly include homogeneous coordinate transformation method [6, 7], spherical coordinate model [8], vector coordinate image shift model [9], and quaternion-based control compensation algorithm [10]. Satellite drift angle compensation is a dynamic negative feedback process that changes with orbital position, with the purpose on theoretically correcting the realtime drift angle residuals down to 0°. The optimization of the angle correction datum is an important part of ensuring the image quality, and is the basis for the numerical calculation of the drift angle and the residual analysis of the angle compensation. Satellites usually use the sub-satellite point as the deflection correction reference, but for the drift angle correction residual characteristics of the image position of the wide field of view edge, it is necessary to quantitatively calculate the residuals of different correction datum for multi-payload and full field of view drift angle and analyze the impact on the image quality. In the research, the effect analysis results of different drift angle correction datums on payloads of ocean color satellites are given as the evaluation index, which are used to guide the selection of satellite deflection correction datums, which has good engineering application value.

2 The Basis for Selecting the Drift Angle Correction Datum and Influence When the space-borne TDICCD camera scans the ground target, the forward flight of the camera with the satellite will cause an image shift velocity V 1 opposite to the camera in the direction of flight. In addition, due to the existence of earth rotation, there is still an image motion velocity V 2 in the latitude direction of the target point, which is at a certain angle with the scanning direction. The combined velocity V is the actual image motion velocity of the target point relative to the camera. The angle η between V and V 1 is called the drift angle. The partial vectors of the combined velocity V along the navigation direction and the navigation direction of the satellite body are V x and V y , respectively, so the drift angle can be expressed as: The sub-vector V y of the combined velocity V in the navigation direction will cause the imaging dislocation of the target point A at all levels of TDICCD, which makes the final output image resolution decrease and the graphics seriously distorted (Fig. 1).

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Fig. 1. Schematic of the drift angle [11]

The condition of decrease in MTF caused by the image motion velocity matching error is more than 5% in one integration time [12–16]. That is the total pixel influence ratio is more than 1/3 of the single pixel size. σVy /Vx =

Vy × 100% Vx

(1)

Drift angle correction datum refers to that when the satellite is in orbit, the drift angle is corrected at a certain imaging position by adjusting the drift angle in real time, so that the theoretical value of the angle residual is 0°. Considering the actual engineering error, the drift angle correction residual is generally considered to meet the accuracy requirement of deviation angle correction within 0.1° (Fig. 2).

Fig. 2. Correction datum of drift angle based on sub-satellite point

General principles and characteristics of angle datum selection: (1) Take the satellite sub-satellite point as the correction datum. When a satellite takes the point as the datum, due to the difference in the combined velocity vector characteristics of the edge position of the field of view and the center position, the numerical characteristics of the modified residual of the drift angle at the edge of the field of view are cyclical, and the amplitude change of the edge position of the field of view is usually greater than the one of the sub-satellite points. (2) Take the payload with the highest spatial resolution as the correction datum. The payload with the highest spatial resolution is the qualitative selection method to ensure the residual balance of the payload view point and the full field of view. If

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the payload visual axis coincides with the optical axis, it is equivalent to taking the subsatellite point as the correction datum. When there is a deviation between the visual axis and the optical axis, the drift angle is corrected by the visual axis, and the correction error of the optical axis drift angle does not meet the correction accuracy. For multi-payload satellite systems, imaging quality of the full field of view for payload will be affected (Table 1).

3 Simulation 3.1 Orbit and Payload Parameters

Table 1. Orbital property parameters No.

Item

Value

1

Simulation time

20200628

2

Orbit semi-major axis

7028.14 km

3

Inclination

98.45°

4

Descending local time

10:30a.m.

Fig. 3. Schematic diagram of load imaging parameters

The spatial resolution of the spectrometer is 100 m, the width is 1000 km. The center of the field of view is the optical axis, the simulation system establishes the field of view center FOV0 , the field of view angle positive direction and negative direction are the maximum FOV+1 and FOV-1 , respectively. The positive and negative signs conform to the right-hand rule, the thumb points to the flight + X direction, and the four fingers coincide with the satellite body + Y direction (Fig. 3).

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Coastal zone imager is high-resolution TDI imaging payload. Through the payload mode design, the width is greater than 1000 km, using off-axis optical system and equivalent field of view center for the visual axis. Between coastal zone imager visual axis and spectrometer optical axis, there is a pitch direction angle, deviation from the subsatellite point angle of -θ, in line with the right-hand rule, thumb pointing to the satellite body + Y direction, four fingers coinciding with the satellite body -X direction, In the simulation system, the field of view center FOV’0 is established, the maximum positive and negative directions of the payload field of view angle are FOV’+1 and FOV’-1 . The positive and negative signs conform to the right-hand rule (Table 2). Table 2. Payload parameters No.

Item

Parameters

Value

1

Imaging parameters

TDI stages

4/18/16

2

Field of view center vector (optical axis)

FOV0

0°

3

The maximum field of view angle (positive)

FOV+1

30°

4

The maximum field of view (negative)

FOV-1

−30°

Mid-spectrometer

Coastal zone imager 1

Imaging parameters

TDI stages

8/32/96

2

Field of view center vector (visual axis)

FOV0

0°

3

The maximum field of view angle (positive)

FOV+1

30°

4

The maximum field of view (negative)

FOV-1

−30°

5

The angle between the optical axis and visual axis

−θ

−7°

3.2 Drift Angle Residuals for Different Correction Datum In the case that the satellite does not correct the drift angle, the numerical value of the drift angle of each field of view of the payload is as follows. The maximum angle of the coastal zone imager full field of view is 3.915°, and the maximum value of the spectrometer is 3.914° (Fig. 4). Simulation shows the drift angle with the center of the field of view of the Midspectrometer (optical axis) and the center of field of view of the coastal zone imager (visual axis), respectively, and calculates the angle residual. (1) Take the center position of the field of view of the mid-spectrometer (the intersection position of optical axis) as the correction datum of the drift angle (Fig. 5). (2) Take the center position field of view of the coastal zone imager (the intersection position of visual axis), which deviates from the point under the sub point −7°, as the correction datum (Table 3) and (Fig. 6).

Analysis of the Influence of Satellite Drift Angle Correction Datum CZI-30°FOV CZI+30°FOV CZI visual axis M-Spec-30°FOV M-Spec+30°FOV M-Spec optical axis

4.0 3.5

Drift angle (Degrees)

701

3.0 2.5 2.0 1.5 1.0 0.5 0.0 2000

3000

4000

Time series (seconds)

Fig. 4. Schematic diagram of the drift angle and time series of each field of view of the satellite payload CZI-30°FOV CZI+30°FOV CZI visual axis

0.25

0.20

0.15

0.10

0.05

M-Spec-30°FOV M-Spec+30°FOV M-Spec optical axis

0.12

Drift angle residual (degrees)

Drift angle residual (degrees)

0.30

0.10 0.08 0.06 0.04 0.02 0.00

0.00 2000

3000

Time series (seconds)

a)

4000

2000

3000

4000

Time series (seconds)

b)

Fig. 5. a) With the sub-satellite point as the correction datum, the CZI drift angle residual angle value; b) With the sub-satellite point as the correction datum, the M-Spec drift angle residual angle value

3.3 Effect of Drift Angle Residuals on Payload Image Quality With the optical axis or visual axis as the correction datum, the influence ratio on the full field pixels of the Mid-spectrometer is small, ranging from [0.04%, 2.67%], far less than 1/3; the coastal zone imager works in TDI imaging mode. With the increase of stages, the correction datum of different angles, in terms of sub-satellite point, will cause the influence ratio of drift angle residual on pixels to increase. When the TDI stage parameter is selected as 96, the image quality will decrease rapidly (Fig. 7 and Fig. 8). According to the analysis of the influence of the drift angle on the imaging quality, the satellite uses the visual axis, which is the center field of view of CZI, as the drift angle correction datum. The axis deviates from-7° below optical axis, and can ensure the full field of view imaging quality of the CZI and the M-Spec (Table 4 and 5).

C. Wang et al. CZI-30°FOV CZI+30°FOV CZI visual axis

Drift angle residual (degrees)

0.30

M-Spec-30°FOV M-Spec+30°FOV M-Spec optical axis

0.18 0.16

Drift angle residual (degrees)

702

0.25

0.20

0.15

0.10

0.05

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

0.00 2000

3000

2000

4000

3000

4000

Time series (seconds)

Time series (seconds)

b)

a)

Fig. 6. a) With the visual axis point as the correction datum, the CZI drift angle residual angle value; b) With the visual axis point as the correction datum, the M-Spec drift angle residual angle value Table 3. Maximum residual value of payload drift angle under different correction datums No.

Item

Max-Value

1

Coastal zone full field of view drift angle

0.2260°

2

Mid-spectrometer full field of view drift angle

0.1162°

1

Coastal zone full field of view drift angle

0.1769°

2

Mid-spectrometer full field of view drift angle

0.1534°

The subpoint (optical axis) as the correction datum

The viewpoint (visual axis) as the correction datum

Visual axis datum-M-Spec-30°FOV Visual axis datum-M-Spec+30°FOV Visual axis datum-M-Spec center FOV Optical axis datum-M-Spec-30°FOV Optical axis datum-M-Spec+30°FOV Optical axis datum-M-Spec center FOV

Influence ratio of the pixel (‰)

3.0

2.5

2.0

1.5

1.0

0.5

0.0 2000

3000

4000

Time series (seconds)

Fig. 7. Taking the optical axis and the visual axis as the correction datum, the proportion of each field of view pixels of the mid-spectrometer are affected

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Table 4. Different fields of view, drift angle correction datum, affect the proportion of the imaging pixels of the mid-spectrometer Item

Mid-spectrometer field of view Value

Influence ratios

−30°

0°

+30°

1

Optical axis correction datum

2.02%

0.04%

1.88%

2

Visual axis correction datum

1.30%

1.01%

2.67%

Optical axis datum-CZI-30°FOV Optical axis datum-CZI-30°-8 stages Optical axis datum-CZI-30°-32 stages Optical axis datum-CZI-30°-96 stages

400 375 350 325 300 275 250 225 200 175 150 125 100 75 50 25 0 -25 -50

Visual axis datum-CZI-30°FOV Visual axis datum-CZI-30°-8 stages Visual axis datum-CZI-30°-32 stages Visual axis datum-CZI-30°-96 stages

350 325

Influence ratio of the pixel (‰)

Influence ratio of the pixel (‰)

No.

300 275 250 225 200 175 150 125 100 75 50 25 0 -25

2000

3000

2000

4000

3000

4000

Time series (seconds)

Time series (seconds)

b)

a)

Fig. 8. a) Taking the optical axis as the correction datum, the proportion of FOV -30° of the CZI is affected; b) Taking the visual axis as the correction datum, the proportion of FOV -30° of the CZI is affected. Table 5. Different TDI stage, drift angle correction datum affect the influence ratio of the imaging pixel of the CZI No.

Item

Coastal zone imager TDI stage Value

Influence ratios

96

32

8

1

Optical axis correction datum

37.87%

12.62%

3.15%

2

Visual axis correction datum

29.63%

9.87%

2.469%

4 Conclusion Taking the pixel influence ratio as the evaluation index, the research uses the remote sensing satellite drift angle correction datum optimization and imaging quality analysis method, and selects the field of view center (visual axis) of the coastal zone imager and the field of view center (optical axis) of the spectrometer as the correction datum. In the orbit period, the pixel influence ratios caused by the drift angle correction residual of the multi-payload satellite are simulated. The results show that with the sub-satellite point as the correction datum, the pixel influence ratio of the high-resolution payload caused by the drift angle is 37.87% when the series is 96 with optical axis, and the pixel influence ratio decreases to 29.63% under the same TDI series with visual axis. In order

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to ensure the imaging quality, the visual axis which deviates from the optical axis -7° is taken as the correction datum. It meets the full field of view imaging quality of the two types of payloads. The analysis method and results can be used as the design basis for selecting the correction datum of the drift angle of the ocean color satellite, subsequent imaging mode. It has great engineering value. Acknowledgment. The work was supported by the Civil Aerospace Pre-research Project (D010206), Space Infrastructure Ocean Color Satellites Project.

References 1. Qu, X.: HY-1D Satellite. Satellite Appl. 6, 1 (2020) 2. Jianqiang, L.: Application of HY-1C satellite in natural disaster monitoring. Satellite Appl. 06, 26–34 (2020) 3. Shen, Y., Liu, J., Ding, J., Jiao, J., Sun, S., Lu, Y.: HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. J. Remote Sens. (Chinese), 24(8), 933–944 (2020) 4. Jianqiang, L., Xiaomin, Y., Tao, Z.: The eruption monitored by the CZI images on HY-1D. Haiyang Xuebao (Chinese), 43(2), 139–140 (2021) 5. Benjie, C., Kai, F., Wudong, D., Weichao, Z., Feng, Q.: An integrated calculation method of multi-target guided imaging parameters and drift angle. Spacecraft Eng. 29(05), 47–56 (2020) 6. Jiaqi, W., Ping, Y., Changxiang, Y.: Space optical remote sensing image motion velocity vector computation modeling. Acta Optica Sinica, 24(12), 1585–1589 (2004) 7. Jiaqi, W.: Design of Optical Instruments. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun (2003) 8. Yuan, X.: Calculation and compensation for the deviant angle of satellite borne TDI-CCD push scan camera. Aerospace Shanghai 6, 10–13 (2006) 9. Weichao, Z.: The Effect Analsis of Spacecraft Orbit and Attitude Parameters on Optical Image. Harbin Institute of Technology, Harbin (2009) 10. Zhong, W.: Effects of Spacecraft Orbit and Attitude Parameters on Optical Imaging. Harbin Institute of Technology, Harbin (2009) 11. Zhuang, M.: Residual Calculation of TDICCD Space Camera for Whiskbroom of Image Motion Velocity Matching. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun (2015) 12. Miao, Z.: Estimation of image shift matching residuals of pendulum scanning imaging in TDICCD space camera. Graduate School of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics), Changchun (2015) 13. Yuan, Z.: Modeling and Analysis of Image Motion Velocity for Satellite Borne TDICCD Camera in Roll Attitude. Harbin Institute of Technology (2013) 14. Tianbo, M., Yongfei, G.: Precision of row frequency of scientific grade TDICCD camera. Optics Precision Eng. 18(9), 2028–2035 (2010) 15. Yue, Q.: Two imaging simulation methods of the satellite TDICCD camera in non-track direction. Opt. Instrum. 44(3), 14–22 (2022) 16. Cheng, J., Hu, M., Wang, Y., Yang, W.: Research on ground simulation testing technology of low orbit TDICCD camera image quality. Spacecraft Recov. Remote Sens. 42(4), 29–36 (2021)

Application and Process of Inclination Adjustment of Satellites in Sun Synchronous Orbit Shenggang Wang(B) and Yuan Jun Beijing Institute of Control Engineering, Beijing, China [email protected]

Abstract. In this paper, taking the adjustment of the orbit inclination of a sunsynchronous remote sensing satellite as an example, the influence of the orbit inclination drift on the sun-synchronous satellite is expounded, and the influence model of the inclination drift on the local time of the descending node of the sunsynchronous orbit satellite is given. The influence of different orbit inclination adjustment quantity on the local time of the descending node is compared, considering the satellite life, propellant consumption, a relatively suitable adjustment quantity is selected and the dynamics and process of inclination adjustment are given. Through several times of orbit inclination adjustment, the service life of the satellite is prolonged and achieves good benefits. This inclination adjustment experience can be popularized and used. Keywords: Sun synchronous · Remote sensing satellite · Inclination adjustment · Local time of the descending node · Orbit control

1 Preface Sun-synchronous orbit is adopted by many remote sensing satellites, especially optical ones, due to its lots of advantages. Because the change period of the right ascension of ascending node (RAAN) of the sun-synchronous orbit is consistent with the period of the earth’s revolution around the sun, the local time remains approximately unchanged when the satellite passing through the same position, which is particularly beneficial for remote sensing satellite imaging, and the lighting conditions are relatively stable [1]. However, due to the influence of atmospheric drag [2], solar gravity, earth’s oblateness and etc., the satellite will slowly deviate from the nominal orbit over time [3]. Under normal circumstances, the local time of descending node is required to be controlled within a certain range, such as the commonly used 10:30 sun-synchronous orbit, and its local time of descending node is generally required to be between 10:00 and 11:00. At launch, the orbit will be offset, including semi-major axis and inclination, to meet the requirements of the local time of descending node during the satellite’s whole life [4]. However, due to the advancement of technology, the lifespan of remote sensing satellites has been continuously extended, and many remote sensing satellites lifespan have reached 5 years, 8 years or even longer. The drift of the orbit elements especially the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 705–712, 2023. https://doi.org/10.1007/978-981-19-9968-0_85

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local time of descending node may exceed the given range, affecting the performance of the satellite’s energy and lighting conditions for payload imaging. Therefore, it is necessary to adjust the orbital inclination in orbit, so that the local time of descending node can slowly return to the nominal range, improve the imaging conditions, and prolong the service life of the satellite. Taking a remote sensing satellite for earth resources as an example, this paper expounds the influencing factors of the local time of descending node and the adjustment process of the orbit inclination, which can be used as a reference for other remote sensing satellites to adjust the local time and the orbit inclination.

2 Variation Principle and Influencing Factors of Local Time The mass distribution of the earth is bulging near the equator. For near earth satellites, aspheric perturbations of the earth will occur, causing the nodal line between the orbital plane of the satellite and the equatorial plane to rotate eastward or westward in inertial space at a rate shown in formula (1), it is not only related to the parameter J2 representing the flat distribution of the earth’s mass, but also related to the semi-major axis a and the inclination angle i, and the eccentricity e of the orbit [5]. ˙ =− 

3nJ2 R2e cos i 2a2 (1 − e2 )2

(1)

Among the formula, n is the average angular velocity of the satellite. Re is the radius of the earth, and J2 = 0.001082. By selecting the appropriate a and i, the satellite orbit can meet the requirements of the sun-synchronous orbit, that is, the direction and rate of orbital precession are the same as the direction and rate of the Earth’s annual rotation around the sun. For sun synchronous orbit, e is always less than 10–2 , so: ˙ =−   = −9.97(

3nJ2 R2e cos i 2a2

Re 7 ) 2 cos i = ns = 0.9856◦ /d a2

(2) (3)

ns is the average angular rate of the sun. The most important feature of a sun-synchronous orbit is that the direction in which the sun irradiates the orbital plane is basically unchanged within a year, that is, the included angle between the orbital plane normal and the projection of the sun’s vector on the equatorial plane remains the same, i.e. The local time when the satellite passes over the equatorial node keep the same. However, due to the existence of atmospheric resistance and solar gravitational perturbation, the semimajor axis and the inclination of the orbit will change in long-term operation, which will cause  changes, that is, the angular acceleration of the RAAN will be generated, which will cause the local time of descending node of the satellite change [6]. Taking the total differentiation of formula (3), got: () =

∂() ∂() · i + · a ∂i ∂a

(4)

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During long-term operation in orbit, the semi-major axis will be attenuated due to atmospheric resistance and other influences. And for sun-synchronous remote sensing satellites, there is usually a requirement for a return period, and the sub-satellite point trajectory needs to be maintained. The nominal deviation a has an index requirement, and the orbit maintenance operation is carried out regularly [7]. Therefore, the influence of a on the satellite local time of descending node is ignored, and only the influence of the orbit inclination change is considered, and by (2), there are: ˙ = δ

3nJ2 R2e 3nJ2 R2e ˙ tan iδi sin iδi = cos i tan iδi = − 2a2 2a2 δi = i − i = i0 +

di t−i dt

(5) (6)

Among them, i is the nominal value of orbital inclination, denoting i = i0 − i, , then from (5), (6) can be obtained:  t 1 di 2 ˙ = − ˙ tan i(it + t ) δ = δ dt (7) 2 dt 0 Taking years as the unit, there is: δ = −2π tan i(it +

1 di 2 t ) 2 dt

(8)

By setting i and adjusting the orbit inclination, different δ can be achieved, so that the different adjustment of local time of the descending node can be realized. In addition, counting the change of RAAN by 1°, the local time of the descending node changes by 4 min [8]. From (8), the change curve of local time of the descending node approximates di . In addition, formula (9) is a parabola, and it reaches the extreme value when i = − dt established, where Td is the local time of the descending node on current orbital location, Td 0 is the local time of the descending node on a given orbital location, and Td is the change of the local time of the descending node within a period of time from the given orbital location to current orbital location, which can be obtained from (8). Td = Td 0 + Td

(9)

According to reference [9], for a sun-synchronous orbit, the solar gravitational force on the orbital inclination will produce a resonance effect with a long period, and the perturbation equation can be approximated as: di 3n2 = − s sin i(1 + cos ε)2 sin(2us∗ − 2) dt 16n

(10)

Among them, ε is the angle between ecliptic plane and equatorial plane, us∗ is the average ecliptic longitude of the sun, and (2us∗ −2) is related to the local time of the descending node. For the orbit of 10:30, there is 2us∗ − 2 = 45◦ . . For a sun-synchronous satellite with given nominal orbit elements and current orbit elements, (8), (9), (10) can be used to approximate the drift of the local time of the descending node.

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3 The Principle of Orbit Inclination Adjustment According to literature [10], for near-circular orbits and sun-synchronous orbits with small eccentricity, the controlled acceleration is applied to the satellite through thrusters, and is decomposed into R, T, and W along the orbital system that is, radial, lateral and normal acceleration. There are: da 2T = dt n W cos u di = dt na W sin u d (11) = dt na sin i 2T cos u + R sin u dex = dt na dey 2T sin u − R cos u = dt na Among them ex = e cos ω, ey = e sin ω, u = ω + f (ω is perigee angle, f is true anomaly); Assuming that the thrust of the thruster is constant and there is no attitude deviation during the operation of the thruster, the continuous working time of the thruster is t, the corresponding working arc length is u, and the midpoint of the arc is u0 , then after the maneuver, the orbit elements change are: 2a VT V 1 i = η cos u0 VW V 1 sin u0 (12) VW  = η · V sin i 1 ex = η (2VT cos u0 + VR sin u0 ) V 1 ey = η (2VT sin u0 − VR cos u0 ) V where V = na is the circular orbital velocity, VR = Rt, VT = T t, VW = W t u are the radial, lateral and normal velocity increments, η = sin u 2 / 2 is the efficiency factor, when the thruster working time is 800 s and the u is 48°, η is about 0.9711, which is close to 1. From (12), when only the normal thrust is applied, only the orbital inclination and the RAAN are changed, and other parameters are not affected, and if only the orbital inclination is to be changed, the normal thrust can be applied at u0 = 0◦ or u0 = 180◦ . The influence on RAAN is so small that it can be ignored. The normal velocity increment at this time: a =

VW = ±V i 0◦ ,

(13)

when u0 = takes the positive sign, at this time, the orbit is rising, and the satellite moves from south to north; when u0 = 180◦ , takes the negative sign, at this time, the orbit is descending, and the satellite moves from north to south.

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4 Choice of Inclination Adjustment Quantity The nominal orbit (column 2) and the orbit parameters at 2:50:41 on April 8, 2019 of UTC time (column 3) of a remote sensing satellite are as follows: Table 1. Orbital parameters of a remote sensing satellite UTC time

--

2019/April/8 2:50:41

Semi-major axis(km)

7148.865

7148.9755

Inclination(°)

98.5044

98.395

e

0.0011

0.00135

RAAN

--

166.694

ω (°)

90

89.80

Local time of the descending node

10:30

10:04

Orbital period (min)

100.3753

100.3344

The local time of descending node of the satellite had changed to 10:04, which had caused a certain impact on the satellite business. The orbital inclination had a deviation of –0.1094° from the nominal orbit. If the orbit control were not implemented, the local time of the descending node would continue to drift in the direction of 6:00, and it would exceed the allowable variation range in June 2019 by 10: 00 ~11:00, so the inclination angle must be adjusted to make it drift back to 10:30. In order to make the local time drift back, the orbital inclination adjustment need to have a positive bias relative to the nominal value in addition to eliminating the current negative deviation. Therefore, the inclination adjustment was selected as 0.2°, 0.23°, 0.28° and other different values, and perform recursion according to the formula (8) (9) (10), and the result is shown in Fig. 1. Since some factors are ignored in the formulas (8) (9) and (10), there is an error in the prediction of the local time change, but it is very effective for quickly determining the quantity of the adjustment. When the inclination adjustment quantity is 0.2°, it takes about 1.6 years, and the local time returns to a maximum around 10:16, and then drifts to 10:00. After about 3.3 years, it will exceed the allowable range and needs to be adjusted again; when the inclination adjustment quantity is 0.23°, after about 2.3 years, the local time will return to a maximum around 10:26, and then drift to 10:00, and after 4.6 years, it will exceed the allowable range and need to be adjusted again; when the inclination adjustment quantity is 0.28°, after about 4.0 years, the local time reaches a maximum around 10:56, and it does not need to be adjusted again for a quite long time. However, the adjustment quantity of the inclination is not only constrained by the adjustment requirements of the local time, but also by other conditions, such as the requirements of the power supply system, the requirements of the thermal control system, the requirements of the propellant consumption, etc. Finally, the inclination angle is determined through multi-party negotiation. The adjustment quantity is 0.23°.

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From formula (13), because the inclination adjustment is >0, if the satellite flies from south to north, and implement the orbit maneuver near the ascending node, the thruster needs to generate a positive normal velocity increment to the orbital plane; If the satellite flies from north to south, and implement the orbit maneuver near the descending node, the thrusters need to generate negative normal velocity increments to the orbital plane. When the satellite attitude is 0, the satellite body coordinate axis Xb is along the forward direction of the orbit, + Zb points to the center of the earth, + Yb and Xb, Zb form a right-handed coordinate system, as shown in Fig. 2, when changing orbits at the ascending node, The thrust direction needs to be along the body-Yb direction, and when changing orbits at the descending node, the thrust direction needs to be along the body + Yb direction. Since the + Yb side of this satellite is equipped with a 20N thruster, the orbit control at the ascending node is selected, and attitude maneuvers are not required.

Fig. 1. Prediction of time at the descending node corresponding to different inclination adjustment quantity

Fig. 2. Schematic diagram of inclination adjustment thrust direction

The velocity increment VW = V i = nai, calculated using the relevant data in Table 1, VW = 29.95 m/s, the satellite mass is about 2000 kg, and uses anhydrous hydrazine propellant, which requires about 28 kg of propellant.

5 Inclination Adjustment Process The inclination adjustment quantity is +0.23°, and the +Y side 20N thruster is used. Since the longest ignition time does not exceed 800 s, the orbit inclination adjustment needs to be completed in several times. The first orbit control is arranged in the measurement and control arc, and the time is short. The purpose is to complete the calibration of the thruster and observe whether the satellite telemetry is normal during the orbit change process. The subsequent times are outside the measurement and control arc. See Table 2 for the details. According to the calculation of the ignition center argument being 0°, the entire inclination control is completed in 6 batches. After the inclination adjustment is completed, two in-plane orbital controls are required to jointly control of a, e, ω and restore the regression and freezing properties of the orbit. This is because there is an attitude deviation during the orbit change process, and the thrust will have components in the radial and

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lateral directions, which will affect the satellite’s return and frozen orbit characteristics. After the inclination adjustment is completed, a, e, ω joint control is required to make the satellite meet the return and freezing requirements again. Track requirements. The entire orbital change process was finally estimated and a total of 28.2 kg of propellant was used, which was close to the expected consumption. Table 2. Orbit inclination adjustment control table Ignition serial number

Thrust direction

Average adjustment quantity

v (m/s)

Ignition time length (s)

1

– Y

i = 0.0200°

2.606

219.19

2

– Y

i = 0.0600°

7.818

767.58

3

– Y

i = 0.0500°

6.515

769.26

4

– Y

i = 0.0400°

5.212

507.68

5

– Y

i = 0.0500°

6.515

739.81

6

– Y

i = 0.0100°

1.303

162.25

7

+X

a = 300.21

0.172

113.64

8

+X

a = 20.01

0.010

6.90

6 Effect Tracking In April 2019, the inclination angle of the satellite was adjusted by 0.23°, and the orbital inclination of the satellite and the local time of the descending node were continuously tracked. As of June 2022, the inclination of the satellite and the local time of the descending node are shown in Fig. 3 and Fig. 4.

Fig. 3. Measured values of orbital inclination Fig. 4. Measured local time of descending trend node

From Fig. 3 and Fig. 4, after the orbital inclination is adjusted, the satellite’s local time of the descending node begins to drift back from 10:04 to 10:30, and reaches the maximum value in December 2021, close to 10:30, which is equivalent to 2.6 years to

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reach the maximum value, it continues to drift towards 10:00. It is expected that in 2025, the local time of the descending node will be close to 10:00 again, and it will need to be adjusted again. At that time, the life of the satellite will reach 11 years, far exceeding the design life requirements. According to the remaining quantity of the propellant, the above method can be used to adjust the orbit inclination again, so that the satellite can continue to work, and the service life of the satellite will be prolonged.

7 Concluding Remarks In this paper, the traditional satellite-ground loop method is used in the adjustment of the orbit inclination, that is, the ground calculates the orbit control quantity, generates the instruction block, injects the instruction through the measurement and control system, executes on the satellite, and conducts the orbit measurement using the ground measurement and control system to evaluate the actual change. Track change effect, repeat the above process until the final control target quantity is reached. With the popularization of the global positioning system (such as BeiDou and GPS) and the improvement of the computing power of on-board computers, in the future the orbit control planning and execution will be carried out autonomously by the satellite. The GPS information completes the orbit determination, the satellite independently evaluates the orbit control effect, and iterates this process continuously to achieve the final control effect. This onboard autonomous orbit control strategy can also be applied to the orbit control of multi-satellite networking or multi-satellite constellations, which will be the important application areas and fields in space of AI and big data technology.

References 1. Renwei, Z.: Satellite Orbit Attitude Dynamics and Control. Beijing University of Aeronautics and Astronautics Press, Beijing (1998) 2. Meili, H., Dayi, W., Hao, F., et al.: Method and application of atmosphere density retrieving based measured data. J. Astronaut. 39(12), 1419–1424 (2018) 3. Guoyun, Z., Lifen, C., Xiao, H., et al.: Impact analysis and adjustment of nearly round sunsynchronous orbit satellite injection inclination deviation. Aerospace Shanghai 31(2), 39–41 (2014) 4. Yong’an, Y., Zuren, F., Wei, T., et al: Study of shift control strategy for local time at descending node based on sun synchronous orbit satellite. Control Decis. 23(6), 693–696 (2008) 5. Xiangchen, W., Wei, C., Chen, J.: Local time of descending node active control methods and implement of sun synchronous orbit based orbit inclination modify. Aerospace Shanghai 33(2), 63–67 (2016). 6. Qiang, L., Jianfeng, L., Jihong, C., et al.: Perturbation analysis for dawn dusk orbit. J. Spacecraft TT&C Technol. 35(6), 450–456 (2016) 7. Wang, J.J., Baoyin, H.X., et al.: Constellation acquisition and maintenance of sun-synchronous frozen orbit. Aerospace Control Appl. 47(3), 24–32 (2021) 8. Lin, Z., Zengshan, Y., Jiao, C., et al.: The sun-synchronous orbit simulation of local time of descending node by using STK. Aerospace Control 34(1), 70–74 (2016) 9. Weilian, Y.: Long-term evolution and control for sun-synchronous and recursive orbit. Spacecraft Eng. 17(2), 26–30 (2008) 10. Weilian, Y.: Practical method of near circular orbit control. Spacecraft Eng. 12(1), 1–5 (2003)

Analysis Method for Isolation Index of Transceiver Link of Space Ground TT&C Equipment Jingyu Zhao1,2 , Jianxiao Zou1,2(B) , Shuo Wang1,2 , Yalong Yan1,2 , and Ben Su1,2 1 Shenzhen Institute for Advanced Study, University of Electronic Science and Technology

of China, Shenzhen, China [email protected] 2 State Key Laboratory of Aerospace Dynamics, China Xi’an Satellite Control Center, Xian, China

Abstract. An analysis method for isolation index of the transceiver link of the space ground TT&C equipment is presented in this paper, which mainly solves the interference of the frequency component or the intermodulation component of the transmitted signal to the received signal when the ground TT&C equipment transmits a high-power signal. Firstly, the working principle of the transceiver link and the causes of the transceiver’s interference of the ground TT&C equipment are analyzed. On this basis, starting with the standard mode and the spread spectrum mode, the influence of the transmission of the ground TT&C equipment on the reception under two working systems is analyzed theoretically; Secondly, the isolation degree of ground TT&C equipment under two working systems is designed, and the isolation degree requirements of ground TT&C equipment are obtained; Finally, the correctness of the isolation index design is verified by practical experiments. The design method proposed in this paper can be used for reference for solving the problem of transceiver interference of space ground TT&C equipment. Keywords: Space ground TT&C equipment · TT&C system · Transmission and receiving interference · Isolation

1 Introduction The problem of the transmission and receiving interference of space ground TT&C equipment is a systematic problem. If this problem cannot be solved properly, the frequency component of the equipment’s transmitted signal or the intermodulation component of the received and transmitted signal will fall into the equipment’s receiving band, and the equipment’s receiving link will be saturated and cannot work normally, thus affecting the normal receiving of satellite telemetry data [1]. In recent years, this problem often occurs when several sets of space ground TT&C equipment transmit high-power signals [2]. In order to solve the problem of transmission and receiving interference of space ground TT&C equipment, different institutes have made beneficial attempts. Literature [3] has analyzed the isolation index of the system from two aspects: the influence of the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 713–722, 2023. https://doi.org/10.1007/978-981-19-9968-0_86

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transmission power leakage on the operating characteristics of the field amplifier and the influence of the transmission link on the noise temperature power spectral density of the receiving band. Literature [4] has put forward relevant requirements for the system transceiver isolation from the aspects of the pre selection of receivers, the suppression characteristics of transmitters on receiving frequencies, the design of uplink and downlink channel isolation, the duplex isolation of feeds and the duplex isolation test of antennas. However, in general, the current research on the transmission and receiving interference of ground TT&C equipment by various units focuses on the theoretical level, and has not been applied in engineering. Combined with the current situation of the transmission and receiving interference in the test and operation of space ground TT&C equipment in recent years, the transmission and receiving interference of space ground TT&C equipment from the aspects of working mechanism of equipment transceiver link, index requirements, transceiver isolation design method and index demonstration is studied this paper systematically. Through theoretical analysis and test verification, the reasonable transceiver isolation index of ground TT&C equipment under different working systems (standard mode and spread spectrum mode) is determined, which has great reference value for solving the transceiver interference problem of space ground TT&C equipment.

2 Operating Mechanism of Transceiver Link The working mechanism of the transceiver link of the space ground TT&C equipment is usually shown as Fig. 1 [5].

Fig. 1. Operating mechanism of the transceiver link.

The signal transmission mechanism of uplink is as follows: the baseband unit generates transmission signals of different systems and sends them to the up converter. After sampling and frequency conversion, the transmission signals are transmitted to the power amplifier through the transmission RF switch network. After amplified by the power amplifier, the signals are successively transmitted to the antenna feed through the

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resistive filter, waveguide switch, duplexer and other components, and then transmitted by the antenna. The downlink signal receiving mechanism is as follows: the downlink signal transmitted by the spacecraft is received by the antenna, and sent to the field amplifier after passing through the antenna feed and duplexer, then sent to the down converter for frequency conversion after amplified by the amplifier. The sampled and converted received signal is sent to the baseband for descrambling or demodulation.

3 Analysis of Transceiver Interference 3.1 Influence of Baseband Generated Signal on Receiving Link The following two working modes are commonly used in space TT&C: standard mode and spread spectrum mode. Under the standard mode signal system, the transmitted signal bandwidth is usually narrow, and the transmitted signal is far away from the received signal in frequency. Theoretically, it will not affect the received signal in the signal generation stage. In standard mode, the interference effect of transmission on the received signal mainly depends on the noise generated by the power amplifier itself in receiving band, the suppression system of the receiving filter at the output end of the power amplifier, the suppression degree of the antenna duplexer on the received signal and other factors. The above factors will be analyzed below. The spread spectrum mode generally adopts the BPSK modulation mode, which mainly modulates the phase of the output modulation signal according to the different input data. The phase 0 and π of the output modulation signal represent 0 and 1 of the digital signal respectively. The time domain expression of BPSK is [6]    an g(t − nTs ) cos(ωc t) (1) SBPSK (t) = n

Among them, an = {

+1, Probability is P −1, Probability is 1 − P

(2)

That is, within a symbol duration Ts SBPSK (t) = {

cos(ωc t), an = +1 - cos(ωc t), an = −1

(3)

For spread spectrum, let the spread spectrum code be c(t)c(t) and the carrier frequency be ω0 . In practical application, the spread spectrum code usually adopts the bipolar code, that is ct = {−1, +1}, the BPSK modulated signal of the spread spectrum system can be expressed as s(t) = c(t) cos(ωc t + ϕ)

(4)

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If the information code is considered to be d (t), the BPSK modulation output of direct sequence spread spectrum system is [7] S(t) = d (t)c(t) cos(ωt + ϕ)

(5)

According to the above analysis, the spectrum of spread spectrum mode signal can be obtained, as shown in Fig. 2.

Fig. 2. Spread spectrum system signal spectrum.

It can be seen from the above figure that BPSK modulation spectrum theoretically occupies a very wide signal bandwidth, and the signal and sidelobe may cause intermodulation (the carrier to noise signal amplitude ratio is 24.7 dB when the carrier deviates from 7.5 MHz).At the same time, the actual signal generated by the baseband is tested, and the test results are shown in Fig. 3.When the measured result deviates from the carrier by 7.5 MHz, the carrier to noise signal amplitude ratio is only 13.82 db. Therefore, in the space ground TT&C equipment, in order to prevent the transmitting link from affecting the receiving link, the baseband generated signal should be shaped and filtered to eliminate the high-order sidelobe in the modulation power and generate a high-quality baseband signal, as shown in Fig. 4, when the processed baseband signal deviates from the carrier by 7.5 MHz, the carrier to noise signal amplitude ratio is 49.85 dB.Therefore, high-quality baseband signal generation needs to be designed in detail.

Fig. 3. Measured spectrum without filter

3.2 Influence of High Power Transmitting Signal on Receiving Link The impact of high-power signals on the receiving link is mainly reflected in two aspects: firstly, it is necessary to consider that the noise bottom will also rise during signal

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Fig. 4. Measured spectrum with filter

amplification. Therefore, when the transmitter outputs full power, the noise temperature caused by the transmission power falling in the receiver band is not greater than 1 K, that is, the signal equivalent noise power spectral density is less than –208.6 dbw/Hz; Secondly, it should be considered that after the combined interference signal generated on the transmitting link falls into the receiving frequency band, the stray signal does not exceed the thermal noise level of the receiving link, and the solid state power amplifier (SSPA) has the highest efficiency and maximum output power when it works in saturated state. For example, for power tubes, when the gain of S-band is compressed by 3 dB, its efficiency can reach more than 60%. At this time, the third-order intermodulation coefficient is lower than –17 dBc. If no measures are taken, the linear performance of SSPA will be seriously deteriorated. Considering the influence of the above two points, it is necessary to design the isolation of the receiving filter and the duplexer after the power amplifier, and how to make the SSPA work with high efficiency and high linearity should be considered. 3.3 Influence of Receiving Resistance Filter and Duplexer on Receiving Link The feed of the space ground TT&C equipment generally adopts the transceiver sharing system, so the transceiver isolation mainly depends on the resistance filter and duplexer. The main function of the receiving resistance filter is to suppress the receiving band noise or intermodulation signal generated on the transmitting component, so as to ensure that it will not affect the normal operation of the receiving link. The main function of the duplexer is to use the same antenna to transmit and receive signals in different frequencies. The filter is used to separate the paths of the transmission link and the reception link according to the different frequencies used for transmission and reception, so as to ensure that the transmission link will not affect the normal operation of the reception link. Factors such as high-band rejection, low insertion loss and large power capacity should be considered in engineering. 3.4 Influence of High Power Signal Transmission on Receiving Link Passive intermodulation (PIM) may occur during the transmission of high-power signals. Passive intermodulation means that when two or more transmit carriers pass through a passive component, due to the nonlinear characteristics of the passive component, the transmit carriers are linearly combined, and the new frequency components generated fall into the receiving channel and the power level is large, which will cause interference to the receiver and affect the receiver to receive useful signals. Passive intermodulation

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product level is related to input power and intermodulation order. The lower the input power is, the higher the intermodulation is, the lower the PIM is; On the contrary, the higher the PIM is. The generation of passive intermodulation basically comes from two kinds of passive nonlinearity: contact nonlinearity and material nonlinearity. The former represents any contact with nonlinear current and voltage behavior, such as loosening, oxidation and corrosion at the junction of waveguide flange, tuning screw and rivet; the latter refers to materials with inherent nonlinear electrical properties, such as ferromagnetic materials and carbon fibers. Compared with active intermodulation, passive intermodulation products mainly have the following characteristics [8, 9]: 1. Passive intermodulation products cannot be filtered by filters. Because if a filter is added to the transmission channel, the PIM products in front of it can be filtered out, but the PIM products behind the filter still cannot be filtered out. As a result, a large number of PIM products still fall into the receiving passband. 2. Passive intermodulation products vary with time. Passive intermodulation products are not stable in time. They are extremely sensitive to physical motion or temperature cycling processes or temperature changes. Therefore, reliable data can only be obtained under various test conditions and long-term observation. 3. Passive intermodulation products have threshold effect. Measurements show that passive intermodulation products can appear without warning at a specific temperature or power level. For this reason, it is not enough to simply measure within the normally specified range, and a certain margin must be established. 4. Passive intermodulation products exhibit unpredictability relative to power levels. In a limited dynamic range, the higher the order, the greater the intermodulation product changes with the power level. However, many other relationships were observed in the experiment, including not varying with the power level, because each discrete intermodulation product changes in a different way from other products varying with the power level. Unlike active intermodulation products, it is impossible to establish a fixed relationship between passive intermodulation products and test conditions. Therefore, a large number of tests must be carried out to improve the reliability of test results. 5. Passive intermodulation products can exhibit broadband noise characteristics. For example, in the spread spectrum system, the spread spectrum signal is composed of multiple discrete spectral lines, so its PIM interference is similar to broadband noise. In microwave RF links, common passive components such as coaxial cable, waveguide, connector, filter, duplexer, coupler and antenna will produce passive intermodulation products to a certain extent. Passive intermodulation products are usually generated in systems with multiple frequencies. For this system, which operates simultaneously with multiple frequencies and multiple beams, special attention and careful analysis are required to ensure that the receiving array can work normally when the transmitting array operates at multiple frequencies.

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3.5 Influence of Space Radiation on Receiving Link In the process of signal generation, amplification and transmission, electromagnetic leakage may affect the receiving link. Therefore, it is necessary to set electromagnetic shielding for chassis and cabinet, and lay microwave absorbing materials in the highfrequency box to suppress the harm of space radiation to the overall link.

4 Requirements and Design Method of Transceiver Isolation Index According to the analysis in the previous section, it can be concluded that the impact of the uplink operation of the system on the receiving band mainly includes four aspects: 1. 2. 3. 4.

Noise temperature generated by the power amplifier in the receiving band. Power amplifier output terminal resistance filter suppression system. The antenna duplexer suppresses the noise in the receiving band. The intermodulation signal generated by the transmitted signal of the power amplifier falls into the clutter in the receiving band.

The following is the accounting and analysis in two cases according to the working mode of the system: 4.1 The Standard Mode System The working bandwidth of the standard mode system is very narrow. The influence of the signal on the receiving band under the standard system mainly depends on the noise temperature generated by the power amplifier itself in the receiving band, the rejection regime of the power amplifier output rejection filter, and the rejection regime of the antenna duplexer in the receiving band. The system requires that when the transmitter outputs full power, the noise temperature caused by the transmission power falling in the receiver band shall not be greater than 1 K. Assuming that the noise temperature in the original receiving band is T, the noise spectral density in the receiving band after 1 K deterioration is: (dBW /Hz) = k(T + T ) = kT + 228.6dBW/Hz

(6)

Therefore, if the noise temperature of the transmitter falling into the receiving band is less than –228.6 dBW/Hz, it can meet the requirement that the noise temperature caused by the transmitter falling into the receiver band when the transmission power is not greater than 1 K. Let the output noise power spectrum of the transmitting power amplifier be out (dBW/Hz), the isolation degree ITR (dB) of the duplexer, and the isolation degree IR (dB) of the receive resistance filter, then ITR and IR must meet the following formula: out − (ITR + IR ) ≤ 0

(7)

and the following formula can be obtained: (ITR + IR ) ≥ out − 0

(8)

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When the power amplifier operates at full power of 400 W, the noise power spectral density in the receiving band is –120 dBW/Hz. Calculated as per –228.6 dBw/Hz as required above, the required suppression degree when the received band noise of the power amplifier output reaches the input end of the field amplifier is (ITR + IR ) ≥ out − 0 = 108.6dB

(9)

A certain margin shall be considered during system design, and the value shall be taken as 120 dB. The noise power falling in the receiving band when the S-band power amplifier outputs 400 W can not affect the noise temperature of the S-band receiving link through the reasonable allocation index of the receiving resistance filter and the antenna duplexer. 4.2 The Spread Spectrum Mode System In the spread spectrum mode system, the influence on the receiving band mainly depends on the noise temperature generated by the power amplifier itself in the receiving band, the rejection degree of the power amplifier output receiving filter, and the rejection degree of the antenna duplexer in the receiving band. The system requires that when the transmitter outputs full power, the noise temperature caused by the transmission power falling in the receiver band shall not be greater than 1 K. Assuming that the noise temperature in the original receiving band is T, the noise spectral density in the receiving band after 1 K deterioration is: (dBW /Hz) = k(T + T ) = kT + 228.6dBW/Hz

(10)

Therefore, if the noise temperature of the transmitter falling into the receiving band is less than –228.6 dBw/Hz, it can meet the requirement that the noise temperature caused by the transmitter falling into the receiver band when the transmission power is not greater than 1 K. When the power amplifier operates at full power of 400 W, due to the wide non coherent spread spectrum bandwidth, the power amplifier will generate a certain intermodulation extended interference signal outside the operating band due to its inherent nonlinear characteristics. When the system operates at the high frequency of the band, the extended interference signal will have the greatest impact in the receiving band. At this time, the noise power spectral density in the receiving band is –100 dBW/Hz, which is calculated according to –228.6 dBW/Hz as required above, then according to Formula (9), it can be obtained that the required suppression degree when the received band noise of the power amplifier output reaches the input end of the field amplifier is (ITR + IR ) ≥ out − 0 = 128.6dB

(11)

A certain margin shall be considered during system design, and the value shall be taken as 140 dB. The noise power falling in the receiving band when the S-band power amplifier outputs 400 W can not affect the noise temperature of the S-band receiving link through the reasonable allocation index of the receiving resistance filter and the antenna duplexer.

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5 Test and Validation Based on the above theoretical analysis results, three sets of typical space TT&C equipment are selected to carry out the transceiver isolation test under the condition of S band, which is shown in Table 1. The background noise of the three equipment is –128.6 dBm, –127.4 dBm and –130.5 dBm respectively. Table 1. Transmission to reception influence of standard and spread spectrum mode.

The test results show that: 1. In standard mode, 400 W power is transmitted, which has no impact on the noise packets of the receiving channel. 2. In spread spectrum mode, 400 W power is transmitted, which has no effect on the noise packets of the receiving channel.

6 Conclusions Based on the requirement to reduce the transceiver interference of space TT&C equipment, an analysis method of isolation index of transceiver link of space ground TT&C equipment is proposed. Through theoretical analysis and field testing, the correctness of the proposed design on the causes of uplink and downlink transceiver isolation interference and isolation methods of space TT&C equipment is verified. In engineering practice, the transceiver isolation ability of related equipment is effectively improved, and the results are verified to be correct.

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References 1. Ye, H.: Base Station Multiband RET Antenna PIM Study and Design Optimize. Master’s Degree Thesis. SOOCHOW UNIVERSITY (2014) 2. Wen, X.: Research on RF Front-end Design of Passive Intermodulation Test Machine. Master’s Degree Thesis. UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA (2020) 3. Qiu, C., Shen, H., Soltani, S., Sapra, K.: An optimal and distributed strategy for packet recovery in wireless networks. 2013 Proceedings IEEE of INFOCOM (2013) 4. Arazm, F., Benson, F.: Nonlinearities in metal contacts at microwave frequencies. IEEE Trans. Electromagn. Compatibility, EMC-22(3), 142–149 (1980) 5. Li, M.: Generation and prevention of PIM interference. Electromech. Components, 32(2) (2012) 6. Frank, K., Steven, C.: ADI Passive Intermodulation Effects in Base Stations. Focus Technology. 1005–5517.2017.5.008 7. Wang, L., Yin, Y.: Research on technology of reducing third-order PIM of antenna. National Defense Industry Press (2015) 8. Yang, G.: Analysis and improvement of passive intermodulation in output triplexer. Electronic Information Warfare Technology. https://doi.org/10.3969/j.issn.1674-2230.2018.06.018 9. Tan, Q.: Research on the third-order intermodulation in wireless communication system. Electronics R&D, 1000–0755.2014.12.006

Satellite Momentum Wheel Life Prediction Method Based on PSO-LSSVM Zhang Chao1(B) , Wenjie Ma1 , and Jiexuan Song2 1 DFH Satellite Co.Ltd., Hong Kong, China

[email protected] 2 Institute of Spacecraft Application System Engineering, Beijing, China

Abstract. In order to predicate the satellite momentum life accurately, this paper adopt PSO-LSSVM method to predict the momentum wheel accurately based on the comparison of key performance prediction result and standard threshold. This method find the optimal parameter of LSSVM model using PSO, then the LSSVM algorithm is used to establish momentum wheel life model, finally, the life prediction model is used to predict the momentum wheel using the momentum wheel key performance parameter. The numerical example is conducted to predict momentum wheel, the result shows that the prediction model has good prediction accuracy and strong engineering applicability. Keywords: Momentum wheel · Life · PSO · LSSVM · Reliability

1 Introduction With the mass deployment of satellite constellation, the demand for satellite featured by low-cost and equivalent life design under constrained cost is increasing strongly. Therefore, the satellite equivalent life estimation and prediction technology needs to be solved urgently. The momentum wheels are consisted of electric circuit and rotating part, they can be effected by the space environment, the inner axis frictional resistance the motor assembly and the electric circuit. When the momentum wheels are effected, the satellite attitude is lost. There are many products produced in the aerospace department, the momentum orbit life is very critical to the satellite [1–3]. Li used Bayes net model which takes momentum wheel experiment data into consideration to establish the momentum life prediction model [4], Li used wiener-process to model the degradation trend and conduct the simulation, but this method can’t solve the model accuracy problem caused by different environment temperature of different orbit [5]. Liu proposed a life prediction method for Momentum Wheel Based on Multiple Degradation Parameters, this paper select electric current and lubrication as the key parameters to predict the remaining life of momentum wheel [6]. But due to the momentum wheel remaining lubrication can’t be accurately measured after many times of detach, the model final prediction result accuracy can be effected. Jiang solved the reliability and failure PDF of the momentum wheel which works under different work environment [7]. But there are too many parameters needed to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 723–730, 2023. https://doi.org/10.1007/978-981-19-9968-0_87

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be estimated, the fitness of this model is not high and can’t be applied in engineering practice. The research above study the applied scope and development status of life prediction method, but most of the research assume that the degradation model is known previously, the model parameters are estimated by the condition monitoring data. However, the degradation model is unknown in engineering practice, and the degradation model of different equipment is not the same, the prediction method accuracy can be effected by the selection. The data-based machine learning can solve the problem which the degradation model is unknown, meanwhile, the input of model can be different types of data rather than condition monitoring-data. There are many fault prediction method such as grey theory, neural network method or SVM method. The prediction based on grey theory is an effective method when dealing with problems characterized as small sample, poor information and uncertainty, but the accuracy and stability of this prediction need more improvement [8]; Though the neural net have strong nonlinear mapping ability, but it has the disadvantages such as numerous training data samples are needed, slow convergence and the net structure can’t be determined [9]; the prediction method based on SVM has great advantages when solving the problems featured by small samples, nonlinear and local minima, it has been widely used in many fields, but it also has the disadvantages such as the model sparsity is limited, the output of the model is non probabilistic and the model parameters set are complex [10]. Considering the satellite performance data has disadvantages such as the small samples, less information and non-linear, the LSSVM algorithm, which is characterized by strong nonlinear prediction and generalization ability, is adopted to establish momentum wheel predict model.

2 LSSVM Algorithm Assume that the sample data of training set is: T = {(x1 , y1 ), · · · , (xl , yl )} ∈ (X × Y )l

(1)

In the set above, xi ∈ X = Rn , yi ∈ R are the output of training sample. In the feature space, the LSSVM model can be represented as: f (x) = wT φ(x) + b

(2)

w is the weight vector, b is the offset vector, the high-dimension feature space is transformed by non-linear mapping function ϕ() : Rn → RH . According to the structural risk minimization principle, the objective function of LSSVM can be represented as: min J =

l 1 T γ  2 w w+ ei , i = 1, 2, 3 · · · l, 2 2

(3)

i=1

In the equation above, ei is the error variable, γ is the Regularization parameter which represents the penalty of error variable, when the Lagrange multiplier is introduced in

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the equation, the equation can be shown as follows: L = min J =

l l  1 T γ  2   T w w+ ei − λi w φ(x)i + b + ei − yi 2 2 i=1

(4)

i=1

In the equation above, λi is the Lagrange multiplier. The optimal λ and b can be obtained by KKT: ⎧ n  ∂L ⎪ ⎪ ⎪ = 0 → w = λi φ(xi ) ⎪ ⎪ ∂w ⎪ ⎪ i=1 ⎪ ⎪ ⎪ n ⎪  ⎪ ∂L ⎪ ⎨ =0→ λi = 0 ∂b i=1 ⎪ ⎪ ⎪ ∂L ⎪ ⎪ ⎪ = 0 → λi = γ ei ⎪ ⎪ ∂ei ⎪ ⎪ ⎪ ∂L ⎪ ⎪ ⎩ = 0 → ωT wT φ(x)i + b + ei − yi ∂λi The linear equations can be obtained after eliminating w and e;

  0 IT b 0 = I  + γ1 I α y

(5)

(6)

In the above function, λ = [λ1 , λ2 . . . , λl ]T , I = [1, 1, · · · , 1]T y = (y1 , . . . , yl )T , ⎡

⎤ K(x1 , x1 ) K(x1 , x2 ) · · · K(x1 , xl ) ⎢ K(x2 , x1 ) K(x2 , x2 ) · · · K(x2 , xl ) ⎥ ⎢ ⎥ =⎢ ⎥ .. .. .. .. ⎣ ⎦ . . .  .  K(xl , x1 ) K(xl , x2 ) · · · K xl , xl

(7)

According to the relevant theory of functional analysis, the function which can fit the Mercer can be used as the kernel function of support vector machine, the radial basis kernel function is selected as the kernel function, σ is the Gaussian kernel width:     x − xi 2 K x, x = exp − (8) σ2 The LSSVM prediction model can be obtained as: y=

l  i=1

λi K(x, xi ) + b

(9)

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3 Pso-Svr Line Model The SVR model parameter selection result is critical to the model prediction result. In order to obtain accurate prediction result, the PSO-optimization algorithm can be used to optimize the SVR parameters. PSO initialization particle is featured by position, speed and fitness, then the speed and position can be refreshed by personal best Pbest and global best Gbest .     k k (10) Vidk+1 = wVidk + c1 r1 Pid − Xidk + c2 r2 Pgd − Xidk Xidk+1 = Xidk + Vidk+1

(11)

In the above equations, d ranges from 1 to D.D represent the dimension of the search space; I ranges from1 to n, n represent the particle number in the population; k represent the current iteration time; ω represent the inertia weight; C1 and C2 represent the study factor; r1 and r2 represent the random number fix on interval [0,1]. The penalty parameter C, basis radial parameter σ , insensitive loss error ε are used as the particle in the PSO algorithm, the particle MSE calculated by the SVR model can be used as the fitness function to test whether the particle is good or bad. Refreshing particle speed and position until the error is minimized or the iteration times reach to the max, then the optimal solution which minimized the fitness function can be obtained, the SVR model parameter can be obtained by the PSO optimization algorithm.

4 Numerical Sample 4.1 Data Source We conduct the numerical example based on the momentum wheel electric current simulation data. The sample data length is 1300.We select the former 1000 data as the training set, the last 300 data are used as the test data. 4.2 Model Evaluation Index and Model Parameter In this paper, we RMSE and MAPE to evaluate the prediction performance of PSOLLSVM.   The parameters γ , σ 2 of LSSVM which is optimized by PSO algorithm can be shown in Table 1: Table 1. Model parameter Model

γ

σ2

PSO-LSSVM

10

12.231

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Fig. 1. Current fitness curve

Fig. 2. Prediction fitness curve

4.3 Fitness and Prediction Performance Evaluation The momentum wheel current can be fit by the PSO-LSSVM model, the fitness result curve can be shown in Fig. 1, the prediction result curve can be shown in Fig. 2,

Table 2. The accuracy of model Model

RMSE

Fitness curve

2.0641 × 10–5

Prediction curve

0.0078

MAPE 6.9 12.3

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The prediction and the fitness error can be shown in Table 2. The RMSE and MAPE of the fitness curve and prediction curve can be seen in the following table, the rmse and mape shows that the accuracy of the PSO-LSSVM is very high. 4.4 Reliability Function of Momentum Wheel We can see that the electric current value increase by the lifetime. The valve value of electric current is 1.3A, when the valve value is reached, the momentum can be deemed as failure, the lifetime of the momentum is over. We can find that the predicted life of momentum wheel is 10.3 year. The lifetime of the all 12 momentum wheels can be concluded in Table 3 and Table 4: Table 3. The lifetime of 1–6 momentum wheels No

1

2

3

4

5

6

Time

10.3

10.7

11.25

11.52

11.64

11.56

Table 4. The lifetime of 7–12 momentum wheels No

7

8

9

10

11

12

Time 11.78 11.95 12.21 12.32 12.68 12.89

The Weibull distribution is the most distribution to describe the life characteristics of momentum wheel. We select Weibull distribution to fit the reliability curve of momentum wheel. Using the lifetime data above, the probability density function of the momentum wheel can be obtained in Fig. 3, and reliability function of the momentum wheel can be obtained in Fig. 4: The reliability function and the probability density function of the set of momentum wheel can be shown as follows:     m  t m t m−1 (12) exp − f (t) = η η η   m  t R(t) = exp − (13) η In the functions above, m = 10, η = 12.2313.

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Fig. 3. Momentum wheel pdf

Fig. 4. Momentum wheel reliability

5 Conclusion In this paper, we propose a method to calculate the momentum wheel life. This method firstly find optimal parameter of PSO-LSSVM, then LSSVM is used to establish momentum wheel life model, finally, momentum wheel life is predicted in the numerical example, the result shows that the prediction model has good prediction accuracy and strong engineering applicability.

References 1. Wang, H., Zhang, Y.M., Zhou, Y.: A Reliability allocation method of CNC lathes based on copula failure correlation model. Chin. J. Mech. Eng. 31(6), 128–136 (2018)

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2. Zhao, S., Chao, J., Xiangyun, L.: Remaining useful life estimation of mechanical systems based on the data-driven method and bayesian theory. J. Mech. Eng. 54(12) (2018) 3. Xia, X., Chen, X., Liang, Y.E.: Dynamic prediction of friction torque performance reliability for satellite momentum wheel bearings. China Mech. Eng. 11, 12–19 (2019) 4. Li, H., Pan, D., Chen, C.L.P.: Reliability modeling and life estimation using an expectation maximization based wiener degradation model for momentum wheels. IEEE Trans. Cybern. 45(5), 969–977 (2015) 5. Haitao, L., Guang, J., Jinlun, Z.: Momentum wheel wiener process degradation modeling and life prediction. J. Aerospace Power 26(3), 622–627 (2011) 6. Liu, S.N., Lu, N.Y., Cheng, Y.H., Jiang, B., Xing, Y.: Remaining lifetime prediction for momentum wheel based on multiple degradation parameters. J. Nanjing Univ. Aeronautics Astronautics, 47(3), 360–366 (20l5) 7. Jiang, L., Cheng, Y., Yang, T., Jiang, B.: Remaining Useful Life prediction for the momentum wheel considering work condition switching (IEEE CGNCC). In: CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference, 7829065, pp. 1811–1816 (2017) 8. Cai, K., Jing, C., Di, Z., Shan, Z.: APU Fault Prediction Based on SVM Method. J. Nanjing Univ. Aeronautics Astronautics, 51(4), 466–473 (2019) 9. Zhou, J., Liu, Q., Jin, G., Sun, Q., Xi, M.: Reliability modeling for momentum wheel based on data mining of failure-physics. In: 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, 5432709, pp. 115–118 (2010) 10. Meng, X., Hua, S.: Fault prediction of satellite attitude control system based on neural network. J. Syst. Simul. 31(11), 2499–2508 (2019)

Design of Equivalent Simulation Test System for Universal Power Supply and Distribution of Space Station Multi-aircraft Xu-zhen Jing and Peng Ying(B) Institute of Spacecraft System Engineering, CAST, Beijing 100094, China [email protected]

Abstract. In the process of system-level test and verification of manned spacecraft, traditional power supply and distribution interface test and verification usually use a dedicated equivalent simulation system for special aircraft. The system state is solidified, the simulation method is single, the function is simple, and it cannot be adapted to the subsequent multi-model and extended functions verification. Aiming at the needs of power supply and distribution interface test verification in the current multi-cabin and multi-aircraft system-level test process of space station engineering, a universal, multi-spectrum-supported, and expandable multi-aircraft power supply and distribution equivalent simulation test system is proposed. The system adopts a modular design, combined with a reconfigurable software system, and deeply integrates software and hardware. Through a reconfigurable design, it has the ability to support the interface verification between multiple different aircraft, and it can quickly perform the new research aircraft on the ground. Effective verification. Established a general-purpose module combining software and hardware, with more comprehensive coverage (signal, power, step), higher automation, more detailed data recording, and verified by model testing, the system can quickly realize the interface with multiple types of manned spacecraft Simulation verification improves the parallelism of test and development, covers more failure modes, the system structure is reliable, and the cost is significantly reduced. Keywords: Space station · General purpose · Equivalent simulation

1 Introduction With the implementation of the third step of the manned spaceflight project, in the future construction stage of the space station, the number of launches of various manned spacecraft such as various sections of the space station, manned spacecraft, and cargo spacecraft will increase sharply. It has become the norm. The development cycle and test cycle of the spacecraft will gradually shorten, and the mission function of the spacecraft will gradually expand. Therefore, the functional coverage, adaptability, generalization, long life, research and development of the power supply and distribution test equipment © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 731–739, 2023. https://doi.org/10.1007/978-981-19-9968-0_88

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required for spacecraft testing will be Efficiency and other aspects have put forward higher requirements. The spacecraft equivalent simulation system is an equivalent simulation device that simulates the electrical performance of the spacecraft through the circuit and the instrument system, such as regulated power supply, ground telemetry, pyrotechnic access, and hard-wired command execution [1]. It can realize the effective simulation of spacecraft energy system, power distribution system and some information systems. Generally, the power supply load, downlink analog quantity, state quantity and pyrotechnic command of the aircraft are simulated before the electrical performance test of the spacecraft, and at the same time, the uplink control signal of the ground equipment is received, and the uplink and downlink paths are checked, so as to verify the state quantity measurement loop of the aircraft and the ground equipment., the correctness of the analog measurement loop, the command control loop, and the correctness of the function of the ground equipment. The current equivalent simulation systems are dedicated to each model, and are used to simulate the aircraft power supply load, downlink analog quantity, state quantity, and pyrotechnic commands, and at the same time receive ground equipment control signals for path detection [2]. The common problem of the interface verification test system between traditional manned spacecraft is that it is difficult for ordinary users to change its relatively fixed functions after the construction is completed, and the special plane is dedicated, which cannot meet the diversity test. The developed test system matches the interface of a single type of aircraft, is incompatible with each other, has poor scalability, lacks generalization and modularity, and cannot share software and hardware components, which not only reduces the development efficiency, but also improve complex test system expensive. The traditional power supply and distribution simulator mainly completes the matching check of the static interface, lacks modeling and simulation of the dynamic process of the aircraft power supply and distribution system, and cannot analyze and evaluate the interface characteristics at the same time as the equivalent simulation. This paper proposes an equivalent simulation system for manned spacecraft based on general modularization. This system can be applied to the multi-type spectrum testing requirements of manned spacecraft in space station engineering. Modeling the multitype spectrum of human and spacecraft, fully integrating software and hardware to improve the dynamic equivalent simulation performance, with the ability to analyze interface characteristics, to complete the full test of the power supply and distribution test system, and to evaluate the dynamic performance and indicators of the system, which can effectively reduce the time cost brought by equipment development, and this set of system equipment is more modular and generalized, which is convenient for equipment maintenance and replacement; the equipment is miniaturized and has a higher degree of integration.

2 Equivalent Analog System Design As an important part of the ground power supply and distribution test system, the spacecraft equivalent simulation system [3] is generally dedicated to the model, and is managed as the special equipment of the model. At present, there is research on the degree

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of automation of the equivalent simulation system of the spacecraft, but there is less research on the generalization of the equivalent simulation system, and less research on the dynamic characteristics of the simulated object for the equivalent simulation system. Insufficient. Improving the repetition rate, dynamic simulation performance and signal analysis capability of the equivalent analog system can effectively improve the test efficiency of the model. Using general reconfigurable design ideas and modular design methods to design the system, its hardware modules or (and) software modules can reconfigure (or reset) the structure and algorithm according to the changing data flow or control flow [4]. In a reconfigurable system, hardware information (configuration information of programmable devices) can also be dynamically invoked or modified like software programs. This not only retains the performance of hardware computing, but also has the flexibility of software. The general power supply and distribution equivalent simulation system has the following technical characteristics: 1) Ability to efficiently implement specific functions. Reconfigurable logic devices are all hardwired logic, which changes the function by changing the configuration of the device. 2) The device configuration can be dynamically changed to flexibly meet the needs of various functions. 3) The system cost can be greatly reduced. In addition, for functions that will not be used at the same time, consider using dynamic reconfiguration technology to implement them separately in different demand periods, so as to achieve “multiple uses for one machine”, saving resources, space and costs. A typical reconfigurable, modular test system composition is shown in Fig. 1.

Spacecra under test Mul-source data collecon

Real-me processing module

Analog smulus module

Reconfigurable configuraon

data analysis

Fig. 1. Reconfigurable and modular test system composition

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2.1 Modeling of General Power Supply and Distribution Equivalent Simulation System Through the analysis of the static technical state and dynamic process characteristics of hardware interfaces such as load characteristics, hard-wire commands, state telemetry, and pyrotechnic actions during the in-orbit flight of multiple manned vehicles in the space station. Although the power supply voltage system, load attribute and power, signal path definition, telemetry definition, and signal characteristics are different for each aircraft, by summarizing and analyzing the interface circuit, signal logic, and signal waveform of each aircraft, it is possible to identify the power supply for each aircraft. The general key elements of the power distribution equivalent simulation are analyzed and designed to form a general equivalent simulation system model. The model includes all elements of the test and verification of the power supply and distribution interface of the existing space station aircraft. Based on the model, forward design can be carried out to match the aircraft-to-ground interface, and then the design and configuration status of the equivalent simulation system can be determined in one step.

Fig. 2. Power supply and distribution equivalent simulation system model

2.2 Equivalent System Hardware Design Through the analysis of the equivalent simulation system model, the hardware composition of the equivalent system is modularly designed, and a single module is designed universally. Each module adopts a common standard interface, which is easy to replace and expand. The equivalent simulation system is based on the universal design. In the above, the overall system has been modularized. The overall system adopts the mode

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of motherboard + functional daughter board, and has carried out a universal design for the interface characteristics of the spacecraft’s multi-type spectrum, and has the ability to expand functions. The equivalent simulation system based on modular instruments consists of six parts: control unit, general power supply and load module, jumper box, conditioning box, pull-out display, and cabinet. The system integration diagram is shown in Fig. 3.

Fig. 3. Schematic diagram of equivalent device system architecture

In order to meet the verification requirements of various scenarios, the control unit is configured in the maximum modular way, and the board configuration can be flexibly adjusted according to the task requirements, so as to realize the expansion of system functions such as analog quantity, state quantity and open command., has the technical characteristics of high integration, good real-time performance and strong scalability [5]. The equivalent device signal conditioning box completes the conditioning and expansion of the analog signal collected by the wired front-end equipment, and at the same time, the function of collecting the command signal issued by the wired front-end equipment can be realized through the relay conditioning circuit. The conditioning box no longer adopts the special design method for special machines. The signal conditioning module is designed with a general circuit module, and the conversion of various functions is realized through jumpers. The structure of the signal conditioning box adopts the pluggable design of the sub-mother board and the generalized design of the maximum envelope, which can meet the functional requirements of the signal conditioning box of multiple aircrafts at the same time. According to the modeling and analysis of the voltage pulse commands sent by the wired front-end equipment, the types of voltage pulse commands can be divided into the following types:

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a) Wired front-end command power supply on-off type command; b) Equivalent command power supply on-off type command; c) reset type instruction;

Fig. 4. Principle diagram of voltage pulse command signal acquisition

Based on the modeling and analysis of the contact type instructions sent by the wired front-end equipment, the contact types can be divided into the following types: a) b) c) d)

Wired front-end command power supply command; Equivalent command power supply command; The normally open contact closes a valid command; The normally closed contact disconnects the valid command;

Fig. 5. Schematic diagram of contact type command signal acquisition

Based on the analysis of the signal model of the command, seven classic interface cables are summarized. Through the isolation, conditioning, gating and merging of the interface circuit, a general circuit can be used to adapt to a variety of command interfaces. 2.3 Software Design The software of the equivalent simulation system has a clear function division in the top-level design, and the execution of the functional modules is independent of each

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other, so as to avoid the interference between each module in the program operation [6]. If a module needs to be revised, you can directly modify the corresponding module, which is convenient for maintenance. There is no need to modify the overall software architecture, which shortens the cycle of secondary development. The network communication module mainly realizes the communication between the equivalent simulation system and the front-end devices, receives the command information sent by the front-end settings through the network, and feeds back the equivalent response results. The external structure of the network communication module is a While loop structure, and the internal structure is a conditional selection structure. According to the communication protocol, it initiates a connection to the designated port of the designated IP, receives the instruction information of the destination IP, and feeds back the execution information and various digital collection information. This module simulates the response of the aircraft to hard-wire commands. The software module adopts a sequential loop structure design, controls the hardware module through function calls, reads the measurement results, and reads the collected hardwire command voltage, pulse width, and command polarity. And the collected state is displayed visually, and the action state and signal waveform are displayed. The collection results are stored in the format of time, instruction name, and data result. The pyrotechnic action test module realizes the acquisition, recording and display of the signal waveform of the pyrotechnic state quantity of the front-end equipment. Adopt While loop structure design, loop waiting and identify instruction information. In the front panel setting options, the pyrotechnic commands and corresponding polarities can be configured in tabular format, with command storage and query functions, including information such as name, time, signal waveform, polarity position, etc., which can be flexibly expanded with hardware sampling boards. The load control module uses the script design method to design the load characteristics of the key working sections of the aircraft based on the equivalent model, including steady-state load, load step, load characteristics of the sunlit area and shadow area, form a response script, and test different load characteristics Under the power supply characteristics, and collect and record information including voltage fluctuation, current fluctuation, fluctuation rate and so on. The automatic test module realizes the automatic test of wired command function, analog and digital quantity acquisition, load dynamic change, and pyrotechnic state quantity collection. The test is initiated by pressing the button, and the test is sent one by one according to the content of the configuration module. The software designs a data analysis module. The information collected and output by the simulator can be quantitatively analyzed, including state out-of-limit information, signal slope, frequency domain information, signal jitter, action time, etc., to facilitate rapid analysis of test data, and data analysis results can be displayed in the form of curves It is displayed, and the automatic generation of the report of the tested data is completed. The main software interface functional area includes network connection area, instruction detection display, pyrotechnic simulation test area, load simulation test area, automatic script test area, data analysis and report function area. In the process of software writing, a generalized design is adopted, and the generalization of the software is realized by changing the configuration file. The configuration content of the parameter

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instruction includes the configuration of the network connection of the test model, the configuration of the PXI board, the configuration of the parameter channel, the configuration of the parameter The configuration of the instruction test content. It includes functions such as parameter calling, simulation control, parameter real-time monitoring, and simulation process input, and can configure simulation mode, simulation time, and initial state.

3 System Application and Verification Based on the power supply and distribution equivalent system built by reconfigurable modules, the power supply interface and power supply and distribution equipment of multiple modules of the space station are simulated and verified. Firstly, according to the equivalent device model, the power supply interfaces and power supply and distribution parameters of the multiple cabins under test are structured and organized to form a configuration file, which is imported into the simulation system in batches, and the power supply and distribution equipment of each cabin is verified in turn. It has been verified that the system realizes rapid quantification of technical status on the basis of model analysis, and can seamlessly verify the interfaces and equipment of multiple cabins. Power characteristics and frequency domain characteristics are displayed in the form of visualization. A general, fast, comprehensive and deep equivalent simulation test is realized. After testing, verification and review, the system can effectively cover the interfaces of the tested object, cover normal and fault conditions, cover the power supply mode, and can quickly reconfigure.

4 Conclusion This paper expounds the reconfiguration design method of the multi-spacecraft equivalent simulation system from the aspects of model, architecture, hardware and software. High. The simulation verification shows that the it has the ability to simulate real spacecraft, can be quickly configured, and simulate various interfaces between spacecraft, and can set failure modes. A unified interface is established, which reduces the difficulty of development and significantly reduces the cost. Solve the problem of waste of resources of the test system and high production and maintenance costs caused by complex test objects, many test equipment and low test resource utilization. It has important value for the development of space rendezvous and the technical verification of combined operation.

References 1. Chen, H., Ye, Y., Shen, S.: A measurement system equivalent device design Comput. Measurem. Control 20(5), 1414–1919 (2012) 2. Liu, X., Wang, M., Jiang, H., et al.: The design and realization of an equivalent device of an intelligent pyrotechnic device based on FPGA. Electronic Measurem. Technol. 35(5), 78–82 (2012)

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3. Yu, J., Yan, S., Liu, X., Liu, W., Gao, J.: Design of modularization and generalization equivalent test system. Electr. Dev. (10), 1178–1184 (2012) 4. Zhang, H.: Hardware modularization and software configuration design ideas of spacecraft power supply and distribution test equipment. Spacecraft Eng. 01, 72–76 (2010) 5. Men, B., Zhang, R., Zhou, N.: Universal design of spacecraft power supply and distribution test system. Aerospace Measurem. Technol. (12), 57–62(2018) 6. Su, H., Shen, S., Liu, W., et al.: Design of multifunctional equivalent device based on USB and FPGA. Television Technol. 36(23), 50–54 (2012) 7. Li, L.: Interface design technology of satellite power supply and distribution test equipment. Spacecraft Eng. 01, 66–72 (2002) 8. Zhang, L., Sun, N., Yu, H., et al.: The design and realization of satellite integrated test system based on PXI. Comput. Measurem. Control 16(1), 27–29 (2008) 9. Zhang, F., Chen, L., Jing, X.: Design of automatic test equivalent device system for manned spacecraft based on PXI modular instrument. Comput. Measurem. Control 22(6), 1671–4598 (2014) 10. Chen, X., Zhang, Y.: LabVIEW8.2 programming from entry to master, pp. 56–80. Tsinghua University Press, Beijing (2007)

Design and Verification of a Geographic Information Prediction Scheme for Autonomous Mission Planning of Satellites Rina Wu(B) , Jie Liu, Tao Zhang, Jianmin Zhou, and Chao Chen Beijing Institute of Control Engineering, Beijing 100190, China [email protected]

Abstract. This paper presents a geographic information prediction scheme for autonomous mission planning of satellite, which can predict the satellite orbit and the geographic state of sub-satellite point. During autonomous mission planning, the satellite loads can be automatically activated in sunshine area, domestic area and land area, so that the resources can be utilized with maximum efficiency. The results of system verification showed that the longitude, latitude and geographic information (sunshine area/shadow area, domestic area/overseas area, land/ocean) of the sub-satellite point predicted by proposed scheme are consistent with the simulation data. This paper demonstrates how accurate geographic information can support autonomous satellite mission planning. Keywords: Geographic information · Prediction · Autonomous mission planning

1 Introduction The mission planning of earth observation satellites plays a key role in arranging image information acquisition, ground data reception and optimization of the observation system [1]. However, the traditional ground-based mission planning does not have enough time to adjust the planning scheme accordingly when the observation conditions change, resulting in the failure to maximize the observation efficiency. As a consequence, the overall resource utilization of the system is low [2–4]. In this case, autonomous mission planning has become an important research direction of earth observation satellites. On the basis of ensuring the satellite resource capability and meeting various constraints, autonomous mission planning needs to make decisions on when to observe, which targets to observe, and the start and end times of data acquisition activities and data download activities corresponding to observation tasks. Acquiring spatial geographical information can provide good information support for the completion of autonomous mission planning, so as to ensure the maximum utilization of satellite resources.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 740–745, 2023. https://doi.org/10.1007/978-981-19-9968-0_89

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2 Geographic Information Prediction Algorithm 2.1 Orbit Prediction To realize the geographic information prediction, the first step is to predicting the Orbitthe geographic longitude L and latitude δ of the sub-satellite point at time t. First, through the orbit elements extrapolation algorithm [5–7], the position (x, y, z) of the satellite in the J2000 inertial frame can be obtained. According to the position (x, y, z) and precession-nutation matrix CPN , the geographic longitude L and latitude δ of the sub-satellite point in the WGS84 coordinate system can be calculated as following: ⎡ ⎤ ⎡ ⎤ x x1 T ⎣ ⎦ ⎣ y1 ⎦ = CPN (1) y z1 z X84 = cos(λIG + We (t − t0I ))x1 + sin(λIG + We (t − t0I ))y1

(2)

Y84 = − sin(λIG + We (t − t0I ))x1 + cos(λIG + We (t − t0I ))y1

(3)

Z84 = z1

(4)

In the above equations, t0I is the reference time of the injected orbit, λIG is the Greenwich hour angle of the injected orbit, and We is the rotation speed of the earth. Then the geographical longitude L and latitude δ can be obtained: L = arctan(Y84 /X84 )

(5)

 2 + Y 2 + Z2 ) δ ∗ = arcsin(Z84 / X84 84 84

(6)

δ = arctan(tan(δ ∗ )/(1 − fE )2 )

(7)

where δ ∗ is the geocentric latitude, fE = 1/298.257 is the oblateness of the earth. 2.2 Prediction of Sunshine Area/Shadow Area According to the orbit elements extrapolation algorithm [5–7], projection of the solar vector on orbital plane (Sox , Soy , Soz ) can be obtained. The method is given below: ⎤ ⎡ ⎤ cos(sun ) cos(usun ) − sin(sun ) cos(isun ) sin(usun ) Sox ⎦ ⎣ Soy ⎦ = Coi⎣ sin(sun ) cos(usun ) cos(isun ) sin(usun ) Soz sin(isun ) sin(usun ) ⎡

(8)

Here Coi is the Direction cosines matrix of the orbital coordinates to inertial coordinates, sun is the right ascension of the ascending node, uSun is the argument of perigee, and isun is the inclination, respectively. When arccos(Soz ) < 90°, the sub-satellite point is in the shadow area, otherwise it is in the sunshine area.

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2.3 Prediction of Domestic Area/Overseas Area and Land/Ocean Firstly, the land map is divided into regions of Asia, Europe, Africa, Australia, North America, South America, Antarctica and other parts. The map of China and the land surface model map of each part are separated by equal longitude L into many longitude strips. Each longitude strip is marked with the upper limit and lower limit of the geographical latitude of the effective area. A straight line is used to connect the geographical latitude boundary between the two adjacent strips to form a trapezoidal area. The schematic diagram is shown in Fig. 1.

Fig. 1. Schematic diagram of map division mode

Then each part of the map is pre-installed according to the boundary information of the surface model. The pre-installed one-dimensional array Lon_i represents the longitude strip of the ith region, the one-dimensional array LatMin_i gives the lower limit of latitude in the ith region, and the one-dimensional array LatMax_i marks the upper limit of latitude in the ith region. For areas with complex surfaces, Lon_i at the same longitude boundary point is described by multiple sets of geographic latitude ranges. In order to facilitate the on-orbit modification and adjustment of the pre-installed surface model map, 10° of longitude strips are expanded on the east and west sides of the actual map area as the reserved area (the upper limit of latitude is 100° and the lower limit is 90°). In the next step, the range of geographic longitude L is determined: a) Taking N as the length of Lon_i, at the condition of L in Lon_i[1] ~ Lon_i[N], the geographic longitude L in Lon_i position num in the array can be located by: Num = int((L − Lon_i[1])/L) + 1

(9)

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b) Linear interpolation method is used to calculate the latitude range Lat_min_j and Lat_max_j: Lat_min_j = LatMin_i [Num] + ( LatMin_i [Num + 1] − LatMin_i [Num]) ∗ (L − Lon_i[Num])/L (10) Lat_max_j = LatMax_i [Num] + ( LatMax_i [Num + 1] − LatMax_i [Num]) ∗(L − Lon_i[Num])/L (11) c) The range of the geographic latitude δ can be determined based on the following condition: if δ is located in Lat_min_j ~ Lat_max_j, δ is determined as the domestic area. The land or ocean can be distinguished according to the pre-installed land map as well following the same algorithm as in the domestic/overseas prediction logic, but it needs to calculate the upper and lower limits of multiple groups of geographic latitudes where multiple ranges are required.

3 System Verification Under the control subsystem of a remote sensing satellite, the geographic information prediction method is tested and verified in this paper. The control subsystem is composed of sensor components, the controller and the actuator. The system structure diagram is shown in Fig. 2.

Fig. 2. System composition diagram

An LEO satellite orbiting along a sun-synchronous orbit at an altitude of 500 km with a local time of descending node of 10:30 A.M. is selected for the system. The system can predict the geographic longitude and latitude 90 min after the present moment.

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According to the pre-installed map, the geographic information (sunshine area/shadow area, domestic area/overseas area, land/ocean) of the sub-satellite point can be predicted. The sunshine area/shadow area, domestic area /overseas area, land /ocean is indicated by F_SunArea90 = 1/0, F_China90 = 1/0, and F_Land90 = 1/0, respectively. The geographical longitude and latitude predicted by the system after 90 min of one orbit period are shown in Fig. 3. Figure 4 shows the error between the actual predicted longitude and latitude and the simulation data.

Fig. 3. Geographic longitude and latitude predicted by the system after 90 min

Fig. 4. The error between actual predicted longitude and latitude and simulation data

The geographic information (sunshine area/shadow area, domestic area/overseas area, land/ocean) of the sub-satellite point predicted after 90 min during one orbit period are shown in Fig. 5.

Fig. 5. Predicted results of sunshine area/shadow area, domestic area/overseas area, land/ocean after 90 min

According to the system test results, the comparison accuracy between the longitude and latitude of the sub-satellite point extrapolated for 90 min and the simulation data is less than 0.1° in the steady state, and the comparison accuracy near the singularity is about 0.2°. The prediction results of sunshine area/shadow area, domestic area/overseas area and land/ocean are completely consistent with the simulation results.

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4 Conclusion In this paper, a geographic information prediction scheme for satellite autonomous mission planning is proposed. The system verification results show that the scheme can predict the geographic longitude and latitude as well as the geographic information such as sunshine area/shadow area, domestic area/overseas area, and land/ocean of the subsatellite point. When the satellite carries out autonomous mission planning, it can ensure that the satellite loads can be turned on accordingly in the sunny area, in the domestic area and in the land area, so that all resources can be utilized efficiently.

References 1. Sun, C., Wang, X., Liu, X.: Distributed satellite mission planning via learning in games. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 4381–4386 (2018) 2. Xu, Y., Zhou, J., Yin, J., et al.: Review of mission planning strategies and applications of earth observation satellites. Radio Eng. 51(8), 681–690 (2021) 3. Feng, Z., He, T., Zhu, Y., Hu, H., Zheng, Z.: Design of mission planning system for remote sensing satellite. Aerosp. Control. 39(2), 17–23 (2021) 4. Jiang, W., Hao, H., Li, Y.: Review of task scheduling research for the earth observing satellites. Syst. Eng. Electron. 35(9), 1878–1885 (2013) 5. Liu, L.: Spacecraft Orbit Theory. National Defense Industry Press, Beijing (2000) 6. Liu, L., Wang, Y.: An analysis method of satellite orbit prediction. Acta Astronom. Sin. 46(3), 307–313 (2005) 7. Zhang, R.: Satellite Orbit Attitude Dynamics and Control. Beijing University of Aeronautics and Astronautics Press, Beijing (1998)

Space Technology Session 2

Bit Synchronization Verification Strategy for High Orbit Navigation Receiver Mengdan Cao(B) , Yu Chen, Yukui Zhou, Zhaoqiang Cheng, Yu Peng, and Dongsheng Shi Beijing Institute of Control Engineering, Beijing, China [email protected]

Abstract. The navigation receiver needs to go through multiple processes of carrier synchronization, code synchronization, bit synchronization, and frame synchronization to obtain the navigation message and pseudo-range before calculating the satellite position. For the bit synchronization part, the calculation itself has a certain percentage of error rate without relevant self-check solutions. Once the bit synchronization position is wrong, the impact on the positioning result of the navigation receiver is fatal without auxiliary verification. This paper proposes two methods of bit synchronization verification: bit synchronization verification method based on NH code information for BD, and a general bit synchronization verification method based on converted time for both GPS and BD. These two methods generate a joint verification mechanism to check the correctness of the bit synchronization position, preventing the positioning result from being wrong due to the bit synchronization error. In the high-orbit synchronous orbit scenario, the above-mentioned bit synchronization verification method is used to test and verify on a semi-physical simulation platform. The experimental data shows that the proposed methods can effectively avoid the situation of positioning errors and ensure the reliability of the positioning solution results. Keywords: High orbit · Navigation receiver · Bit synchronization check

1 Overview For the high-orbit weak signals, prolonging the coherent integration time is a common method to improve the signal-to-noise ratio of high-sensitivity receivers [1]. The processing of navigation messages is generally divided into acquisition, tracking, and calculation based on the baseband. The tracking part mainly obtains the navigation message data and ephemeris information through multiple synchronization stages [2]. The tracking part includes multiple processes of carrier synchronization, code synchronization, bit synchronization, and frame synchronization. Every process can impact the efficiency of the navigation receiver and the correctness of the calculation [3]. The carrier synchronization and code synchronization track the satellite’s Doppler frequency and code phase through the carrier and code loop. These two parts have a decisive influence on the efficiency of tracing the satellite [4]. The carrier loop and the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 749–758, 2023. https://doi.org/10.1007/978-981-19-9968-0_90

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code loop are processes of continuously calculating the error feedback amount. At the same time, the results of frequency and phase identification can be used to monitor the quality of the loop track. Once an abnormality is found, satellites can be eliminated from the calculate group. Therefore, the synchronization results of the carrier loop and the code loop are continuously calculated and the error of the calculation results can be quantified. Such a loop synchronization characteristic makes even an error (such as loss of lock) can affect the receiver’s efficiency, but has a limited impact on the correctness of the receiver’s position calculation [5]. The frame synchronization part is to find out the boundary between sub-frames in the navigation message to extract the subframe message. During frame synchronization, the sub-frame header will be detected first and the CRC frame check will be performed on the obtained sub-frame message, so the correctness of the frame synchronization result also has a detection mechanism. For the bit synchronization part, the purpose of the bit synchronization is to determine the demarcation between the navigation data bits. According to the respective navigation message structures of GPS and BD, the position of GPS C/A code and the position of Beidou Neumann Hoffman (hereinafter referred to as NH) code at the beginning of the bit are estimated by the bit synchronization algorithm to achieve the division of each bit in the data stream. Lay the foundation for further processing of navigation message data. On the one hand, the actual position of the navigation message data stream does not change, so after the bit synchronization position is found, the subsequent processing is no need to calculate. On another hand, unlike the carrier and code loop which can monitor the synchronization quality through loop tracking errors, and unlike the frame synchronization link which can use the check data bits that come with the subframe to perform self-checking. Therefore, there is no simple and direct self-checking mechanism for the synchronization result in the bit synchronization part. Once the bit synchronization position is calculated incorrectly, it means that there is a deviation in the satellite pseudo-range latch time, which will bring about a pseudo-range deviation of 1-ms or even an integer multiple of it (the amount of pseudo-range conversion time). Moreover, this wrong bit synchronization position does not necessarily make the position of the navigation data bits wrong (that is, the navigation data bit separation may still be correct in the dimension of the sub-frame of the message), so the frame synchronization and frame check are still possible to pass normally. Finally, the receiver is made to incorporate the satellite with the wrong pseudo- range calculation into the calculation group, which directly leads to an error in the position calculation of the navigation receiver. Therefore, it is an important guarantee for the correctness of the calculation of the navigation receiver to check the calculation results of the bit synchronization and to find and eliminate the satellites whose position of the bit synchronization is wrongly calculated in time, which is of great significance in engineering practice.

2 High Orbit Bit Synchronization Method 2.1 Common Bit Synchronization Method The histogram method [6] means that the data stream is grouped by 20 ms, and the symbol transition of two adjacent 1 ms data is counted. Since the symbol transition only occurs at the bit edge, the data bit edge can be judged according to the statistical results.

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On the one hand, there is a certain proportion of bit demodulation errors in this method. On the other hand, when the carrier-to-noise ratio in high orbits is low and the dynamics are strong, the bit error rate or bit synchronization error rate will be higher, although the receiver is tracking the satellite signal, GPS positioning could not be achieved for a long time. The following describes the bit synchronization method adopted by the high-orbit navigation receiver in detail. 2.2 High-Orbit GPS Bit Synchronization Method Read the immediate correlation PROMPT_In (real) and PROMPT_Qn (imaginary) value (n is the nth ms), note: BITn = PROMPT_In ∗ PROMPT_In+1 + PROMPT_Qn ∗ PROMPT_Qn+1  BUF[n] = 9N=0 BIT20N+n , n ∈ [0, 19] After reading the 200 ms real-time related data, find the minimum value in BUF[n], n ∈ [0, 19], and the serial number n = 0 corresponding to the minimum value indicates the position of the current bit synchronization. It can be calculated continuously many times, and if the calculation results are consistent, it is considered that the bit synchronization position [7, 8] has been found. Even after finding the bit synchronization position, the calculation of the bit synchronization position can also be continued while the subsequent frame synchronization is being performed. Once the calculation results are inconsistent, it means that the bit synchronization position calculation is wrong, and the satellite needs to be abandoned in the positioning calculation [9–11]. 2.3 High-Orbit Beidou Bit Synchronization Method The receiver architecture based on this paper is FPGA+DSP. FPGA is responsible for the capture part and the Beidou (BD) bit synchronization part, DSP is responsible for the tracking and positioning solution except the BD bit synchronization. In BD capture part, FPGA conducts correlation calculation according to all possible combinations of C/A code position, frequency and NH code position, and feeds the combination with the largest correlation value to DSP as C/A code position, frequency and NH code position. The specific process is as follows: 1) Approximate code phase and approximate frequency interval are obtained by coarse capture, which are used as initial code phase and initial frequency for subsequent search; 2) Considering a number of (1/4) chip positions on the left and right sides of the center of the initial chip position as the chip position set, and a number of frequency intervals on the left and right sides of the center of the initial frequency as the frequency set; 3) Consider all 39 possible NH codes including (0/1) ambiguity;

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4) Extracting each element from the code chip set and frequency set as combination (C/A code and frequency), and proceeding relevant operations with local signals to accumulate 1-ms data. Then repeat in the same way to obtain continuous 20 independent 1-ms data. 5) Extracting an NH code case from the NH code set and multiply it by the relevant data in 4) bit by bit, and then accumulate them as the stripping process of NH codes. The final cumulative result is the relevant value of the current 3-dimensional combination (C/A code, frequency, NH code). 6) Go through and calculate the correlation value of all the 3-dimensional combinations (C/A code, frequency and NH code), and the combination with the largest correlation value is selected as the result of C/A code, frequency and NH code and fed back to DSP. Under the condition of weak signal in high orbit, the bit synchronization position may be wrong by several bits, and since 20 ms bits constitute a bit, when the bit synchronization position is wrong by several milliseconds, the resulting bit (from 0/1) may still be correct, which means frame synchronization and frame verification may still pass. Therefore, it is necessary to verify the bit synchronization position.

3 Bit Synchronization Check Method 3.1 Beidou Bit Synchronization Verification Method Based on NH Code Information For GPS, the general strategy of searching for bit hopping edge based on conjugate difference mode has been proved to be efficient in engineering practice. In order to prevent the small probability of error, the accuracy of GPS bit synchronization can be further ensured by simply calculating the position of the bit synchronization and comparing it with the existing position. Size and calculate according to the section on BD NH process, the BD bit synchronization position is as a combination with C/A code and frequency search, along with all the GPS synchronous calculation is after have frequency and code phase locked in the tracking loop. Different from GPS computing bit synchronization when there is only one uncertainty of bit synchronization position and no other interference, the C/A code and frequency are also uncertain at BD procedure. It is the uncertainty of these two dimensions, especially the frequency, that leads to possible errors in the search method based on the maximum correlation value. As shown in Fig. 1, a certain degree of frequency offset can significantly reduce the relevant results of accurate NH codes, so that the correct NH codes do not reflect the maximum correlation peak, resulting in the position error of NH codes. At this time, without carrier ring locking, the frequency is likely to have a degree of deviation sufficient to interfere with the correlation of NH codes. Figure 2 further shows the correlation of different NH code positions. It can be seen that once

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a bit synchronization error occurs and the frame synchronization passes, the NH code position in the synchronization tends to deviate more than one or two milliseconds from the actual position. Therefore, it is necessary to perform a second check on the BD bit synchronization position given by FPGA on DSP. So if the FPGA is required to repeat the three-dimensional search (C/A code, frequency, NH code), it will not only increase the resource overhead, but also the repetition of the same strategy will be difficult to achieve the small probability of bit synchronization position error by simple repeat calculation of GPS because of the uncertainty of other dimensions. In engineering practice, it is also found that once the position of FPGA NH code is wrong, it is difficult to correct.

Fig. 1. Frequency and integral time normalized Sinc function

Fig. 2. Correlation value of different NH code positions

The specific BDS synchronization verification method proposed in this paper is to store 20 NH code symbols within a bit length at a period of 20 ms with the beginning of obtained NH code positions between bits. Check 20 continuous NH codes in the bit length by using the positive and negative polarity characteristic of only two NH codes in the BD2 generation (BD2) message, and check whether the polarity of 20 continuous NH codes starting from the current bit synchronization position meets the polarity requirements of NH codes in BD2. (The positive polarity of NH codes in BD2 message is expressed in hexadecimal as follows: 0x04D4E, negative polarity in hexadecimal should be: 0xFB2B1). In particular, considering the possible ambiguity in demodulation, and

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taking advantage of the positive and negative polarity of the NH code of the BD2 message, the verification is considered to pass if either polarity is observed. At the same time, in the case that the effective information component does not fall in the I channel but mainly concentrates in the Q channel, this check can be carried out on both the I channel and the Q channel at the same time. If any road conforms to any polarity, it can be regarded as the re-check of the bit synchronization position. In practical engineering, the verification can be repeated several times to further increase the reliability of the bit synchronization position. The above bit synchronization verification method can eliminate the satellites with wrong bit synchronization position before positioning, and ensure the validity of the initial positioning solution result of the receiver. 3.2 GPS and BD Universal Bit Synchronization Detection Mechanism Based on Converted Time Once the receiver calculates the position, the distance between the current time-shot satellite position and the last time-shot receiver position can be calculated, plus the pseudo-range corresponding to the current time-shot satellite, and the distance value is divided by the speed of light to further obtain the corresponding amount of time. Use the converted time to verify the satellite bit synchronization calculation result: the converted time should be equal to the corresponding TIC time interval (that is, the time interval between two locked pseudo ranges). If it is within the range, send the satellite position solution results and other data to the receiver’s position solution equation; if it is not within the range, give up the satellite in the positioning solution. The principle is described in detail below according to Fig. 3:

A

B

the moment of the satellite signal is TIC1 transmitted

C the moment of the satellite signal is transmitted

D TIC2

Fig. 3. Schematic diagram of GPS and BD universal bit synchronization detection mechanism based on converted time

As shown in Fig. 3, point B and point D are the moment when the pseudo-range is locked, respectively denoted as TIC1 and TIC2, and point C is the moment when the satellite signal is transmitted. Point C to point D is the distance from the time of satellite signal transmission to the time of satellite signal reception, that is, the pseudo-range value of the satellite at the time of TIC2. Point C is the launch time of the satellite, which is the launch time used to calculate the satellite position in the current time shot. Point B is the moment when the pseudo-range of the last time shot is locked, which is the calculation time of the receiver position of the last time shot. Therefore, when point B reaches point C is the time conversion of the distance from the last time-shot receiver position to the current time-shot satellite position. In this way, from point B to point C

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plus point C to point D is equal to point B to point D (that is, the time interval between two locking pseudo ranges). If it is calculated that the time from point B to point C plus point C to point D deviates from the time interval of the locked pseudo-range by more than 1 ms, we can suspect that the satellite’s bit synchronization position is incorrectly calculated, and the bit synchronization position deviates by at least 1 ms or above, this satellite cannot participate in the receiver positioning solution equation. This method is used for GPS satellites and BD satellites and is a general bit synchronization check method. We assume that the result of the receiver’s initial positioning calculation is correct. For satellites that are captured and tracked after that, this method can be used to check whether the calculation of the position synchronization position is reasonable, and immediately eliminate the satellites that may cause the positioning calculation error to ensure the reception. The machine positioning solution results remain valid. In addition, this paper proposes a method of indirect check bit synchronization based on the change of partially calculated items. The navigation receiver uses the Newton iteration method to obtain the clock offset (the last term of the position solution) in addition to the calculation of the position term, and the clock drift (the last term of the speed solution) can be obtained in addition to the calculation of the velocity term. Although these two quantities are common error quantities for each satellite, that is, the clock error itself will not affect the position calculation, and the clock drift will not affect the speed calculation. However, these two quantities should show a generally stable and gradual change during the calculation process. Therefore, after the stable calculation, once the clock error and clock drift suddenly jump in the positioning process, especially When a new satellite is added, it likely means that the input quantities, including satellite pseudo-range observations and Doppler frequencies, are in error. Considering that the position of the in-position synchronization directly determines the amount of pseudorange observations, if a new satellite is added and the two common errors, especially the clock error, have changed significantly compared with the previous solution, then there is reason to suspect that at this time The bit synchronization position is wrong, which is the mechanism mentioned in this paper to indirectly check whether the bit synchronization position may be a problem based on the change of the partial solution item. The judgment of the specific variation range needs to be determined in engineering practice according to the specific scene and specific accuracy requirements, especially the clock drift itself is also related to the receiver hardware clock, so this paper only proposes here, the two common errors can be monitored. Changes to indirectly measure whether the bit synchronization position is in error.

4 Test Verification In order to verify the bit synchronization checking method, this paper uses a certain type of satellite navigation receiver to build a semi-physical simulation verification system, as shown in Fig. 4. The signal source is used to simulate the orbit of this type of satellite [12, 13], and the orbit parameters are shown in Table 1.

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The signal source

RS422

The navigaon receiver

test data

result

Interpretaon of result

The test equipment

truthful data

Fig. 4. Block diagram of semi-physical simulation verification system

Table 1. Instantaneous number of orbits of a certain type of satellite Time

Semi-major axis/km

Inclination/(°) RAAN/(°) Eccentricity True Mean Arg of anomaly/(°) anomaly/(°) perigee/(°)

23 Dec 2023 42166.258669 0.085 04:00:00.000

82.448

0.000300

0.000

0.000

202.691

Before the bit synchronization verification method is adopted, there have been tens of thousands of kilometers of wrong positioning solutions in a certain positioning solution, as shown in Table 2. After analysis and verification, it is due to the deviation of 1-ms in the calculation of the position synchronization position of the No. 12 Beidou satellites involved in the positioning calculation. Table 2. An initial positioning result Output data X position

Y position

Z position VX speed VY speed VZ speed

Simulator −29306442.26 30314586.23 36771.56 output data

−0.6159

0.5819

−0.1863

Receiver −29285583.35 30325695.56 29563.25 output data

−9.5354

12.2356

−3.2637

Adopting the bit synchronization verification methods shown in Sects. 3.1 and 3.2, there is no initial positioning error, and the long-term positioning solution is correct. During the positioning process, the stars are continuously added and subtracted, and there is no abnormal jump in the positioning result. Changing situation. As shown in Fig. 5, it is the result of the positioning solution for the continuous operation of the receiver for 10 h. The position and velocity data are continuously smooth, and there is no abnormal jump during star addition.

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(a) location data

(b) speed data

(c)The number of satellites involved in positioning

Fig. 5. Simulator output data

5 Conclusion Two methods of bit synchronization verification are proposed in this paper, verifying the position results of bit synchronization of the high-orbit navigation receiver. Based on NH code information, errors of BD bit synchronization cannot be checked pass, therefore the wrong satellites will not be involved in the positioning due to method 1. Based on converted time, Method 2 is a common bit synchronization detection mechanism for both GPS and BD. This method can effectively prevent the abnormal jumps of position results bring by new acquired satellites. In addition, the changes in clock error and clock drift in the navigation receiver calculation process can indirectly reflect whether the calculation result jumps, and further verify the calculation result. The experimental data shows that the above method can effectively solve the problem of wrong calculation of the bit synchronization position, which is of great significance to the engineering practice of high-orbit navigation receivers.

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References 1. Hostetler, L.D., Andreas, R.D.: Nonlinear Kalman filtering techniques for terrain-aided navigation. IEEE Trans. Autom. Control 28(3), 315–323 (1983) 2. Huan, H.: Analysis of BDS/GPS signals’ characteristics and navigation accuracy for a geostationary satellite. Remote Sens. 13, 1967 (2021) 3. Qin, H.: Research on positioning of high earth orbital satellite using GNSS. Chin. J. Space Sci. 28(4), 316–325 (2008) 4. Tsui, B.Y.: Fundamentals of Global Positioning System Receivers (A Software Approach). Introduction, pp. 1–6 (2004) 5. Foucras, M., Julien, O., Macabiau, C., et al.: Detailed analysis of the impact of the code Doppler on the acquisition performance of new GNSS signals. Nonlinear Anal. Theory Methods Appl. (2014) 6. Gang, X.: GPS Theory and Navigation Receiver Science. Electronic Industry Press, Beijing (2009) 7. Li, X.-S.: A GPS bit synchronization method for high-dynamic and weak signal. J. Electron. Inf. Technol. 33(10), 2521–2525 (2011) 8. Long, Z., Nan, G., Huang, B., et al.: A novel terrain-aided navigation algorithm combined with the TERCOM algorithm and particle filter. IEEE Sens. J. 15(2), 1124–1131 (2014) 9. Zhi-Feng, H., Jian-Ye, L., Rong-Bing, L.I., et al.: BeiDou signal bit synchronization algorithm based on long-time differential correlation and maximum likelihood. J. Chin. Inertial Technol. (2017) 10. Shi, M., Peng, A., Ou, G.: Analysis to the effects of NH code for Beidou MEO/IGSO satellite signal acquisition. In: Industrial Electronics & Applications, pp. 2075–2080. IEEE (2014) 11. Li, R., Han, Z., Liu, J., et al.: A high-sensitivity acquisition algorithm for BeiDou signals with NH code. J. Navig. 72(6), 1–15 (2019) 12. Shi, T., Zhuang, X., Xie, L.: Performance evaluation of multi-GNSSs navigation in super synchronous transfer orbit and geostationary earth orbit. Satell. Navig. 2(1), 13 (2021). https:// doi.org/10.1186/s43020-021-00036-0 13. Liu, Z., Wang, J.: A large Doppler weak direct spread signal acquisition algorithm and optimization method. In: Journal of Physics: Conference Series, vol. 1650, no. 3, p. 032148, 8 p. (2020)

Real Time Dynamic Adjustment Calculation Method of Camera Integration Time Based on Spaceborne Digital Elevation Model Linfeng Xing(B) , Chao Xue, Xin Guan, and Gaojian Lv Beijing Institute of Control Engineering, Beijing 100194, China [email protected]

Abstract. A camera integration time calculation method for optical remote sensing satellites with agile maneuverability and active scanning ability of any track is proposed, and the accuracy of on-board autonomous camera integration time calculation is further improved based on on-board digital elevation model. Keywords: Camera integration time · Dynamic adjustment · Spaceborne digital elevation model

1 Introduction With the continuous development of aerospace technology, the resolution of optical remote sensing satellites has been continuously improved. The ground pixel resolution of commercial satellites such as GeoEye and pleiadesneo has reached 0.3 m. They all use TDICCD as imaging devices, and the satellite is in orbit push broom imaging. As the ground elevation changes constantly, the space camera adjusts the integral time parameters of the camera according to the image information obtained at the previous time, which may not be suitable for the area where the ground is photographed at the next moment, resulting in the image obtained at the next moment being brighter or darker. Therefore, the satellite needs to calculate the integration time in real time according to the elevation information and send the integration time to the camera. According to the on-board digital elevation model (DEM), the satellite orientation is corrected so that the optical axis of the camera accurately points to the observed object on the ground, rather than the projection point of the object on the sea level. When the target point has a relative speed relative to the camera field of view, the camera integration time needs to be calculated according to the principle that the moving distance of the target point on the imaging plane is not more than 1/10 pixels within the exposure time, the position relationship of different fields of view of the load focal plane, as shown in Fig. 1. If the observation satellite carries out short-range push scan imaging with zero attitude in orbit, only the factors of orbit and ground speed need to be considered, and the fixed camera integral time is calculated to control the camera exposure time. However, if agile satellite is considered to carry out long strip active scanning imaging across the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 759–765, 2023. https://doi.org/10.1007/978-981-19-9968-0_91

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Fig. 1. Schematic diagram of different view of the load focal plane

region or attitude control with active rotation angular velocity in the three axes in the process of remote sensing imaging, such as forward and reverse two-way push scan imaging, the camera integration time is changing in real time, and the traditional camera integration time calculation method is no longer applicable. At the same time, the lack of ground elevation information will lead to satellite pointing offset and affect the image quality, It is necessary to propose a new dynamic calculation method for the integration time of imaging camera in the process of attitude maneuver, so that the exposure time control of camera shooting in the process of moving imaging or long strip active scanning is more flexible and adaptive. The following two problems need to be solved in this paper: 1) the traditional method of calculating the camera integration time when the observation satellite is in push scan imaging is only applicable to the zero attitude of the satellite orbit system. Because the observation satellite has the attitude control of active rotation angular velocity in the three axes in the process of remote sensing imaging, the traditional method is no longer applicable. 2) The traditional integral time calculation method with the fixed elevation or interpolation error causes the deviation of the target point vector calculation, which not only affects the quality of the original image, but also directly affects the image processing effect of the satellite with on-board image intelligent processing. The above two issues are the key factors affecting the quality of the new generation of intelligent autonomous agile mobile imaging on earth satellite in orbit mission completion.

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2 Dynamic Adjustment Method of Camera Integration Time 2.1 Dynamic Acquisition of Real-Time Point High-Precision Elevation Data Geographic longitude at known imaging starting point λB0 , Geographic latitude δB0 , Geographic elevation hB0 , Geographic longitude of imaging end point λB1 , Geographic elevation latitude δB1 , Geographical  hB1 , Target attitude angle ϕg , θg , ψg , Target angular  velocity p ωi = p ωix , p ωiy , p ωiz , Calculate the high-precision levation of real-time target: a) Calculate the satellite vector in the inertial coordinate system: ⎡ ⎤ cos() cos(u) − sin() cos(i) sin(u) I rs = r ⎣ sin() cos(u) + cos() cos(i) sin(u) ⎦, i rs = C iI I rs , C iI = diag(1, 1, K) sin(i) sin(u) where, C iI is the coordinate conversion matrix from J2000 inertial coordinate system I to the converted inertial coordinate system i, K = 1.0033633486. b) Calculate the representation of the z-axis pointing to the target point vector rZM and the geocentric pointing to the target point vector re of the central field of view coordinate system m under J2000 inertial coordinate system I: T  s = CMo (3, 1) CMo (3, 2) CMo (3, 3) , i rZM _e = C iI · C TOI · s cos γm = −i rTs · i rZM _e

 

i i

rs · rZM _e







 

2

i

i

T



T · s

= cos γ r r − R2e − i rs sin2 γm , I rZM = i rZM · CoI ·s

ZM s

CiI · CoI

m I







T



T re = I rs + i rZM CoI · s CiI · CoI · s

where, CMo (M , N ) represents the elements in row m and column n of matrix CMo . COI is the coordinate conversion matrix from J2000 inertial coordinate system I to orbital coordinate system o, and Re is the radius of the earth. c) Calculate the longitude λg and latitude δg of the target point: ⎡

⎤ cos(λG + ωe (t − t0 )) sin(λG + ωe (t − t0 )) 0 e re = ⎣ − sin(λG + ωe (t − t0 )) cos(λG + ωe (t − t0 )) 0 ⎦C TPN I re 0 0 1 

  

λg = arctan 2 e re (2), e re (1) , δg = arcsin e re (3) e re , δg = arctan tan δg

1 − Ee2



where λG is the Greenwich true star time angle, t g is the estimated time, t 0 is the reference time of the injected orbital parameters, C PN is the precession nutation matrix, and Ee is the eccentricity of the earth model, with a value of 0.08181919.

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d) Real time high-precision elevation calculation: If the on-board global digital elevation map is pre bound on the satellite, the elevation h of the target point can be queried through the longitude λg and latitude δg of the target point. If the digital elevation map is not bound, the following interpolation calculation is carried out: 

 IF (λB1 − λB0 ) > (δB1 − δB0 ) h = λg − λB0 (hB1 − hB0 ) (λB1 − λB0 ) + hB0 

 ELSE h = δg − δB0 (hB1 − hB0 ) δB1 − δB0 + hB0 . 2.2 Projection of Angular Velocity in Field of View Coordinate System a) Calculation of coordinate transformation matrix of load line of sight relative to orbit system

 C bo = C TLOS C123 ϕg , θg , ψg , C LOS = C312 (α, β, γ ) where, α, β, γ is the bias angle of the load camera, Cabc (x1 , x2 , x3 ) is the Euler angle x1 , x2 , x3 , and x1 , x2 , x3 is the coordinate conversion matrix rotated in a-b-c order. b) Calculation of coordinate transformation matrix of each field of view coordinate system relative to orbit system C Mo = C MP C Pb C bo = C 312 (θM , φ, 0)C 312 ( ω, ϕ, κ)C bo C +Yo = C +YP C Pb C bo = C 312 (θ+Y , φ, 0)C 312 ( ω, ϕ, κ)C bo C −Yo = C −YP C Pb C bo = C 312 (θ−Y , φ, 0)C 312 ( ω, ϕ, κ)C bo Among them ω, ϕ, κ is the installation angle of the camera relative to the star, φ is the rotation angle of the apparent direction around the Y axis of the camera coordinate system, θM is the rotation angle of the central field around the X axis of the camera coordinate system, θ+Y is the rotation angle of the +Y linear array apparent direction around the X axis of the camera coordinate system, and θ−Y is the rotation angle of the −Y linear array apparent direction around the X axis of the camera coordinate system. c) Projection calculation of camera target angular velocity in field of view coordinate system: M

ωi = C MP p ωi ,+Y ωi = C +YP p ωi ,−Y ωi = C −YP p ωi

2.3 Calculate the Ground Speed and Oblique Distance of the Three Fields of View of the Camera Calculate the projection I rZr of the vector of the Z axis of the field of view pointing to the ground target point in the J2000 inertial coordinate system for the three reference coordinate systems of the central field of view M, +Y linear array and −Y linear array respectively, the projection I re of the vector of the earth center pointing to the ground

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target point in the J2000 inertial coordinate system, and the projection r ves of the linear speed of the ground target point relative to the camera field of view in the field coordinate system, where, r = M , +Y , −Y . The specific calculation method takes r = M as an example: a) Obtain the representation of the satellite speed in the reference attitude coordinate system ov = s1



 μa 1 − e2

 r, o vs3 = −μ · e · sin f

   o  r r · vs1 , vs = C MO · o vs1 0 o vs3

Among them, μ is the gravitational constant of the earth, a is the semimajor axis of the satellite orbit, e is the eccentricity of the satellite orbit, and F is the true perigee angle; o v is the tangential component of velocity in the satellite orbital coordinate system; s1 o v is the radial component of velocity in the satellite orbital coordinate system; r v is s3 s the projection of the satellite speed vector in the field of view coordinate system R. b) Calculate the component of the linear velocity of the pointing point caused by the earth’s rotation in the central field of view coordinate system M I

T  ver = 0 0 ωe × I re , M ver = C Mo C oI I ver

Among them, ωe is the rotation speed of the earth. c) Calculate the satellite target angular velocity M ωi . The component of the linear velocity of the ground pointing point motion caused by I under the central field of view coordinate system M: M

T

 vrs = M ωi × 0 0 I rZM

d) According to the results of step a), step b) and step c), calculate the component of the linear velocity of the pointing point relative to the satellite in the central field of view coordinate system m: M

ve = M ver − M vs − M vrs

e) Recalculate the longitude, geocentric latitude and geographic latitude of the target point according to the high-precision elevation results with the method in 2.2(c)

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f) Calculate the component of the ground vertical vector in the central field of view coordinate system M:   

   T M e rv = cos δg cos λg cos δg sin λg sin δg , rv = C Mo C oI I rv ⎡

⎤ cos(λG + ωe (t − t0 )) − sin(λG + ωe (t − t0 )) 0 I rv = C PN ⎣ sin(λG + ωe (t − t0 )) cos(λG + ωe (t − t0 )) 0 ⎦e rv 0 0 1 g) Calculate the correction value of the z-axis component of the relative speed of the target point: cosT = M rv (1)

 M r (1)2 + M r (3)2 , sinT = v v



1 − cosT 2 , M ve (3) = M ve (1) cot T

where, M ve (m) m is the M ve element of vector m, M rv (m) is the same. 2.4 Calculate the Integration Time of Three Cameras a) Turn the relative speed of the ground target point back to the focal plane coordinate system of each camera: Mf

ve = C fM M ve = C 312 (0, φr , 0)TM ve , +Yf ve = C f +Y +Y ve = C 312 (θ+Y − θM , φr , 0)T+Y ve −Yf

ve = C f −Y −Y ve = C 312 (θ−Y − θM , φr , 0)T−Y ve

b) Calculate camera integration time:



  





TIM = dpld fpld · I rZM Mf ve (1)





 



TI +Y = dpld cos(θ+Y − θM ) fpld · I rZ+Y +Yf ve (1)





 



TI −Y = dpld cos(θ−Y − θM ) fpld · I rZ−Y −Yf ve (1)

where dpld is the camera pixel size and fpld is the camera apparent principal distance.

3 On Orbit Test Results The method was applied to a remote sensing satellite. The on-board digital elevation model was used to dynamically calculate and adjust the camera integration time in real time, and a good imaging effect was obtained. Figure 2 shows the splicing imaging of 4 strips of any track slope, and the image data of 360 km long in the Yangtze River Basin are obtained; Fig. 3 is a partial enlarged view of Vancouver with 1.5°/s due south and due north opposite scanning; Fig. 4 shows the three-dimensional imaging of Beijing Yanqing national snowmobile center with sharp changes in elevation. However, due to the use of the method in this paper, the real-time calculation of camera integration time in Fig. 2, Fig. 3 and Fig. 4 during the imaging process is accurate, and the image quality is very high.

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Fig. 2. The Yangtze river

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Fig. 3. BC place stadium

Fig. 4. Beijing Yanqing national bobsled center

4 Conclusion A real-time dynamic adjustment calculation method of camera integration time based on Spaceborne digital elevation model is designed, which supports satellite forward and backward active scanning, active scanning strip splicing imaging of any track, and obtains better imaging quality in orbit.

References 1. Han, Ch.Y.: Recent earth imaging commercial satellites with high resolutions. Chin. J. Optks Appl. Opt. 3(3), 201–208 (2010). (in Chinese) 2. Yang, X., Jin, G., Dai, L.: Influence and analysis about satellite attitude change on dynamic aerospace TDICCD camera. In: Symposium on Space Optics and Electromechanical Technology of Chinese Society of Space Sciences, 2013, pp. 345–352. Chinese Society of Space Sciences, Beijing (2013). (in Chinese) 3. Guo, H., Wang, H., Qiu, D.: An improved algorithm for computing observing angle of agile imaging satellite base on ellipsoid kinematic model. J. Natl. Univ. Defense Technol. 34(4), 99–102 (2012). (in Chinese)

A Time Sequence Design for Improving the Output Frequency and Accuracy of Star Sensor Under High Dynamic Conditions Rui Liu(B) , GuiMing Li, ZhiHui Li, and JianFu Zhang Beijing Institute of Control Engineering, Beijing, China [email protected]

Abstract. During the satellite in-orbit mission, the load platform needs to rotate at an angular velocity of 16°/s at a constant speed. The field of view of the star sensor will be frequently affected by sunlight and earth light. The requirement of high-speed rotation and the restriction of the field of view, as well as the high communication frequency of 10Hz, require the star sensor to have the ability to quickly identify the star map and provide high-precision output information. In this paper, we design a time sequence of the control system to improve the star map recognition speed and data update rate of the star sensor. The falling edge of the synchronization signal is aligned with the integration center of the probe to ensure the accuracy of the exposure time of the star sensor and the accuracy of the predicted attitude, which improves the ability of the star sensor to identify the star map and determine the attitude information quickly. According to the time from the exposure to the calculation of attitude information, the timing design of half-cycle interval between the synchronization signal and the fetching command makes use of the parallel working capability of the star sensor exposure and the star map data processing, which ensures sufficient time for the star map calculation, and improves the data update rate of the star sensor. Keywords: Star sensor · High dynamic conditions · Time sequence design · Output frequency

1 Introduction In recent years, with the rapid development of satellite technology, the requirements for its positioning accuracy have become higher and higher, and therefore the research on attitude measurement sensors that guarantee high attitude accuracy and stability of satellites has become more and more urgent. The star sensor is an important measurement component in the satellite attitude control system [1], and is also a widely used optical attitude sensor; it uses a star in space as the reference source for attitude measurement, and outputs the pointing of the sensor’s optical axis in the inertial reference system [2, 3]. It is considered to be the most accurate attitude sensor available, and can be used on a wide range of orbiting satellites. Compared to other attitude sensors, such as Earth and solar, star sensors have the following main advantages: they can provide higher pointing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 766–771, 2023. https://doi.org/10.1007/978-981-19-9968-0_92

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accuracy, three-axis attitude and all-round attitude information for any pointing. It can also meet the requirements of deep and narrow detection outside the solar system, and the output attitude is measured directly in real time with respect to the inertial coordinate system, without the problem of slow drift. The technical requirements of star sensors tend to develop towards high accuracy, high data update rate, miniaturisation, low power consumption and high reliability. The data update rate is an important indicator for star sensors and an important direction for the future development of star sensor technology, which characterises the maximum output frequency of the effective attitude data. As the shape and function of spacecraft become more and more complex, the attitude determination requirements under high dynamic conditions pose a more severe challenge to the performance of star sensors [4–7]. High dynamic condition means that the exposure time of star sensitivity is short [8], and the recognition of star map is difficult and timeconsuming, which leads to the reduction of data update rate. Generally, to improve star sensor’s star map recognition ability and data update rate, on the one hand, the hardware performance of star sensor can be improved, and on the other hand, the star map recognition algorithm can be optimized [9–11]. In this paper, from the point of view of controlling the use of subsystem, by controlling the timing design of subsystem, the star map recognition ability and data update rate of Star Sensor will be improved.

2 The Problems We Face During the mission of our satellite, the payload needs to rotate at an angular velocity of 16°/s, and the field of view of the star sensor will be frequently affected by the sunlight and ground light. The high rotation speed requirement of the high dynamic star sensor and the field of view constraint limit require the star sensor to have the ability to quickly identify the star map and output high precision attitude information. 2.1 How to Accurate Prediction of the Exposure Chart? To improve the fast recognition capability of the star sensor, the attitude control computer is needed to provide the corresponding coarse attitude information for the next exposure moment. Since the payload module is rotating at a high speed, a small time error can lead to a huge difference in the star chart, so the closer the time corresponding to the coarse attitude prediction is to the next exposure time, the more accurate the predicted attitude information provided by the attitude control computer is, and the faster the star sensor recognizes the star chart. Therefore, the control system needs to solve the problem of aligning the moment corresponding to the predicted attitude with the next exposure moment when using the star sensor. In order to solve this problem, during the development of the model, a synchronization signal is sent from the attitude control computer to the star sensor, which is a periodic signal with the same frequency as the control cycle, both being 100 ms. The falling edge of the synchronization signal is aligned with the probe integration center moment, and the alignment is ensured by the relative time interval calculated by the internal highprecision relative clock of the star sensor. This method ensures that the probe is exposed

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at a fixed moment of each cycle, while the attitude control computer provides the estimated coarse attitude information to the star sensor at that fixed moment. The accurate attitude estimation information can improve the star map recognition and attitude data processing speed of the star sensor. In addition, the probe is exposed at a fixed moment of each cycle, which ensures the temporal accuracy of the attitude information. 2.2 How to Increase the Output Frequency of the Attitude Information? During the development process, it was also found that the brightness and quality of star points in the star map were limited due to being in a high-speed rotation state, and the star map data processing speed became unstable, which constrained the data update rate of the star sensor. The time required for the star sensor to obtain attitude data from exposure, image transmission, and image calculation is greater than 100 ms but less than 150 ms. It is very important for the stable operation of the rotating load that the attitude data can be obtained by the attitude control computer. Therefore, it is necessary to ensure both sufficient processing time of star map data and 100 ms update rate of star sensitive data. In order to solve this problem, the method of sending the fetch command by the attitude control computer with 50 ms delay of the exposure synchronization signal was proposed during the development of the model. Through this timing design, the fetch command acquires the attitude data of the star sensor exposure image 150 ms earlier. By this method, the synchronization signal controls the exposure of the star sensor, and the fetch command controls the reading of the star sensor attitude data. The star sensor has 50 ms for each control cycle to perform exposure and image calculation in parallel, while the star sensor is calculating and processing the star image acquired in the previous exposure cycle and the attitude data acquired in the next cycle by the fetch command during the current exposure and image transmission. In this way, the interval between the exposure of a pair of star maps and the completion of the calculation of the transmission is greater than one cycle, that is, to ensure that the star map calculation processing time is sufficient (150 ms), but also to ensure the data update rate of 100 ms.

3 Our Time Sequence Design to Solve the Problems This section details the timing design involved in the solutions to the two proble ms in the previous section, as shown in the following figure. 3.1 Accurate Prediction of the Exposure Star Map First, the attitude control computer sends a synchronization signal, active low, with a period of 100 ms and an output pulse width of 2 ms at the 50th ms of each 100 ms control cycle. This synchronization signal is the integration control signal of the current frame of the star sensor, and its falling edge is aligned with the probe integration center moment. At the same time, the attitude control computer forecasts the coarse attitude information required for the 50th ms exposure of the current cycle to the star sensor before the 45th ms of each 100 ms control cycle operation, which is the attitude of the 50th ms moment

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of the current cycle. Considering the information transmission delay, the attitude control computer needs to provide the coarse attitude information 5 ms before the star sensor exposure to ensure that the coarse attitude information can be used in time for star map processing after the exposure. The high-precision relative timer inside the star sensor takes the falling edge of the synchronization signal of the current cycle as the reference, and opens the exposure of the next cycle after accumulating a time interval of T ms (90 ms < T < 100 ms), and ensures that the falling edge of the synchronization signal is aligned with the center moment of the exposure. This method provides precise time alignment and can guarantee that the probe is exposed at a fixed moment of each cycle, while the attitude control computer can provide the star sensor with the predicted coarse attitude information at that fixed moment. In this method, the probe is exposed at a fixed moment of each cycle, which ensures the accuracy of the attitude information. In addition, since the exposure time is determined, the attitude control computer can provide the star sensor with an accurate predicted coarse attitude to assist the high dynamic star sensor to quickly identify the star map to determine the attitude information when the rotation speed is stable. In this way, this method gives the high dynamic star sensor mounted on the rotating load the ability to use the forecast information to quickly identify the star map and give high-precision output information. 3.2 Parallel Processing of Star Map Exposure and Calculations Next, the attitude control computer sends a fetch command TC_RTS to the star sensor at the 0 ms of each 100 ms control cycle, and the star sensor receives the fetch command and sends back the star sensor attitude data to the payload module computer via RS422 serial port. The exposure moment of the last cycle is at 50 ms, and the time required for the star sensor to calculate the attitude data from exposure is between 100–150 ms. Therefore, the 0 ms fetch command of the current cycle cannot obtain the attitude information of the star chart exposed in the previous cycle. The star-sensitive device has the ability to work in parallel, and it can process the star map of the previous cycle for star map calculation during the exposure integration. By exposing at 50 ms of each cycle and sending the fetch command at 0 ms of each cycle, the time duration of each exposure and the star image calculation is 150 ms. Therefore, the fetch command sent at 0 ms of this cycle acquires the star sensor attitude information before 150 ms (i.e., 50 ms of the previous cycle). Similarly, the star map is obtained from the 50th ms exposure of the current cycle and will be sent to the computer after 150 ms. This timing design ensures sufficient processing time (150 ms) for star map calculation and 100 ms data update rate. The timing design is shown in Fig. 1. Where the rising edge of the 100 ms period signal is the 0 ms of this cycle and the falling edge of the period signal is the 50 ms of this cycle. The implementation steps are as follows.

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n+2

n-1 100ms Control cycle signal

n+1

50ms q(n-1)

q(n)

q(n+1)

SYNC TC_RTS

TC_RTS

TC_RTS

Exposure

Storage & Transmission Q(n-2)

Star map calculation processing

n

n-1

n-2

n+1

Q(n-1)

n-1

n+2

Q(n)

n

n+1

n+2

Fig. 1. The timing sequence design of control system.

Step 1: The control system computer sends a TC_RTS fetch command to the star sensitive at the 0 ms of the nth control cycle. At this time, the star sensor is calculating the star map of the (n−1)th control cycle exposure. Step 2: The star sensor sends back the attitude data for the (n-2)th cycle (−150 ms). Step 3: The load cell control computer sends the estimated coarse attitude information for the 50th ms to the star sensor at the 45th ms of the nth control cycle for star map processing. Step 4: The control system computer sends a synchronization signal to the star sensor at the 50th ms of the nth control cycle and the star sensor starts the exposure. The falling edge of the synchronization signal is aligned with the probe exposure integration center moment. Step 5: Star sensor in the nth control cycle after the end of the exposure, the transmission, storage, and the star map for calculation and processing. The calculation process of the star map continues until the first 50 ms of the (n + 1)th control cycle. Steps one to five are repeated.

4 Conclusion In this paper, we design a control method to improve the star map recognition speed and data update rate of the star sensor. First, in this method, each control cycle of the attitude control computer sends a synchronization signal, and the falling edge of the

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synchronization signal is aligned with the integration center of the probe. This method ensures that the probe is exposed at a fixed time every cycle, and the attitude control computer provides the estimated coarse attitude information to the star sensor at that fixed time. The accurate attitude estimation information can improve the star map recognition and attitude data processing speed of the star sensor. In addition, the probe is exposed at a fixed time of each cycle, which ensures the temporal accuracy of the attitude information. Secondly, according to the time from the exposure to the calculation of attitude information, the synchronization signal and the fetch command are sent at an interval of 50 ms, and the purpose of the current cycle fetch command is not to obtain the data of the current cycle or the previous cycle, but to obtain the data of the previous cycle. This ensures a fast data update rate and sufficient processing time for star map calculation, overcoming the problem of long image calculation and processing time due to short exposure time, which reduces the data update rate. The method proposed in this paper can further improve the performance of the high dynamic star sensor and the satellite, and is suitable for satellites with high speed rotation or large maneuvering attitude operation. The control method proposed in this patent is clear, simple, and easy for designers to design and implement. The method proposed in this paper is clear, simple, and easy for designers to design and implement, and can greatly improve the star map recognition speed and data update rate of the star sensor, which is suitable for satellites with high speed rotation or large attitude maneuvering operation.

References 1. Nardell, C.A., Wertz, J., Hays, P.B.: Image processing, simulation and performance predictions for the MicroMak star tracker. In: Proceedings of SPIE-the International Society for Optical Engineering, vol. 5916 (2005) 2. Liebe, C.: Accuracy performance of star tracker-a tutorial. IEEE Trans. Aeros. Electron. Syst. 38(2), 587–599 (2002) 3. Schmidt, U., Wunder, D.: Hard-wired digital data preprocessing: applied within a modular star and target tracker. In: SPIE, vol. 3163, pp. 214–223 (1997) 4. Yan, J.Y., Jiang, J., Zhang, G.J.: Dynamic imaging model and parameter optimization for a star tracker. Opt. Express 24(6), 5961–5983 (2016) 5. Yan, J.Y., Jiang, J., Zhang, G.J.: Modeling of intensified high dynamic star tracker. Opt. Express 25(2), 927–948 (2017) 6. Bezooijen, V., Roelof, W.H.: SIRTF autonomous star tracker. In: IR Space Telescopes and Instruments, vol. 4850, pp. 108–122. International Society for Optics and Photonics (2003) 7. Pan, D., Zhou, Q., Liu, X., et al.: Modeling and detection of star spot in high dynamic condition. Acta Photonica Sinica 51(3), 0304002 (2022) 8. Wei, X., Tan, W., Li, J., Zhang, G.: Exposure time optimization for highly dynamic star trackers. Sensors 14(3), 4914–4931 (2014) 9. Da, L., Zhou, Q., Yu, L., et al.: Modeling and compensation of star image spots under high dynamic conditions. Flight Control Detect. 3(3), 86–94 (2020) 10. Qu, Y., Li, Q., Di, Z., et al.: Map matching algorithm based on image convolution. Flight Control Detect. 4(2), 19–25 (2021) 11. Wang, Q.: Research on Star Point Extraction and Joint Attitude Determination Algorithm of High Dynamic Star Sensor, pp. 23–38. National University of Defense Technology, Changsha (2016)

Research on the Measurement Method of Inter-satellite Clock Difference and Distance of Formation Satellites Yang Liu(B) , Yujie Liu, and Sufang Chen China Academy of Space Technology (Xi’an), Xi’an 710100, China [email protected]

Abstract. The formation satellite constellation carries out satellite networking by establishing inter-satellite measurement link and inter-satellite communication link, and complete the measurement of inter-satellite clock difference, frequency difference and distance with the help of inter-satellite link, Then the highprecision time synchronization and frequency synchronization of satellite constellation are realized. Taking the double-satellite formation constellation as an example, this paper proposes an inter-satellite clock difference and distance measurement method, introduce the principle of inter satellite clock difference and distance measurement, designs measurement system scheme, gives implementation steps of measurement function and the measurement value matching method in two-way measurement, the problem that the transmission delay does not match the clock difference when the satellite is actually in orbit is proposed for the first time, and the compensation method is given, The measurement method is verified in a dynamic environment through a certain product platform, the measurement accuracy of inter-satellite clock difference is better than 0.2 ns and the ranging accuracy is better than 0.06 m. Keywords: Inter-satellite · Clock-difference · DOWR · Pseudo-range

1 Introduction With the rapid development of space technology in recent years, inter-satellite links have been more and more widely used in satellite navigation and positioning, satellite formation flying, space-based passive positioning and other spacecraft systems [1–3]. The formation satellite carries out satellite networking by establishing inter-satellite measurement link and inter-satellite communication link, and complete the measurement of intersatellite clock difference, frequency difference and distance with the help of inter-satellite link, Then the high-precision time synchronization and frequency synchronization of satellite constellation are realized. In recent years, much research has been done on inter-satellite measurement technology inside and outside, but the known literature focuses on theoretical research, algorithm simulation and ground validation. The practical problems that the inter-satellite ranging equipment of formation satellites may encounter during long-term operation in orbit are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 772–779, 2023. https://doi.org/10.1007/978-981-19-9968-0_93

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less involved [4–6]. Taking the double-satellite formation constellation as an example, this paper proposes an inter-satellite clock difference and distance measurement method. The problem that the transmission delay does not match the clock difference when the satellite is actually in orbit is proposed for the first time, gives the compensation method and verifies the measurement method through a certain product platform. It provides a reference for the engineering realization of the inter satellite measurement technology in orbit.

2 Measurement Principle of Inter-satellite Clock Difference and Distance The dual one way ranging (DOWR) measurement system can eliminate the influence of time synchronization between satellites, eliminate the public error in the channel, and complete the measurement of inter-satellite clock difference, frequency difference and distance [7, 8]. The basic principle is shown in the figure below. T1 (t1 )

τa1

τa2

A

t

τ (t2 )

τ (t1 )

Δt (t1 ) T2 (t2 )

Δt (t2 )

τb1 B

t1

τb2

t2

t

Fig. 1. The basic principle of dual one way ranging (DOWR)

Satellite A and satellite B, which carry out bidirectional measurement, send timing signals using their respective devices respectively, and receive the timing signal from the other party. Here, the time difference between local clock timing signal measured by star A and received timing signal from B star is defined as the pseudo-range of satellite A, mark as T1 (t1 ), the time difference between local clock timing signal measured by star B and received timing signal from A star is defined as the pseudo-range of satellite B, mark as T2 (t2 ). The instantaneous time difference of satellite AB at t1 and t2 is t1 and t2 respectively, which can be obtained from Fig. 1, T1 (t1 ) = t(t1 ) + τb1 + τ (t1 ) + τa2

(1)

T2 (t2 ) = −t(t2 ) + τa1 + τ (t2 ) + τb2

(2)

The τ (t1 ) is the propagation delay of the timing signal from the transmitting antenna of satellite B to the receiving antenna of satellite A. τ (t2 ) is the propagation delay of the timing signal from the transmitting antenna of satellite A to the receiving antenna of satellite B. τa1 , τa2 , τb1 and τb2 are the time delays of the transmitting and receiving equipment of satellite A and satellite B respectively.

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The atomic clock is used on the satellite, and the relative frequency accuracy is better than 5 × 10−10 , If t = t2 − t1 < 2 ms and |t(t2 ) − t(t1 )| < 0.001 ns, therefore, it can be considered that t(t1 ) ≈ t(t2 ) = t. There is relative motion between satellites. During the time difference t, The distance between satellites will change. For formation flying constellations, the relative speed of the two satellites is generally small, not more than 10 m/s, that is v = 33 ns/s, If t ≤ 2 ms, the distance change is less than 0.02 m, so it can be considered as τ (t1 ) ≈ τ (t2 ) = τ . Formula (1) − formula (2): t =

T1 − T2 − τz1 2

(3)

τ=

T1 − T2 − τz2 2

(4)

Formula (1) + formula (2):

In the formula,τz1 = (τb1 + τa2 ) − (τa1 + τb2 ), τz2 = (τa1 + τb2 ) + (τa2 + τb2 ), The combined delay can be obtained through the test, and the device delay is constant. Calculate the derivative of Eq. (1) and Eq. (2) with respect to time, Considering that when t = t2 − t1 < 2 ms, so ˙t (t1 ) ≈ ˙t (t2 ) = ˙t , τ˙ (t1 ) ≈ τ˙ (t2 ) = τ˙ = V , Get the formula: T˙ 1 = ˙t + τ˙

(5)

T˙ 2 = −˙t + τ˙

(6)

T˙ 1 and T˙ 2 are the pseudo-range change rate of satellite A and satellite B respectively, that is, the inter-satellite pseudo-velocity, ˙t is the change rate of inter-satellite time difference, that is, the accuracy of inter-satellite relative frequency. Formula (5) − formula (6): γ = ˙t =

T˙ 1 − T˙ 2 2

(7)

V = τ˙ =

T˙ 1 + T˙ 2 2

(8)

Formula (5) + formula (6):

The inter-satellite frequency difference f = γ ·fA can be obtained by using formula (7), the inter-satellite relative velocity V can be obtained by using formula (8), the intersatellite time difference t can be obtained by using formula (3), the inter-satellite distance τ can be obtained by using formula (4).

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3 Implementation of Measurement Method 3.1 Measurement System Scheme The two satellites participating in formation measurement are equipped with intersatellite measurement terminals and satellite time-frequency terminals respectively [9, 10, 11]. The inter-satellite measurement terminal is used to complete the measurement and calculation of clock difference, frequency difference, distance and speed between two satellites. The satellite time-frequency terminal is composed of rubidium atomic clock and time-frequency management circuit, it provides satellite second pulse signal (1pps signal) and reference frequency signal (10 MHz) for inter-satellite measurement terminal, The relative frequency accuracy of rubidium atomic clock is better than 5 × 10−10 . The inter satellite transmission signal is BPSK signal, and the message is spread spectrum through pseudo-code first, and then modulated to carrier wave. The code rate of this system is 5 Mcps, the code period is 0.2 ms, the carrier frequency is K-band, and the IF frequency point is 140 MHz, The data frame format of the message is a fixed frame length, the frame period is 200 ms. Each frame contains inter-satellite measurement information such as local frame number, local pseudo-range, local pseudo-velocity and local second count. The pseudo-code spread spectrum ranging technology is used to obtain the inter-satellite pseudo-range, and the carrier phase measurement technology is used to obtain the inter-satellite pseudo-velocity. 3.2 Realization of System Measurement Function Step 1. The two satellites independently generate local second counts based on local 1PPS signals. According to the local 5PPS (transmission frame time mark signal), the local frame number is generated, where the local 1PPS is aligned with the local 5PPS phase. And ensure that the frame number of the first frame after each 1PPS is 1 after modulo five operation. Step 2. When the two stars have finished demodulating the signal of the opposite star, gets the accumulated receive code phase information from the start of the local frame time marker (5PPS) to the end of the receive frame time marker (5PPS) as inter-satellite pseudo-range information. And at the local frame time mark (5PPS) moment, the count Value of Doppler are extracted to obtain inter-satellite pseudo-speed. Step 3. Two satellites load local measurements such as local seconds count, local frame number, local pseudo-range into local transmission frames and send them to the opposite satellite. Step 4. After receiving and demodulating the measurement information sent by the other party independently, the two satellites will process the opposite satellite measurement information and local measurement information together and automatically calculate the inter-satellite clock difference, inter-satellite frequency difference, distance and distance change rate according to the principles described in Sect. 2.

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Step 5. Satellite B adjusts the local second count to match that of satellite A according to the local second count of satellite A in the measurement information frame from satellite A, then regenerate the local frame number of the satellite according to the regenerated second count. Step 6. When the inter-satellite clock difference exceeds the specified threshold, satellite B automatically adjusts the 1pps time mark phase according to the inter-satellite clock difference measurement information, so that the inter satellite clock difference is within the threshold. The threshold value of inter-satellite clock difference is selected to ensure that the system error caused by the different time of two-way transmission can be ignored, and at the same time, the frequency of satellite time phase adjustment should not be too large. 2 ms is selected as the threshold value in this system. When satellite A and satellite B are just turned on, the inter-satellite clock difference t(k) is relatively large. After using the above algorithm to calculate once, the 1pps time mark of satellite B is automatically adjusted, the accuracy of the measured value calculated again will converge to the range required by the system. Generally, after the 1pps time mark of satellite B is adjusted twice, the system can enter the precision measurement stage, and then the time mark will not be adjusted until it exceeds the adjustment threshold.

3.3 Pairing of Measured Values in Two-Way Measurement When the two satellites use the demodulated measurements and local measurements for joint processing, the problem of pairing measurements is involved. Only when the pseudo-range and pseudo-velocities of two satellites at the same time are compared, the correct inter-satellite measurements can be obtained. When conducting two-way comparison calculation: Step1. Operate Modulo 5 operation on the local frame number and the receiving frame number. Step2. Cache five sets of local pseudo-distance and local pseudo-speed according to the result of local frame number after Modulo 5 operation. Step3. By current result of receiving frame number after Modulo 5 operation, Call the corresponding local pseudo-range, local pseudo-speed of the local cache and pseudorange, pseudo-speed currently received for calculation. 3.4 Mismatch Between Transmission Delay and Clock Difference The clock difference between two satellites will gradually increase with the increase of the satellite operating time. It may occur that the clock difference between AB satellites will gradually increase beyond the propagation delay. There are two cases: satellite A time mark ahead satellite B time mark and satellite B time mark ahead satellite A time mark. Satellite A time mark is ahead of satellite B time mark, and the clock difference is greater than the transmission delay This is shown in the following Fig. 2,

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Fig. 2. Satellite A time mark is ahead of satellite B time mark

In this case, the clock difference calculated by comparison is as follows. T =

(t + τ ) − (200 ms − t + τ ) T1 − T2 = = t − 100 ms 2 2

(9)

It can be found that under this condition, the calculated clock difference is 100 ms smaller than the actual clock difference. Satellite B time mark is ahead of satellite A time mark and the clock difference is greater than the transmission delay This is shown in the following Fig. 3,

Fig. 3. Satellite B time mark is ahead of satellite A time mark

In this case, the clock difference calculated by comparison is as follows. T =

(200 ms + t + τ ) − (τ − t) T1 − T2 = = t + 100 ms 2 2

(10)

It can be found that under this condition, the calculated clock difference is 100 ms greater than the actual clock difference. In these two special cases, the results of the clock difference measurement need to be compensated during the comparison calculation process. In case (1) the result of the clock difference calculation needs to be added 100 ms, in case (2) the result of the clock difference calculation needs to be subtracted 100 ms.

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4 Method Validation The verification platform is set up, and the channel simulator is inserted on the two-way transmission channel. The channel simulator is controlled independently to simulate the forward link channel and the reverse link channel respectively. Through controlling the inter-satellite distance delay, Doppler and signal level, the inter-satellite dynamic simulation is carried out. The Doppler frequency shift is 5 kHz, and the maximum frequency shift change rate is 425 Hz/s. The accuracy of clock difference measurement is shown in Fig. 4, and that of distance measurement is shown in Fig. 5. The accuracy of inter-satellite clock difference measurement is better than 0.2 ns and that of distance measurement is better than 0.06 m in practical projects.

Fig. 4. Precision of clock difference measurement

Fig. 5. Precision of distance measurement

5 Concluding Remarks Taking the double-satellite formation constellation as an example, this paper proposes an inter-satellite clock difference and distance measurement method, designs measurement

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system scheme, gives implementation steps of measurement function and the measurement value matching method in two-way measurement, the problem that the transmission delay does not match the clock difference when the satellite is actually in orbit is proposed for the first time, and the compensation method is given, and the measurement method is verified by using a channel simulator in a dynamic environment through a certain product platform, The measurement accuracy of inter-satellite clock difference is better than 0.2 ns and the ranging accuracy is better than 0.06 m. This method has strong engineering practical value, and has been verified in orbit, with good functional performance. In the future, it can be popularized and applied to the field of inter-satellite measurements for LEO Internet satellites and high-precision time synchronization between the earth and the satellites [12].

References 1. Schaire, S.H., Shaw, H., Altunc, S.: NASA near earth network (NEN) and space network (SN) Cube Sat communications. In: Proceeding of Space Ops 2016 Conference, pp. 2598–2617. AIAA, Daejeon (2016) 2. Chen, T., Lin, B., Gong, W., Chang, J.: Time recovery strategy of navigation satellite based on inter-satellite link. Chin. J. Space Sci. 40(3), 419–424 (2020) 3. Huang, Z., Lu, J.: Space-base passive positioning and modern small satellite technology. J. Inst. Command Technol. 4(3), 24–29 (2003) 4. Zhang, X., Chen, Q., Jiang, Y.F.: Design of inter-satellite links and analysis of key technologies for micro-nano satellite networking systems. Telecommun. Eng. 60(8), 883–889 (2020) 5. Gao, T., Bai, Y.: Satellite-ground two-way time synchronization new algorithm based on Ka link. Electron. Des. Eng. 14(27), 10–15 (2019) 6. Wu, W., Jiang, M., Wang, X.: Two-way satellite time and frequency transfer based on software defined receiver. J. Time Freq. 42(2), 94–100 (2020) 7. Shang, X., Xiao, X., Gao, J., Zhang, P., Song, B.: Analysis of inter-satellite velocity measurement accuracy influenced by doppler integral time. Spacecaraft Eng. 4(29), 34–39 (2020) 8. Zhang, H., Dong, S., Yuan, H., Wu, W.: Analysis of the performance of SDR TWSTT. J. Time Freq. 4(43), 284–291 (2019) 9. Zhou, S., Hu, X., Liu, L., He, F., Tang, C.: Status of satellite orbit determination and time synchronization Technology for global navigation satellites system. Acta Astronom. Sin. 4(60), 2–10 (2019) 10. Zhong, X., Chen, H.: Technology research on timing and frequency measurement system in satellite constellations. J. Astronaut. 31(04), 1110–1116 (2013) 11. He, Y., He, K., Wang, G., Du, E., Du, L.: A survey of navigation satellite time-frequency system. Navig. Position. Timing 5(8), 61–70 (2021) 12. Su, Y., Liu, Y., Zhou, Y.: Broadband LEO Satellite communications: architetures and key technologies. IEEE Wirel. Commun. 26(2), 55–61 (2019)

Research on Asteroid Detection Radar Based on High Precision Signal Delay Estimation Sufang Chen(B) , Mengna Jia, Yang Liu, and Dong Liu China Academy of Space Technology, Beijing 710100, Shanxi, China [email protected]

Abstract. With the development of deep space exploration technology, the detection of small celestial bodies has become a research hotspot at home and abroad. A new cooperative asteroid detection radar scheme is adopted in this paper, using dual base station radar system, the electromagnetic wave signals transmitted and received in both directions can directly penetrate the interior of the asteroid, high precision measurement of time-delay transmission through the asteroid, and through multi-channel correlation technology, the time delay spectrum estimation of penetrating and diffracting asteroid signals are obtained, through time delay transmission and time delay spectrum estimation, using inversion algorithm, it can reverse the internal density, porosity, chemical composition, etc., and estimate the shape and size of the asteroid, in this paper, a system and scheme for detecting the internal structure of small celestial bodies using a bistatic system radar are presented, and a specific realization of the time-delay spectrum estimation using multi-channel correlation technology is given. Have great significance on the deep analysis and exploration of asteroids. Keywords: Asteroid exploration · Dual base station radar system · Multichannel correlation technology · Time delay–spectrum estimation

1 Introduction As asteroid exploration has become one of the key development goals in the field of deep space exploration in major space-spanning countries. Asteroid exploration can study the origin of asteroids and the origin and evolution of the solar system, discover resources and energy that may be used by humans, and find technologies and methods to prevent asteroids from hitting the earth. NASA has launched a number of asteroid probes to detect the size, mass, shape, rotation, gravity and other aspects of the asteroid, and the lander “Philae” carried by the Rosetta asteroid probe launched by ESA EASA successfully landed on the surface of the comet, becoming the world’s first probe to complete the mission of landing in place on the surface of the comet. Asteroid detection has developed from overflight and companion detection to surface soft landing and sample return detection, at present, Japan has realized the surface sampling return of asteroids, the European “Rosetta-Philae” probe is also on the surface of the comet to conduct more detailed scientific research, and the United States, Europe, Japan in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 780–786, 2023. https://doi.org/10.1007/978-981-19-9968-0_94

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follow-up planning, are actively promoting the implementation of asteroid sampling and return missions, China has also opened asteroid exploration mission [1–6]. Through single-station radar or optical measurement methods, the outer surface and shallow surface structure of the asteroid can be measured, and the internal composition and structure of the asteroid cannot be detected in depth. Therefore, the dual base station radar system proposed in this paper can invert the internal density, porosity, chemical composition, etc. of the asteroid by orbiting the asteroid, penetrating and diffring the asteroid by transmitting electromagnetic signals in both directions, accurately measuring the delay, amplitude, phase and other changes after the signal penetrates the asteroid’s interior, using high-resolution related delay map estimation, and using chromatography and full waveform inversion algorithms [9–11] And estimate the size and shape of the asteroid, which is of great significance for the deep analysis detection of asteroids.

2 Introduction and Modeling Analysis of Dual Base Station Radar System 2.1 Dual Base Station Radar System The radar measurement system of the dual base station is composed of two parts: the radar host and the transponder, the radar host is installed on the orbiter, and the transponder is installed on the lander, and adopts the cooperative target radar, continuous wave incoherent two-way measurement technology, and the transceiver time-sharing working mode. Switching control is carried out through the transceiver switch, and in the transmission time slot of the radar host, the transponder terminal is in the state of receiving the spread spectrum signal transmitted by the host; In the receiver time slot of the radar host, the transponder terminal is in the state of transmitting spread spectrum signals to the radar host. Through the time slot coordination, the signal transmission delay between the host and the transponder can be measured with high precision; At the same time, the host and transponder in the receiving signal time slot, the use of multiple related devices to calculate the relevant results, to obtain a high-resolution signal transmission delay map, the use of this map and output delay information, can achieve the asteroid internal structure and shape of the inversion. 2.2 System Modeling According to the description of Fig. 1, the transponder will land on the surface of the asteroid as the lander, the host will fly around the orbiter, and as the relative position of the host and the transponder changes, the signal captured by the host includes a signal that penetrates the asteroid (called a transmission signal) and a signal that does not pass through the asteroid (called a diffraction signal), and according to the simulation verification of the asteroid, the signal characteristics of the system are modeled as follows:

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Fig. 1. Geometric relationship between the position of the probe, lander and asteroid

(1) With the change of the relative position of the two devices, the diffraction signal must exist; (2) With the change of the relative position of the two devices, the transmitted signal may not exist; (3) When the diffracted signal and the transmitted signal exist at the same time, the delay interval between the diffraction signal and the transmitted signal is greater than 2 chips, and the diffraction signal will arrive before the transmitted signal; (4) Each received time slot captures both the diffraction signal and the transmitted signal, if only one signal is captured, it is considered a diffraction signal, and the diffraction signal is tracked; If two signals are captured, it is necessary to judge the diffraction signal and the transmitted signal according to the arrival sequence of the captured signal, and track the two signals separately; (5) In the tracking process, the diffraction signal and the transmitted signal produce multiple-way correlation results with a code phase interval of 1/8 chip (the code phase interval can be designed as needed, this article is designed as 1/8 chip), according to the previous simulation model verification, record the code front of the tracking code The relevant results of Mchipand post-coded N chip (M and N can be adjusted according to the size of orbits and asteroids, the scheme designed in this paper, M = 1 chip, N = 10 chip), which transmits the multiplexed correlated delay map to the ground system for subsequent signal inversion and internal structure detection.

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3 Time Delay–Spectrum Estimation According to the modeling and analysis of the system, for the diffraction signal and the transmitted signal, in fact, can be seen as the other party’s multipath signal, for the diffraction signal, its multipath contains two parts: one is, multiple diffraction signals together to produce multiple paths, the second is that there may be a transmission signal, the formation of multiple paths; By measuring the delay and delay map of the diffraction signal at different locations, the shape structure of the asteroid can be reversed; For the transmitted signal, its multipath contains two parts: one is due to the possible pores, gravel and other different structures in the asteroid’s interior, which will produce refraction or scattering of the signal, resulting in short-term multipath, and second, the diffraction signal will produce multiple paths. According to the multipath principle, through the multipath correlation technology, the delay map estimation of the transmitted signal and the diffraction signal can be obtained respectively. 3.1 Mathematical Model of Multichannel Correlation Technology Considering that there may be multiple multipath signals at the same time, let the received signal be: s(t) = A(t)c(t) cos(w0 t) +

N 

αi A(t)c(t − τi ) cos(w0 t + θi )

(1)

i=0

where A(t) is the modulation information and c(t) is the spread spectrum code, cos(w0 t) for the carrier, αi , τi , θi is the amplitude, delay, and phase change of a multipath signal. The local tracking carrier creates a noninverting branch cos(w0 t + θ ) and orthogonal branches sin(w0 t + θ ), The local code generator produces M + N channels equal to the phase interval for δ, Acopy of the patch c(t − nδTc), where Tc is a piece of time, n = −M, −(M − 1)… 0, 1, 2… N, that is, relative to the tracking code phase, produces a leading M-way and lagging N-channel pseudo-code. The local and received signals do correlation integration operations, and the delay-Doppler information of the noninverting and orthogonal branches- is obtained, respectively: Inconverting branch (I branch) related results:  Tcoh 1 s(t) cos(w0 t + θ )c(t − nδTc )dt I = Tcoh 0 (2)  1 = 2 A(t) cos(θ )Rc (nδTc ) + 21 N i=1 αi A(t) cos(θ − θi )R(τi − nδTc ) Orthogonal branch (Q branch) related results:  Tcoh 1 Q = Tcoh s(t) sin(w0 t + θ )c(t − nδTc ) 0  = 21 A(t) sin(θ )Rc (nδTc ) + 21 N i=0 αi A(t) sin(θ − θi )R(τi − nδTc )

(3)

where Rc (•) is the autocorrelation function of the code. According to Eqs. (2), (3), the relevant output signal is a superposition of a direct signal and a multipath signal. At the same time, from the single-branch correlation

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results of the noninverting branch and the orthogonal branch, the map is subject to: 1) the multipath signal and the direct signal carrier phase offset; 2) Multi-diameter signal pseudo-code phase and direct signal pseudo-code phase shift; 3) The influence of whether the modulation data at the time of correlation has a jump. When the correlation operation is performed, the system design can ensure that the calculation is not crossed the data edge, so the influence of the modulation data bits at the relevant time can be temporarily ignored, so in the subsequent analysis, only the effects of the carrier phase offset and the code phase offset of the multipath signal are considered. (1) The influence of the multipath signal carrier phase on the map According to Eqs. (2) and (3), the effect of the phase shift of the multipath signal on the relevant result, for the noninverting branch conforms to the cosine function relationship, for the orthogonal branch conforms to the sine function relationship, the same amplitude and code phase offset, the impact of the carrier phase shift on the relevant amplitude is as follows (Fig. 2):

Fig. 2. Influence of carrier phase offset on correlation result

(2) The effect of the phase shift of the code on the map According to formulas (2), (3), the code phase offset of the multipath signal determines the position of the relevant peak, different phase offsets, the relevant peak position is different, and the delay map estimation of the multipath signal can be obtained through the relevant peak position of the multipath correlation output. Assuming that there is no carrier phase offset between the multipath signal and the tracking signal, the locally generated code phase interval is 1/8 chip, for example, when the multipath signal number phase delay is 6/8 chip and 24/8 chip, the simulation is carried out, and the simulation results are as follows 3, through the relevant results, you can reverse the delay information of the multipath signal, which is very valuable for signal analysis (Fig. 3).

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(a)Code phase offset 6/8chip

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(b) Code phase offset 24/8 chips

Fig. 3. The effect of code phase shift on the correlation results

3.2 Specific Implementation of Delay Map Estimation The original multi-correlation result obtained by using the multiplexer correlator is shown in Fig. 4:

Fig. 4. Block diagram of a multi-way related implementation.

Compared with the traditional capture tracking control circuit, the output multichannel correlation delay map is estimated to require local generation of multiple pseudocodes, the local code loop controller controls the word and the initial phase of the control word through the control code frequency, and at the same time generates a multichannel pseudo-code with a code phase interval of 1/8 chip, after the signal capture is successful, downconvert the received signal. The in-phase branch and the orthogonal branch are simultaneously integrald with the locally generated multiple pseudocodes, and the relevant values are output.

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4 Conclusion A system and scheme for detecting the internal structure of an asteroid using a dualstation radar system are proposed, and the system is modeled and analyzed. This scheme can achieve (1) high-precision delay measurement of the electromagnetic signals inside the diffraction and transmission asteroid, and (2) multi-channel signal delay map estimation of diffraction and transmission asteroids by using multiplex-related technologies. High-precision delay information and delay map estimation use inversion technology to invert the internal structure of asteroids, and estimate the size and shape of asteroids, which is of great significance for the deep analysis and detection of asteroids. At the same time, the use of multi-channel related technology, adjust the correlation interval, can achieve a certain bandwidth, multi-path signal delay, phase, amplitude signal high-precision extraction, to provide a certain reference for the application of this aspect.

References 1. Ouyang, Z., Li, C., Zou, Y., et al.: The progress of deep space exploration and the development strategy of deep space exploration in our country. In: International Symposium on Deep Space Exploration Technology and Applied Science, Qingdao, pp. 237–243 (2002) 2. Zhang, R., Huang, J., et al.: The development overview of asteroid exploration. J. Deep Space Explor. 6(5), 417–423 (2019) 3. Rogez, Y., Puget, P., Zine, S., et al.: The CONSERT operations planning process for the Rosetta mission. Acta Astronaut. 125, 212–233 (2016) 4. Kofman, W., et al.: The comet nucleus sounding experiment by radiowave transmission (CONSERT): a short description of the instrument and of the commissioning stages. Space Sci. Rev. 128, 413–432 (2007). https://doi.org/10.1007/s11214-006-9034-9 5. Herique, A., Agnus, B., Asphaug, E., et al.: Direct observations of asteroid interior and regolith structure: science measurement requirements. Adv. Space Res. 62, 2141–2162 (2018) 6. Liu, D., Wang, D., Yang, R.: Study on time delay measurement method of asteroid detection radar. In: Special Group on Space Science and Space Exploration 2019 Annual Conference, Beijing (2019) 7. Liu, Y., Tian, Y., Li, G.: GPS multipath estimation based on maximum likelihood estimation. J. Aeronaut. 30(4), 1467–1471 (2009) 8. Chen, K., Gui, Q., Yue, L.: A multi correlator-based GPS multipath estimation method. J. Aeronaut. 33(9), 1241–1247 (2012) 9. Zhangjianzhong, Huangjilin: A spectral inversion algorithm for GPR signals of thin layers. Xiamen University (2013) 10. Su, H., Xu, F., Lu, S., et al.: Iterative ADMM for inverse FE–BI problem: a potential solution to radio tomography of asteroids. IEEE Trans. Geosci. Remote Sens. 54(9), 5226–5238 (2016) 11. Liu, S.: A comparative study of three optimization methods in full waveform inversion. Changan University (2019)

Review of Micro-vibration Modeling, Suppression, and Measurement for Spacecraft with High-Precision Meng Ge, Kuai Yu, Yongsheng Wu, Dong Wang, Guangyuan Wang(B) , and Chongzhou Yu Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. With the development of aerospace technology, the demand for precision, stability, security, and reliability of spacecraft with high-precision (e.g. remote sensing satellites, space telescopes, and large deployable antennas) is increasing. Micro-vibration has become an important factor affecting the performance of high-precision spacecraft. In this paper, micro-vibration modeling, suppression, and measurement for spacecraft with high-precision are reviewed. The shortcomings of the above aspects are introduced through engineering cases. The development directions of the micro-vibration technology for spacecraft are analyzed and summarized. Keywords: Micro-vibration · Integrated Modeling · Vibration suppression · Vibration measurement

1 Introduction Remote sensing satellites, space telescopes, and large deployable antennas have extremely high resolution, which makes payloads more sensitive to the stability, safety, and reliability of the spacecraft. For example, with the development of space technology, the ground resolution of earth observation satellites has improved significantly, from about 100 m in the 1970s to less than 1 m at present [1]. The micro-vibrations caused by the vibration source and payload on the spacecraft, as well as the space environment have become an important factor affecting the performance of high-precision spacecraft. Micro-vibration of the spacecraft is defined as the jitter response, whose amplitude is small and the frequency bandwidth is wide, caused by moving parts during the in-orbit operation. The influence of micro-vibration on general spacecraft is usually negligible, but it seriously affects key performances such as pointing accuracy, stability, and resolution of payloads on high-precision spacecraft. The importance of micro-vibration for high-precision spacecraft is mainly reflected in the following aspects: (1) The frequency coverage of micro-vibration ranges from almost zero to kilohertz. There are many types of disturbance loadings, which have strong coupling with © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 787–796, 2023. https://doi.org/10.1007/978-981-19-9968-0_95

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mechanical, optics, and electromagnetism. These factors make the micro-vibration mechanism complex and make it difficult to establish an accurate micro-vibration response prediction method. (2) The operating frequency of the payload is low and the payload structure is complex. The passive vibration isolation has the possibility of coupling with the structure. Therefore, it is necessary to propose new technologies and methods for microvibration suppression. (3) Due to the influence of micro-vibration, high-precision spacecraft need to compensate for and eliminate micro-vibration when working in orbit by obtaining different forms of micro-vibration data through measurement. In general, the magnitude of micro-vibration is 10–3 g, and the displacement is in the order of microns. A highsensitivity and high-resolution micro-vibration measurement system is required to further improve the measurement accuracy of the micro-vibration. In this work, micro-vibration modeling, suppression, and on-orbit measurement for spacecraft with high-precision are reviewed. The development directions of the microvibration technology for spacecraft are analyzed and summarized.

2 Integrated Modeling Method of Micro-vibration The Hubble Space Telescope (HST) was launched into orbit by the space shuttle Discovery on April 25, 1990. Shortly after the HST entered orbit, its payload was found to be inoperable whose reason was the attitude disturbance caused by the thermal vibration of the solar array when the HST entered and exited the earth shadow. Since then, the micro-vibration problem of spacecraft has attracted extensive attention. Research on the environment, integrated modeling, and comprehensive evaluation methods of microvibration is the basis and premise of micro-vibration in the spacecraft. In 1990, Eyerman comprehensively reviewed the problem of spacecraft micro-vibration [2]. Eyerman classifies the disturbances existing during spacecraft operation in orbit into two categories: One is the disturbance caused by the changes in the space environment in which the spacecraft is in orbit and the interaction between the spacecraft. Such disturbances are called “External” disturbances. The other is the “Internal” disturbances caused by interactions within the system or between subsystems during the normal operation of the spacecraft. The "External" disturbances mainly refer to the external effects of the space environment, including space microgravity, electromagnetic radiation, and alternating temperature. The "internal" disturbance is the additional disturbance to the spacecraft when the moving parts of the spacecraft work in orbit, such as the momentum wheel, the refrigerator, the solar array driving assembly (SADA), and the antenna driving mechanism. The effects of the internal and external disturbances on the spacecraft can be thoroughly understood through micro-vibration simulation and prediction, which can provide the theoretical basis for the design of high-precision spacecraft. In response to the impact of vibration on the quality of image, NASA began a comprehensive assessment of complicated high-precision spacecraft based on integrated modeling technology in the late 1980s, which has passed inspection by engineering

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practice. The simulation results can be obtained by the integrated modeling method, which models the internal and external disturbances in the spacecraft. Since the 1990s, Nexus, Space Interferometer Mission (SIM), James Webb Space Telescope (JWST), Terrestrial Planet Finder (TPF), and other high-precision space observation systems developed by NASA have complex structures, and their optical resolution has far exceeded the technical level at that time. Therefore, NASA entrusted the Space System Laboratory (SSL) of the Massachusetts Institute of Technology (MIT) to develop micro-vibration integration modeling software Dynamics Optics Structures (DOCS) [3]. In DOCS, the structural, optical, disturbance, and control models can be established respectively and integrated. Comprehensive performance analysis and design improvement can be carried out to achieve optimal overall performance. The framework of DOCS is divided into four parts: modeling, model preparation, analysis, and design, which can be adopted for the analysis of optical path difference, pointing jitter, fringe visibility, disturbance, and sensitivity (See Fig. 1).

Fig. 1. Framework of DOCS

Since then, NASA has developed the Integrated Modeling Environment (IME) for JWST, which contains jitter analysis and structure-heat-optical analysis [4]. In addition, IME also has modules such as process, data, and file management. To ensure the success of JWST, Jet Propulsion Laboratory (JPL) developed the Integrated Modeling of Optical Systems (IMOS) in 1998, which was successfully used for the modeling and analysis of JWST. In 2002, Ball developed the Integrated Telescope Model (ITM) for the verification of integrated simulation results of JWST [5]. With the development of synthetic aperture radar (SAR), microwave remote sensing satellites have made great progress, such as Lacrosse, AstroMesh in the United States, TerraSAR-X in Germany, TecSAR in Israel, and Cosmo-SkyMed in Italy. In the future, the imaging quality of high-orbit and large-aperture SAR antenna satellites is very sensitive to vibration.

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The modeling and analysis of electromechanical coupling dynamics for microwave remote sensing satellites has attracted extensive attention. For the large reflector antenna [6], active phased array antenna [7], and plate slot array antenna [8], the multi-field coupling analysis of displacement field, electromagnetic field, temperature field has been carried out, and the corresponding coupling model has been proposed. At present, the integrated modeling method of micro-vibration for high-precision spacecraft is mainly based on linear theory, which has limitations in modeling complex spacecraft. In addition, the existing integrated modeling method lacks coupling analysis of the thermal flutter induced by hot and cold alternation. For large aerospace structures, there is also a lack of a micro theoretical model for the force-heat transfer of assembly joints, which is not conducive to improving the analysis accuracy of high-precision spacecraft. The accurate modeling and prediction of micro-vibration for spacecraft are also the foundation of micro-vibration suppression.

3 Suppression of Micro-vibration After obtaining the dynamic response of the spacecraft, it is necessary to adopt appropriate micro-vibration suppression according to the magnitude of the micro-vibration and the structural design of the spacecraft to reduce the dynamic influence of the disturbance source on the sensitive payload. From the perspective of vibration transmission, micro-vibration suppression technology can be divided into micro-vibration suppression of disturbance sources, transmission paths, and sensitive payloads [1]. As for the method of vibration suppression, micro-vibration suppression can be classified into passive isolation and active suppression of micro-vibration [9]. Honeywell in the United States developed D-Strut tuned fluid damper and applied it to the vibration isolation of the momentum wheel on HST [10], as Fig. 2 shows. Its main feature is the use of an “Arched Flexure”, which can provide large damping within a small deformation of the structure, and can attenuate vibration by 40dB/decade in high-frequency bands.

Fig. 2. D-struct vibration isolator

Fig. 3. CMG isolators on WorldView-II

Stewart platform has the characteristics of six degrees of freedom, compact structure, large loading capacity, and easy decoupling, which is suitable as a high-precision and

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high-stability vibration isolator. Numerous tests have proved that the Stewart platform can act as a vibration isolation system during spacecraft operation and improve the imaging quality of optically sensitive equipment. Both WorldView-II and Chandra Xray observers adopted this platform to effectively isolate the disturbance of the CMG and the momentum wheel [11], as exhibited in Figs. 3 and 4.

Fig. 4. Momentum wheel isolator on Chandra X-ray

Fig. 5. SUITE active/passive vibration isolation platform

The Vibration Isolation and Suppression System (VISS) is developed by the US Air Force Research Laboratory (AFRL) and manufactured in the United Kingdom [12]. VISS is the world’s first in-orbit vibration isolation system to be tested in space. The system has been used as a mature technology in small medium-wave infrared telescopes. Satellite Ultraquiet Isolation Technology Experiment (SUITE) was developed by AFRL in partnership with CSA Engineering (See Fig. 5). SUITE effectively isolates the micro-vibration of the momentum wheel, in which each actuating rod is connected in series with an active and a passive isolation unit, and the active isolation unit uses a piezoelectric ceramic as the actuating and sensing element. SUITE has been successfully applied to the data control system of the small satellite PICOSat and has achieved an excellent isolation effect. In the design scheme of JWST, in addition to the vibration isolation of the momentum wheel, the entire optical facility is also isolated [14], as shown in Fig. 6. The isolation system consists of four rod-shaped isolators in a criss-cross pattern. The isolator material is graphite with damping rubber attached to its surface. The isolation frequency is close to 1 Hz, which can minimize the influence of disturbance on the imaging quality of the optical payload. At present, the mechanism of passive suppression of micro-vibration is insufficient. For example, there is no effective damping device for low-frequency vibration of large flexible structures. The lower the isolation frequency, the greater the deformation of the isolation device, which cannot be applied to isolation objects that have strict requirements on positional accuracy. In addition, the frequency band of the passive isolation device is a constant value, and the disturbance frequency of the vibration source that works for a long time will change, which will further affect the isolation efficiency of the isolator. Due to the environmental differences between the universe and the ground, the actual on-orbit frequency of the spacecraft may be coupled with the vibration isolator, resulting in a decrease in isolation efficiency or even resonance. Therefore, with the increasing requirements for micro-vibration suppression in the field of aerospace engineering, it is necessary to develop new suppression mechanisms, technical approaches,

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Fig. 6. Isolators on JWST

and methods of micro-vibration to solve the technical bottlenecks currently encountered. Especially for the nonlinear problems faced by large and complex space structures, it is necessary to explore a new mechanism of vibration energy localization, which can provide a theoretical, method, and technical support for the micro-vibration suppression under the multi-source excitation of complex spacecraft in the future.

4 On-orbit Measurement of Micro-vibration For high-precision spacecraft, it is not only necessary to ensure a safe and reliable service life, but also to monitor the real-time micro-vibration response of the spacecraft due to internal and external disturbance. Micro-vibration response could affect the performance of the precision payloads and lead to a mission failure. The measurement methods of disturbance sources are listed in Table 1. According to the different measurement objects, the on-orbit measurement of microvibration includes linear vibration measurement and angular vibration measurement. 4.1 Line Vibration Measurement Linear vibration measurement mainly adopts quartz flexible acceleration sensor, Micro electro mechanical system (MEMS) acceleration sensor, and grating acceleration sensor. SAMS-II was developed by NASA in 2000 and employed three orthogonally mounted QA-3100 quartz flexible accelerometers for transient vibration monitoring of spacecraft, astronauts, and equipment [15], as shown in Fig. 7. The QA-3100 has a resolution specification of up to 0.01 µg. The same series of QA-3000 is in Fig. 8. In addition, NASA also developed advanced microgravity acceleration measurement systems (AMAMS) [16], as depicted in Fig. 9. MEMS accelerometers are MEMS devices that appeared and were commercialized earlier. At present, common MEMS accelerometers can be divided into capacitive, resonant, optical, piezoresistive, piezoelectric, tunnel current, and thermal convection MEMS

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Table 1. Method and accuracy of micro-vibration measurement. Measurement method

Physical quantity

Measurement accuracy

Measurement range

Accelerometer

Acceleration

10–5 g

0.1–5000 Hz

Six-axis force platform

Force/Moment

10−3 N/10–3 Nm

0.1–5000 Hz

Diaphragm force sensor

Force

10−3 N/10–3 Nm

3–5000 Hz

Differential accelerometer

Angular acceleration

10–1

3–500 Hz

Line laser displacement sensor

Displacement

10–9 m

0–5000 Hz

Interferometric laser displacement sensor

Displacement

10–9 m

0–5000 Hz

Laser gyro

Angular velocity

10–3

0–1000 Hz

Permanent magnet sensor

Angular displacement

10–5

0–5000 Hz

Fiber optic gyroscope

Angular velocity

10–1

0–5000 Hz

Angular rate sensor

Angular velocity

10–5

0–5000 Hz

Fig. 7. SAMS-II

Fig. 8. QA-3000 accelerometer

Fig. 9. AMAMS

accelerometers according to the sensing method. Honeywell, Allied Signal, and Applied MEMS have developed three MEMS accelerometers for the United States respectively, whose accuracy of acceleration can reach the order of µg. Among them, Applied MEMS sensor has the best comprehensive performance. Fiber Bragg grating (FBG) accelerometers measure vibration information by monitoring the wavelength drift of FBG, which have the advantages of high sensitivity, fast measurement speed, and strong anti-electromagnetic interference [17].

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4.2 Angle Vibration Measurement Angular vibration measurement mainly includes attitude-based measurement, magnetohydrodynamics-based measurement, and laser gyro-based measurement. The ALOS is a high-resolution earth observation satellite belonging to the Japan Aerospace Exploration Agency (JAXA) for mapping, environmental monitoring, disaster management support, and resource surveys. The attitude and orbit control system (AOCS) of the ALOS adopted traditional sensors such as gyroscopes, earth sensors, sun sensors, star trackers, and GPS receivers. Its Nyquist frequency is 5 Hz, which does not meet the measurement requirements of angular vibration. The Applied Technology Associates (ATA) developed a broadband angular rate sensor (ARS) based on the principle of magnetohydrodynamics (MHD) in 1984 and designed the MHD inertial measurement unit in 1997 [18]. The ARS-12 can measure inertial angular motion less than 50 nrad at discrete frequency points and is not affected by linear acceleration, but the sensitivity of the cross-axis is limited by incorrect physical calibration. The latest and most sensitive ARS is the ARS-14. Diagrams of ARS-12 and ARS-14 are shown in Figs. 10 and 11.

Fig. 10. Diagram of ARS-12

Fig. 11. Diagram of ARS-14

Laser gyroscopes are developed from ring Interferometers. When there is an angular velocity, the light waves in different directions will have a frequency difference, which will result in interference fringes. The photodetector detects the moving speed of the interference fringes, then outputs the corresponding frequency signal, and finally obtains the angular velocity. There is no mechanical jitter in the laser gyro, and the output signal is a pure sine wave with a small delay. The advantages of laser gyroscope are high precision, high sensitivity, high angular resolution, and large dynamic range.

5 Conclusions With the improvement of aerospace technology, the integrated modeling, suppression, and on-orbit measurement of micro-vibration can effectively ensure the performance of high-precision spacecraft. This work reviews the research on the above technologies, and summarizes the research directions in the future as follows:

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(1) It is necessary to establish a general multidisciplinary integrated modeling method and system with high precision and high efficiency of mechatronics-thermaloptical-control. The theoretical method of force-heat transfer on assembly joints of large spacecraft will help to promote the prediction accuracy of micro-vibration. (2) Research on the mechanism of vibration suppression, and the development of new passive isolation devices and active suppression methods for micro-vibration are necessary for complex payloads with special constraints such as displacement, weight, and geometric dimensions. (3) Further improving the accuracy of on-orbit measurement of micro-vibration to obtain on-orbit micro-vibration data is of great significance for research work such as fault diagnosis and image restoration of high-precision spacecraft.

References 1. Zhang, Q.J., Wang, G.Y., Zheng, G.T.: Micro-vibration attenuation methods and key techniques for optical remote sensing satellite. J. Astronaut. 36(2), 125–132 (2015) 2. Eyerman, C.E.: A Systems Engineering Approach to Disturbance Minimization for Spacecraft Utilizing Controlled Structures Technology. Massachusetts Institute of Technology, Boston (1990) 3. Miller, D.W., De Weck, O.L., Mosier, G.E.: Framework for multidisciplinary integrated modeling and analysis of space telescopes. In: Integrated Modeling of Telescopes, vol. 4757, pp. 1–18. SPIE (2002) 4. Stone, C.M., Holtery, C.: The JWST integrated modeling environment. In: 2004 IEEE Aerospace Conference Proceedings, vol. 6, pp. 4041–4047. IEEE (2004) 5. Atkinson, C.B., Harrison, P., Matthews, G., Atcheson, P.: Integration and verification of the James Webb Space Telescope. In: Optical Manufacturing and Testing V, vol. 5180, pp. 157– 168. SPIE (2003) 6. Duan, B.Y., Wang, C.S.: Reflector antenna distortion analysis using MEFCM. IEEE Trans. Antennas Propag. 57(10), 3409–3413 (2009) 7. Wang, C.S., Duan, B.Y.: Coupled structural-electromagnetic-thermal modeling and analysis of active phased array antennas. IET Microw. Antennas Propag. 4(2), 247–257 (2010) 8. Song, L.W., Duan, B.Y., Zheng, F., Zhang, F.: Performance of planar slotted waveguide arrays with surface distortion. IEEE Trans. Antennas Propag. 59(9), 3218–3223 (2011) 9. Tan, T.L., et al.: Overview of micro-vibration testing, isolation and suppression technology for spacecraft. Aerosp. Shanghai 31(6), 36–45 (2014) 10. Davis, L.P., Wilson, J.F., Jewell, R.E.: Hubble space telescope reaction wheel assembly vibration isolation system. NASA Rep. 87, 669–690 (1986) 11. Bronowicki, A.: Forensic investigation of reaction wheel nutation on isolator. In: 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, p. 1953 (2008) 12. Cobb, R.G., et al.: Vibration isolation and suppression system for precision payloads in space. Smart Mater. Struct. 8(6), 798–812 (1999). https://doi.org/10.1088/0964-1726/8/6/309 13. Joshi, A., Kim, W.J.: System identification and multivariable control design for a Satellite UltraQuiet isolation technology experiment (SUITE). Eur. J. Control. 10(2), 174–186 (2004) 14. Mosier, G.E., et al.: The role of integrated modeling in the design and verification of the James Webb Space Telescope. In: Space Systems Engineering and Optical Alignment Mechanisms, vol. 5528, pp. 96–107. SPIE (2004)

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15. NASA Technical Reports Server. https://ntrs.nasa.gov/citations/20020082952. Accessed 07 Sep 2013 16. NASA Technical Reports Server. https://ntrs.nasa.gov/citations/20050215024. Accessed 07 Sep 2013 17. Luo, W., Cao, J.H., Lu, Z.X., Li, H.L., Li, W.J.: Progress of on-orbit autonomous measurement for spacecraft structure deformation. Unmanned Syst. Technol. 3(5), 54–59 (2020) 18. Eckelkampbaker, D., Sebesta, H.R., Burkhard, K.: Magnetohydrodynamic inertial reference system. Proc. SPIE – Int. Soc. Opt. Eng. 4025, 99–110 (2000) 19. Zou, W., Huang, Y., Liang, K., Zhu, K., Xue, Z.: Calibration and measurement method of laser gyro goniometer. Metrol. Sci. Technol. 66(4), 40–47 (2022)

Satellite Gravity Gradient Measurement Technology Based on Superconducting Suspension Ming Li1(B) , Yue-xin Hu1 , and Ming-xue Shao2 1 DFH Satellite Co., LTD., Beijing 100094, China

[email protected] 2 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100090, China

Abstract. In order to improve the accuracy of satellite gravity gradient measurement, the satellite gravity gradient measurement technology based on superconducting suspension is studied. Firstly introduce the principle of satellite gravity gradient measurement, analysis the measurement system, principle and mathematical model of gravity gradient satellite, give the relationship between the accuracy of gravity field inversion and the accuracy of gravity gradient measurement. Then the basic principle of superconducting gravity gradiometer is introduced, the key technology of load and platform for satellite gravity gradiometry are preliminary analyzed, and the preliminary solution ideas are put forward, which provide a reference for the further research of satellite gravity gradiometry in the future. Keywords: Satellite gravity gradient · Superconducting suspension · Gravity gradiometer

1 Introduction Superconducting gravity gradiometer is a precision relative gravity measurement instrument built by using superconductivity and working under the condition of liquid helium temperature [1–6]. In the 1990s, Paik of Stanford University in the United States and his colleagues began to develop superconducting gravity gradiometer [7, 8], whose application goals are gravitational wave detection, space gravity measurement and basic physics research. In 2002, the Paik research group, which has moved to the University of Maryland, reported as low as 0.02 E/Hz1/2 @0.5 Hz Instrument noise background [9]. Compared with the electrostatic levitation gravity gradiometer, the superconducting gravity gradiometer has only differences in the measurement principle of the accelerometer. The former uses the superconducting detection quality to replace the electromagnetic detection quality of the latter, and uses the superconducting quantum interferometer to replace the capacitive device to measure the displacement of the detection quality. Because the displacement of superconducting induction detection quality has higher sensitivity than electrostatic suspension method, it also has greater development potential. This paper introduces the satellite gravity gradient measurement technology based on superconducting gravity gradiometer. It is expected that the measurement sensitivity can reach 0.01 mE, reaching the international leading level. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 797–803, 2023. https://doi.org/10.1007/978-981-19-9968-0_96

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2 Principle of Satellite Gravity Gradient Measurement 2.1 Satellite Gravity Gradient Measurement System Satellite gravity gradient measurement refers to the measurement mode combining highlow satellite tracking (SST-HL) and satellite gravity gradient (SGG) to obtain a global high-precision gravity field model. The high-low satellite tracking satellite mode mainly relies on the high-precision dual frequency GNSS receiver carried on the LEO satellite to establish a tracking link with the high orbit GNSS navigation satellite to obtain the high-precision orbit information of the LEO satellite. SGG measurement mode is mainly completed by gravity gradiometer. 2.2 Gravity Field Model In geodesy, the potential function v of the gravitational field is usually expressed as the sum of the normal potential function u and the disturbed potential function T, that is V=U+T

(1)

When the origin of the coordinate system coincides with the center of mass of the earth and the mass of the selected reference ellipsoid is equal to the mass of the earth, the spherical harmonic series expansion expression of the disturbance potential function T of the earth’s gravitational field is as follows     GM ∞ R l+1 l T(r, θ, λ) = C¯ lm cosmλ + S¯ lm sinmλ P¯ lm (cosθ ) (2) l=2 r m=0 R R: Is the average radius of the earth; GM: is the gravitational constant; l, m: are the order and degree of spherical harmonic expansion respectively; C lm , S lm : fully normalized spherical harmonic coefficient of disturbance relative to normal gravitational potential, which is related to the internal mass distribution of the earth and is an infinite set; P lm (cosθ ): Is a fully normalized joint Legendre function; r, θ, λ: Are the radius, latitude and longitude of the observation point under the geocentric spherical coordinate  l+1 : is the attenuation factor, reference system, r = r + h, h is the satellite height; Rr which describes the attenuation of the gravity field with the increase of orbital height. The gravity gradient is the second derivative of the gravitational potential V and the first derivative of the gravitational acceleration. The acceleration of gravity is a vector,    and the expression in space is g = gx , gy , gz . The disturbance potential function T represents the change of gravitational acceleration g in space, that is, the second derivative of gravitational potential V in space, and the unit is E (1E = 10−9 ms−2 /m). Gravity gradient is a second-order tensor with nine components in space, namely ⎡ ∂2V ∂2V ∂2V ⎤ ⎡ ⎤ VX X VX Y VXZ ∂X 2 ∂X ∂Y ∂X ∂Z 2 2 2 ⎢ ∂ V ∂ V ∂ V ⎥ ⎣ = VYX VY Y VZY ⎦ (3) T = ⎣ ∂Y ∂X ∂Y 2 ∂Y ∂Z ⎦ 2 2 2 ∂ V ∂ V ∂ V V V V ZX YZ ZZ 2 ∂Z∂X ∂Y ∂Z

∂Z

Therefore, if the gravity gradient t of each position measured by GNSS can be obtained through satellite measurement data, the potential coefficient set {c can be solved   C lm , S lm , and then the gravity field model is obtained by inversion.

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2.3 Measurement Model of Gravity Gradiometer Gravity gradiometer is a combination of high-precision accelerometers. Each pair of accelerometers is symmetrically installed at the satellite centroid, and the center of the gradiometer coincides with the satellite centroid. The primary key to the realization of gradient measurement is the high-precision acceleration of the accelerometer test mass relative to the satellite. The output equation of each accelerometer is  a out













= a TM − a SC = g sc + g TM − a cp + a ng

(4)

  a TM , a SC : the quality of the accelerometer and the acceleration received by the   satellite; g TM , g sc : the accelerometer inspection mass and the gravitational acceleration  received by the satellite; a ng : the external non-conservative forces on satellites other  than gravity; a cp : the acceleration introduced by satellite rotation (Fig. 1).

Fig. 1. Gravity gradient measurement mode.

Let the satellite rotation angular speed be ω         a cp = − ω × ω × r + r × ω  + 2 ω × r 

(5)

Therefore, the output of each pair of accelerometers i and j can be written as           a i = g sc − g i,TM + ω × ω × r i + ω  × r i + a ng

(6)

 aj

         = g sc − g j,TM + ω × ω × r j + ω  × r j + a ng

(7)

The differential mode output of the accelerometer is                   a ij = a i − a j = − g i,TM − g j,TM + ω × ω × r i − r j + ω  × r i − r j          (8) = − g i,TM − g j,TM + ω × ω ×l + ω  × l

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The gravity gradient value is the axial change rate of the differential mode of the test mass of the two accelerometers affected by the gravity acceleration 







g i,TM − g j,TM g i,TM − g j,TM ∂ 2V = =    2 ∂r ri− rj l Written in matrix form as ⎡  ⎤  a ij,x  ⎥ ⎢  a ij = ⎣  a ij,y ⎦ =   a ij,z ⎡ ⎤ ⎡ ⎤   −Vxx − ωy2 − ωz2 −Vxy − ωx ωy − ωz −Vxz − ωx ωz − ωy lx   ⎥ ⎢ 2 2 ⎣ ⎣ −Vyx − ωx ωy − ωz −Vyy − ωx − ωz −Vyz − ωy ωz − ωx ⎦ · ly ⎦    lz −Vzx − ωx ωz − ωy −Vzy − ωy ωz − ωx −Vzz − ωx2 − ωy2

(9)

(10)

It can be seen from the above formula that the differential mode information of the accelerometer contains the gravity gradient tensor and the information of the satellite angular motion to be deducted. Therefore, the observation matrix of gravity gradient is ⎤ ⎡ a 14,x a25,x a36,x ⎡ ⎤ ly lz xx xy xz x ⎢ al14,y a25,y a36,y ⎥ ⎥  = ⎣ yx yy yz ⎦ = ⎢ (11) ly lz ⎦ ⎣ lx a14,z a25,z a36,z zx zy zz lx

ly

   = −[T] + [ω] + ω

lz

(12)   [T] is the gradient matrix, [ω] is the angular velocity matrix, ω is the angular acceleration matrix. In order to get the gravity gradient value from the observation, the influence of angular velocity and angular acceleration must be deducted.

3 Satellite Superconducting Gravity Gradient Measurement Technology 3.1 Basic Principle of Superconducting Gravity Gradiometer Superconducting gravity gradiometer is still a relative gravity instrument based on spring vibrator, which measures the variation of gravity gradient with time. When the superconductor is close to the superconducting coil carrying current, due to the complete diamagnetic magnetism of the superconductor, the superconductor in the magnetic field spontaneously forms a shielding current on its surface. The magnetic field generated by the shielding current can offset the external magnetic field, making the magnetic field inside the superconductor constant at 0 (Fig. 2). At the same time, the effective inductance of the superconducting coil will also change. Moreover, because the magnetic flux of the superconducting coil loop is conserved, it will cause the superconducting coil to produce a changing small current on

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Fig. 2. Schematic diagram of superconducting accelerometer.

the basis of the initial current. The current is coupled to the squid system through the superconducting transformer. By detecting the change of current with squid, the change of relative position between the superconductor and the superconducting coil can be obtained, which constitutes a superconducting displacement sensing circuit. At the same time, the superconductor will be pushed back to its original position by the force of the magnetic field, which forms an electromagnetic spring. Using the above basic principles, we can combine two displacement sensing circuits into a differential measurement circuit, measure the displacement change of a test mass, and form a superconducting accelerometer. Combine the two superconducting accelerometers, measure the difference of the output current of the two accelerometers, and get the difference of the two test mass accelerations to form a uniaxial gravity gradiometer. 3.2 Key Technology of Superconducting Gravity Gradient Load 1. High Q superconducting suspension technology Thermal noise is one of the physical limitations of high-precision instruments. The thermal noise power spectrum is proportional to the temperature and inversely proportional to the quality factor Q of the system, so reducing the temperature can reduce the thermal noise. The liquid helium temperature of the superconducting gravity gradiometer working near 4 K is about 75 times lower than that of the instrument working at room temperature (about 300 K), but it is not enough for the gradient measurement ability to reach 10−5 E, so it is very important to improve the quality factor Q of the system. 2. Superconducting differential displacement sensing technology High precision gravity gradient measurement is achieved by detecting the displacement of the inspection mass. Realizing the gradient measurement of 10−5 E requires detecting the displacement change of the inspection mass to 10–17 m, that is, one percent of the distance of an atomic nucleus, which is a challenging technology. The superconducting differential displacement sensing technology based on the principle of magnetic flux quantization can meet this requirement. 3. High precision magnetic flux detection technology The readout noise of the instrument is one of the intrinsic sources that limit the performance of the superconducting gravity gradiometer. The readout of the signal is realized by detecting the change of magnetic flux with high accuracy. To realize the

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10−5 E gradient measurement readout device, it is necessary to detect one millionth of the basic magnetic flux, which is an index that is difficult for ordinary instruments to reach. This not only requires the matching of design parameters, but also includes a lot of low-temperature electronics work. 3.3 Key Technologies of Superconducting Gravity Gradient Satellite Platform 1. Space 4 K low temperature long-term maintenance technology The new superconducting gravity gradiometer needs to ensure that the superconducting material enters the superconducting state, needs the satellite platform to provide 4 K low temperature environment for it and maintain it for a long time, and needs to use liquid helium refrigeration to realize 4 K working environment. Because the temperature of liquid helium is relatively low, it is easy to evaporate by heating. If heated unevenly, it will lead to thermal stratification of the fluid in the liquid helium cooler, resulting in local evaporation of liquid helium, which increases the difficulty of evaporation control. At the same time, how to effectively seal the liquid helium cooler, how to achieve the ultimate passive insulation, and how to efficiently couple with superconducting devices are all technical problems to be faced. 2. Influence of attitude error on gravity gradient tensor measurement and its processing technology Gravity gradient is a tensor, and the final expression of observation must be placed in a certain coordinate system, so high-precision attitude information is inevitable; At the same time, the satellite is in motion, and the accuracy of the gravity gradiometer is also affected by the rotation of the satellite, which needs to be deducted by the attitude information. The above problems are mainly solved from two aspects. One is to study highprecision data processing methods. The gravity tensor invariant will be used to invert the earth’s gravitational field model. The gradient tensor invariant does not change with the change of the coordinate system, so the accuracy requirement of the attitude is reduced. The second is to rely on the satellite platform technology with high stability and high precision to measure the rotation and attitude of the satellite with high precision, so as to meet the requirements of high-precision gradient measurement.

4 Conclusions This paper introduces the space superconducting gravity gradient measurement technology with 0.01 mE sensitivity, introduces the principle of satellite gravity gradient measurement and superconducting gravity gradient measurement, and analyzes the key technologies of superconducting gravity gradient load and satellite platform, which provides a reference for the further research of satellite superconducting gravity gradient measurement task.

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References 1. Xu, H.: Progress in gravity measurement technology and gravity mechanics. Geospatial Inf. 1(3), 3–4 (2003) 2. Xu, H.: Discussion on some problems in the work of refined geoid in China. Geospatial Inf. 4(5), 1–4 (2006) 3. Rummel, R., Horwath, M., Yi, W., Albertella, A., Bosch, W., Haagmans, R.: GOCE, satellite gravimetry and antarctic mass transports. Surv. Geophys. 32(4–5), 643–657 (2011). https:// doi.org/10.1007/s10712-011-9115-5 4. Ning, J.: Follow the development of the world and devote ourselves to the study of the earth’s gravity field. J. Wuhan Univ. Inf. Sci. Ed. 26(6), 471–474 (2001) 5. Zheng, W., Xu, H., Zhong, M.: Research progress of international satellite gravity gradient measurement program. Sci. Surveying Mapp. 35(2), 57–61 (2010) 6. Liu, X., Liu, X., Ma, D.: Superconducting gravity instruments: opportunities and challenges. Navig. Control 18(3), 7–13 (2019) 7. Chan, H.A., Paik, H.J.: Superconducting gravity gradiometer for sensitive gravity measurements. I. Theory. Phys. Rev. D Part. Fields 35(12), 3551–3571 (1987) 8. Chan, H.A., Moody, M.V., Paik, H.J.: Superconducting gravity gradiometer for sensitive gravity measurements. II. Experiment. Phys. Rev. D Part. Fields 35(12), 3572–3597 (1987) 9. Moody, M.V., Paik, H.J., Canavan, E.R.: Three-axis superconducting gravity gradiometer for sensitive experiments. Rev. Sci. Instrum. 73(11), 3957–3974 (2002) 10. Cai, L.: Analytical error analysis method of satellite gravity measurement. Doctoral dissertation of Huazhong University of Science and Technology (2013)

The Mathematic Analytical Results of Self Excited Flyback Converter with Double Switches Shan Li(B) , Dinghao Sun, Liwei Wang, Xuhui Liu, and Xudong Wang Beijing Institute of Control Engineering, Beijing 100190, China [email protected]

Abstract. The primary power voltage of spacecraft is increasing gradually in our country. The DC/DC converter in spacecraft, its circuit topology is changing in order to reduce the voltage of the main switch with its shutoff. The double switch forward converter, The double switch flyback converter, Half-bridge, Full-bridge, Phased-shifted Full-bridge converter. In these five circuit topologies, components and cost used in the double switch flyback converter circuit are the least and the lowest without PWM integrated circuit. The self excited flyback converter with double switches is discussed with the mathematic analytical method in this paper. By using the input voltage, the output voltage, the output power and the parameters of the transformer of the converter as independent variables, we construct the representation of the operating period, the representation of the maximum magnetic density and the representation of the ratio of duty time D. On the basis of this, we get an important conclusion: the relationship between energy transmission coefN0 1 ficient η, the ratio of duty time and ( N V )/( V ), it shows that energy transmission 1

0

N0 1 coefficient η is higher when the value of ( N V )/( V ) is 0.3–0.6. 1

0

Keywords: Flyback converter with double switches · The operating period · The maximum magnetic density · The ratio of duty time D · Energy transmission coefficient η

The primary power voltage of spacecraft is increasing gradually in our country. The DC/DC converter in spacecraft, its circuit topology is changing in order to reduce the voltage of the main switch with its shutoff. The double switch forward converter, The double switch flyback converter, Half-bridge, Full-bridge, Phased-shifted Full-bridge converter. In these five circuit topologies, components and cost used in the double switch flyback converter circuit are the least and the lowest without PWM integrated circuit. The operating process theory [1–8] about converter and Pulse width modulator (PWM) has been constructed with the mathematic analytical method. The operating process about the double switch flyback converter has been described in the literature [9]. The flyback converter with double switches shown in Fig. 1 is discussed with the mathematic analytical method in this paper. By using the input voltage, the output voltage, the output power and the parameters of the transformer of the converter as independent variables, we construct the representation of the operating period, the representation of the maximum magnetic density and the representation of the ratio of duty time. On © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 804–813, 2023. https://doi.org/10.1007/978-981-19-9968-0_97

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the basis of this, we get an important conclusion: the relationship between energy transN0 1 mission coefficient η, the ratio of duty time D and ( N V1 )/( V0 ), it shows that energy

N1 N0 transmission coefficient η is higher when the value of ( V )/( V ) is in the range of 0.3 1 0 – 0.6. This paper provides a theoretical basis for designing the flyback converter.

1 Discussion Question The circuit model of the flyback converter with double switches shown in Fig. 1(a) (not marked K1 and K2 controlling circuits) and the symbol definition in the Fig. 1(a) as following: V1 : the primary power voltage. K1 , K2 : Two synchronized switches. D1 , D2 : Diode (ideal model) for leakage inductance energy of primary winding releases through the primary power supply. Transformer model T: N1 : Turns of primary winding. N0 : Turns of secondary winding. k: Coupling coefficient between primary winding and secondary winding. A: magnetic circuit area (m2 ). δ: magnetic circuit gap(m). D3 : output diode (ideal model). C: Output filter capacitor (Assume enough capacitance). R: Load resistance (Decided by output voltage and output power). V0 : Output voltage(V). P0 : Output power(W). Derived from above defined transformer parameters, Primary winding inductance: L1 =

μ0 AN12 δ

(1)

L0 =

μ0 AN02 δ

(2)

Secondary winding inductance

μ0 : absolute permeability in vacuum, μ0 = 4π · 10−7 (T · m/A). Derived from formula (1) and (2) L0 = L1

N02 N12

(3)

Coupling inductance between two winding  N0 M = k L1 L0 = kL1 N1

(4a)

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(a)

(b) d2 D1

K1 T

S2 D2

V1

i1

d1 S1

(c)

M

Vm

i0

Lo-M

L1-M

C

R Vo

D3

K2

i1(t),i0(t) Po

P2

i1(t)

i0(t)

i1(Ton)

O

P1 Ton

i0(Ton+T1)

T1

P4

P3 T

T2

t

Toff

Fig. 1. (a) Topologic diagram of flyback converter with double switches. (b) Electrical circuit diagram of Fig. 1(a). (c) Waveforms of i1 (t) and i0 (t)

M = kL0

N1 N0

(4b)

The electric current symbol quoted in this paper i1 (t) primary winding current through transformer T; i0 (t) secondary winding current through transformer. It is described that the operating process of the flyback converter with double switches in Fig. 1(c): During the period of K1 and K2 turning-on (Ton ), i1 (t) across K1 , K2 and primary winding links with the primary power supply, the current is rising linearly to the point of i1 (Ton ) from zero point, then forming the line OP0 in the Fig. 1(c). During the period of K1and K2 turning-off (Toff ) the induced potential direction of primary winding reverses, the process is divided into two timing periods T1 and T2 . During the period of T1 , the energy of supply coupling inductance on one side feed back to the primary power acrossD1 , D2 together with the energy of primary leakage inductance L1 -Muntil exhausting of energy of primary leakage inductance. In the end forming the line P0 P3 in the Fig. 1(c). During the period of T1, the energy of coupling inductance transfers to primary winding because of reversing of the induced potential direction of the coupling inductance. i0 (t) rises linearly over time from zero though D3 , forming the line P1 P2 in the Fig. 1(c). During the period of T2 , i1 (t) = 0; the induced

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potential of secondary winding isV0 , the whole energy stored in the secondary winding transfers to output circuit gradually though D3 , forming the line P2 P4 in the Fig. 1(c). The energy transmission is finished at one time from primary to secondary windings in the flyback converter with double switches, shown in the line OP1 P2 P4 in the Fig. 1(c), as the same operating process repeat. The paper purpose is to construct the representation of the operating period T, the representation of the maximum magnetic density B and the representation of the ratio of duty time D by using the input voltageV1 , the output voltageV0 , the output power Po and the parameters of the transformer of the converter as independent variables N1 , N0 , k, A, δ.

2 Operating Equation During Ton Period The equation i1 (t) through primary winding during Ton period is as following V1 di1 = dt L1

(5a)

i1 (0) = 0; when t = 0

(5b)

Formula (5a) (5b) solution as followings: i1 (t) =

V1 t L1

(6)

V1 Ton L1

(7)

i1 (t) rises to i1 (Ton ) during the period of Ton i1 (Ton ) =

The electric charge Q1 flowing out from primary power supply during the period of Ton as shown in the area OPO P1 O in the Fig. 1(c) Q1 =

1 i1 (Ton )Ton 2

(8)

Q1 flowing out from primary power supply, so embedded energy W1 in Q1 W1 =

1 V1 i1 (Ton )Ton 2

(9)

Ton of formula (9) is substituted by Ton of formula (7) to get formula (10) W1 =

1 2 L1 i (Ton ) 2 1

(10)

Substitute formula (1) into formula (7) to get i1 (Ton ) =

δ V1 Ton μ0 A N12

(11)

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3 Operating Equation During Toff Period When K1 , K2 turning-off, operating in the period of Toff divided into T1 and T2 . 3.1 Operating Equation During the Period of T1 During the period of T1 , the current flows across the primary windings at the same time across the secondary windings, as shown in Fig. 1(b), constructing operating equation according to mutual inductance principle [10]. L1

di0 di1 +M = −V1 dt dt

(12a)

M

di1 di0 + L0 = −V0 dt dt

(12b)

The initial condition of formula (12a) (12b) as following. i1 (Ton ) =

V1 Ton , when t = Ton L1

i0 (Ton ) = 0; when t = Ton

(13a) (13b)

Substitute formula (3) (4) into formula (12a) (12b) to get

k

di1 N0 di0 V1 +k =− dt N1 dt L1

(14a)

N 2 di0 V0 N0 di1 + 02 =− N1 dt L1 N1 dt

(14b)

Derived from formula (14a) (14b) together as following di1 V1 N1 V0 =− (1 − k ) 2 dt (1 − k )L 1 V1 N0

(15a)

V1 di0 N1 N1 V0 = (k − ) 2 dt (1 − k )L 1 N0 V1 N0

(15b)

Derived from formula (13a) (15a) as following i1 (t) =

V1 V1 N1 V0 Ton − (1 − k )(t − Ton ) 2 L1 (1 − k )L 1 V1 N0

(16)

If formula (16) equals to zero, then T1 =

1 − k2 1 − k NV11 NV00

Ton

(17)

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Derived from formula (13b) (15b) as following i0 (t) =

N1 V1 N1 V0 (k − )(t − Ton ) 2 (1 − k )L 1 N0 V1 N0

(18)

Substitute t = Ton + T1 and formula (17) into formula (18), further substitute formula (7) into formula (18) to get i0 (Ton + T1 ) =

N1 V0 N1 k − V1 N0 i1 (Ton ) N0 (1 − k NV1 NV0 ) 1

(19)

0

3.2 Operating Equation During the Period of T2 The operating equation during the period of T2 is following in the Fig. 1(a) L0

N 2 di0 di0 = L1 02 = −V0 dt N1 dt

(20)

The initial condition of the formula (20) is given by the formula (19). The solution of i0 (t) during the period of T2 obtained from the formula (19) (20) as following i0 (t) =

N1 V0 N12 V0 N1 k − V1 N0 i (T ) − (t − Ton − T1 ) 1 on N0 (1 − k NV1 NV0 ) N02 L1 1

(21)

0

Substitute formula (7) into formula (21), if formula (21) equals to zero, then T2 =

k NV11 NV00 − 1 1 − k NV11 NV00

Ton

(22)

Toff derived from formula (17) (22) as following Toff = T1 + T2 = k

V1 N0 Ton N1 V0

(23)

The operating period T obtained from Ton and formula (23) T = Ton + Toff = (1 + k

V1 N0 )Ton N1 V0

(24)

The ratio of duty time D derived from formula (24) D=

1 Ton = T 1 + k NV11 NV00

(25)

Substitute formula (24) into formula (11) to get i1 (Ton ) =

δ μ0 AN1

T N1 V1

+ k NV00

(26)

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4 The Energy Transmission Coefficient η Q0 Output electric charge as shown in the area P1 P2 P4 P3 P1 in the Fig. 1(c). From formula (19) (23) to get Q0 = k

N1 V0 V1 N0 1 − k NV11 NV00

k−

(

1 V1 i1 (Ton )Ton ) 2 V0

(27)

Output energy W0 is multiplied by Q0 of formula (27) and output voltageV0 . W0 = k

N1 V0 V1 N0 1 − k NV11 NV00

k−

1 ( V1 i1 (Ton )Ton ) 2

(28)

Known by the formula (9), () of the formula (28) is input energy W1 , so “energy transmission coefficient η” is as following η=k

N1 V0 V1 N0 1 − k NV11 NV00

k−

Formula (29) (25) are plotted at the same time in the Fig. 2.

 N0 1 Fig. 2. Relationship of η and D related to variable ( N V1 ) ( V0 )(k = 0.95)

(29)

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5 The Expression of the Operating Period T Output power P0 can be described as following W0 T

P0 =

(30)

Substitute formula (29) into (28), then together with formula (24) into (30) to get η N1 i1 (Ton ) 2 NV1 + k NV0

P0 =

1

(31)

0

Substitute formula (26) into (31), the expression of the operating period T is obtained as following T = 2μ0 (

N1 N0 1 P0 + k )2 A V1 V0 η δ

(32)

6 The Representation of the Maximum Magnetic Density B According to full current law to get N1 i1 (Ton ) =

B δ μ0

(33)

Substitute formula (33) into formula (31), the representation of the maximum magnetic density is known as following B = 2μ0 (

N1 N0 1 P0 +k ) V1 V0 η δ

(34)

7 The Representation of the Ratio of Duty Time D D of formula (25) is expressed similarly as following D=

N1 V1 N1 V1

+ k NV00

(35)

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8 Voltage Waves of Main Switches K1 and K2 The two input lines of a double-channel oscilloscope are common ground. The point S1 in the Fig. 1(b) is defined as common ground point. One input terminal of oscilloscope is connected to point d1 , the other input terminal of oscilloscope is connected to point S2 . The voltage waves Vd1 S1 , VS2 S1 of main switches K1 and K2 are related to leakage resistances of K1 , K2 and two input terminals. 2M

0.475V1

V1 i1(t)

10M

VS2S1

i0(t)

Vd1S1 10M

2M

Fig. 3. Effective circuit of VS2 S1 and Vd1 S1

Fig. 4. Voltage wave circuit of VS2 S1 and Vd1 S1

Leakage resistance of K1 , K2 are shown as 2 M, the input equivalent resistance is 10 M, k = 0.95, NV11 NV00 = 0.5 in the Fig. 3. Analysis as following: substitute the above values into the formula (29) togetη ∼ = 0.814. Vm in the Fig. 1(b) is known as following according to M of formula (4b) and didt0 of formula (20). Vm = M

N1 V0 di0 = −k V1 ∼ = −0.475V1 dt V1 N0

(36)

Solving the circuit in the Fig. 3, to get the voltage values of the period of T2 in the Fig. 4.

9 Conclusions The differences between formula (32) (34) (35) and formulas (9a) (9d) (8) in the literature [11], are that there are η and k items appearing in the formulas (32) (34), and k item

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appearing in the formula (35). Known by the formula (29), when k → 1, then η 1,now they are same each other. The energy transmission coefficient η, defined in this paper, and the relations between η, D and ( NV11 )/( NV00 ) are plotted in the Fig. 2. The figure has great significance in some aspect of designing this kind of converter: it points out that the energy transmission coefficient η is higher when ( NV11 )/( NV00 ) in the range of 0.3–0.6.

References 1. Sun, D.: Newly Established Equation of Flyback Converter and Its Analytical Solution. J. Aerosp. Autom. Control. 3, 58–65 (2002) 2. Sun, D.: Analytical solution of the problem of quasi-resonant flyback converter and its geometrical characteristics. J. Aerosp. Autom. Control. 22(2), 72–80 (2004) 3. Dinghao, S.: Three kinds of self-oscillating converters and their analytical results. Aerosp. Control App. 36(2), 55–62 (2010) 4. Sun, D.: Completely analytical results of a kind of oscillators in pulse width modulators. Aerosp. Control App. 37(5), 26–30 (2011) 5. Sun, D., Zhang, Y., Ye, D.: Newly established operational theory of forward converters. Aerosp. Control App. 37(3), 21–27 (2011) 6. Sun, D., Ye, D., Zhang, Y.: Operating characteristic figure of an LC series resonance magnetic recovery-based forward converter. Aerosp. Control App. 38(5), 51–62 (2012) 7. Sun, D.: A variable frequency type of pulse width modulator constructed of using general pulse width modulator. Aerosp. Control App. 38(1), 40–45 (2012) 8. Sun, D., Ye, D., Zhang, Y.: Operating Characters Figure of Forward Converter with Magnetic Recovery Winding. J. Aerosp. Autom. Control. 33(1), 15–51 (2015) 9. Zhangzhansong, C.: Principle and Design of Switching Power Supply, pp. 344–347 Publishing House of Electronics Industry, Beijing (2000) 10. Geluge: Electricity theory. HaerBin: Northeast Education Press, pp. 275–279 (1952) 11. Sun, D.: Newly established equation of flyback converter and its analytical solution. J. Aerosp. Autom. Control. 1, 48–54 (2001)

Anomaly Interpretation Method Based on Data Mining for Remote Sensing Satellite Payload Jia You(B) and Chengzhi Lu China Academy of Space Technology, No. 104, Youyi Road, Haidian District, Beijing, China [email protected]

Abstract. Because of the rapid development of earth observation technology and artificial intelligence technology, high-resolution optical imaging satellites have emerged continuously, and the era of remote sensing big data has arrived. Due to the limitations of huge image size, sparse target distribution, rich variation of target time domain and insufficient labeling training samples, the current automatic detection of abnormal targets in satellite remote sensing images is inefficient and easy to generate false alarms. This paper proposes a data mining-based joint interpretation method for remote sensing satellite load data, which realizes automatic and fast accurate interpretation of remote sensing image abnormal targets and significantly improves the efficiency of camera data quality assessment. It is the first time to realize a major technical leap from visual interpretation to machine full adaptive interpretation of remote sensing image data, which can provide reference for subsequent adaptive interpretation of remote sensing satellite images. Keywords: Satellite remote sensing images · Data mining · Adaptive real-time interpretation

1 Introduction Due to the strong demand of environmental observation, traffic monitoring, military reconnaissance and other earth observation and perception tasks, aerospace remote sensing technology has been developed rapidly, and many remote sensing satellites have emerged that can provide ultra-high resolution images with metre-level or even submeter-level spatial resolution. The huge amount of high-resolution data contains both valuable and irrelevant information, and it is difficult to make effective evaluation of camera imaging quality, overall transfer function and camera image signal-to-noise ratio by simply relying on manual selection of abnormal target data, so the intelligent and rapid extraction of abnormal targets and related information in images through data mining technology has become a hot spot of current research and has important application value [1]. This paper proposes a data mining-based joint interpretation method for remote sensing satellite load data, which realizes automatic and fast accurate interpretation of remote sensing image anomaly targets and significantly improves the efficiency of camera data quality assessment [2]. It is the first time to realize a major technical leap from © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 814–822, 2023. https://doi.org/10.1007/978-981-19-9968-0_98

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visual interpretation to machine full adaptive interpretation of remote sensing image data, which can provide reference for subsequent adaptive interpretation of remote sensing satellite images [3].

2 Comprehensive Time Series Analysis of Load Data In this paper, we analyze the anomaly laws of the load data in different spectral load channels for the load data in the whole star test and in orbit operation of spacecraft, identify uniform load anomalies, establish a complete library of anomaly patterns, and lay the foundation for anomaly detection [4–6]. In addition, because of the periodic nature of spacecraft imaging missions, the joint feature law of the same scene image data is extracted, and a preliminary model of load data time series based on one-hot coding is established to form a load data time series matrix, which lays the foundation for the subsequent Transformer-based modeling. 2.1 System Hardware Design and Implementation There are many spectral bands of spacecraft payload data, and each spectral band type has different value range, histogram and spectral characteristics of image gray value, which are generally normalized to facilitate subsequent processing [7]. A typical normalized load data is shown in the figure below, where the areas marked with color are anomalous areas (Fig. 1).

Fig. 1. Load characteristic data normalization

For spacecraft, the normalized load data can be divided into digital quantities with discrete variations of 0 and 1 and analog quantities with continuous variations of 0 to 1 (or −1 to +1). Its anomalies can be divided into point anomalies and time series anomalies.

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Fig. 2. Load characteristic data point anomaly

Point anomalies are load characteristic data that exceed the normal threshold, as shown in the figure below, the area circled in the figure has load data that exceeds the lower limit of the normal threshold (Fig. 2). Spacecraft have different load spectrum characteristics, but the anomalies can be summarized as point anomalies and time series anomalies. Therefore, this paper investigates the point anomalies and time series anomalies of spacecraft load data. 2.2 Load Integrated Data Code By adding the load image eigenvalues of a channel at each moment in the last row of the spectral segment matrix, a time series matrix of spectral segments and image eigenvalues can be formed, as shown in the following formula. For a certain load data channel, this paper uses the load data time series matrix as the input and the load data of the subsequent period as the output to train the Transformer model. After the training is completed, the load data time series matrix can be input into the Transformer model, and the Transformer model outputs the load feature values for the next time period.

(1)

3 Research on Anomaly Detection Method Based on Adaptive Threshold 3.1 Load Characteristic Data Creation Firstly, from the remote sensing satellite load channels, S channels of interest are identified, and the load data of different spectral bands of said S channels of interest are encoded in one-hot format respectively, and the load time series matrix Xi =

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  (1) (2) (n) (t) xi , xi , . . . , xi , 1 ≤ i ≤ S with xi ∈ Rm of each channel Pi is obtained, and n is the number of columns of the time series matrix. Denotes the load data eigenvalues of (t) this channel Pi at time t with 1 ≤ t ≤ n, xi includes m − 1 one-hot encoded spectral segment information and 1 image eigenvalue of this channel Pi . ⎧⎡ ⎤ ⎤ ⎡ 0 1 ⎪ ⎪ ⎪ ⎥ ⎢ ⎥ ⎢ ⎪ ⎪ ⎨⎢ 0 ⎥ ⎢ 1 ⎥ ⎢0 ⎥ ⎢0 ⎥ X = ⎢ ⎥, · · · ⎥, ⎢ ⎥ ⎢. ⎥ ⎢. ⎪ ⎪ . . ⎪ ⎣ ⎦ ⎣ ⎦ ⎪ . . ⎪ ⎩ 0.98 1.00

⎡

0 ⎢0 ⎢ ⎢ ,⎢1 ⎢. ⎣ .. 0.50

⎤⎫ ⎪ ⎪ ⎪ ⎥⎪ ⎬ ⎥⎪ ⎥ ⎥ ⎥⎪ ⎪ ⎦⎪ ⎪ ⎪ ⎭

(2)

The above equation represents the load time series matrix t = [1, 2, . . . , n] from moment to moment, each column x(t) represents the spectral information of the moment and the eigenvalues of a load channel at t time, x(t) the first m − 1 row represents m − 1 the one-hot encoded spectral information, and the last row represents the eigenvalues of the load data of the channel at t time.

(3)

3.2 Load Data Prediction and Residual Calculation Taking a load channel data as an example, using theload data time series data X of length ls − 1 as input and the characteristic values Y = y(1) , y(2) , . . . , y(lp ) of subsequent lengths lp as output, the improved Transformer model proposed in this paper is used to accurately construct a time domain model of the load characteristic data of different spectral bands. For the channel Pi , it is assumed that h + 1 predicted load   value of the channel ˆ = yˆ (t−h) , . . . , yˆ (t) ; the has been calculated before the t time, which is recorded as Y   corresponding h + 1 real load value has been recorded Y = y(t−h) , . . . , y(t) at this time. Then the real load data eigenvalues Y can be subtracted from the predicted load ˆ to obtain the load residual vector e. The above process is shown in data eigenvalues Y

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the formulas below. The load residual vector e is calculated as follows.

(4)

  e = e(t−h) , . . . , e(t−ls ) , . . . e(t)

(5)

  e = Y − Yˆ = e(t−h) , . . . , e(t)

(6)

3.3 Load Residual Smoothing The smoothed residual vector is obtained by smoothing the residual vector es by the Exponential weighted moving average method.   (7) es = EWMA(e) = es(t−h) , . . . , es(t) EWMA in the above equation represents the exponentially weighted moving average function, and the specific algorithm is shown in the following equation. α in the above equation is the weighted decay rate. es(0) = e(0)

(8)

es(t) = (1 − α)es(t−1) + αe(t)

(9)

The true load value, predicted load value, predicted residual, and smoothed residual, all of which correspond to time, are obtained as shown below.

(10)

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Assuming the existence of a fixed residual threshold, the existence of anomalies can be determined based on the relationship between the magnitude of the smoothed residuals and the threshold [8]. Taking a load image feature data as an example, the above process is shown in the figure below (Fig. 3).

Fig. 3. Anomaly detection based on fixed thresholds

3.4 Adaptive Threshold Design However, for spacecraft loads, fixed thresholds are difficult to determine; if the threshold is too large, a large number of anomalies will not be detected, and if the threshold is too low, false alarms will occur from time to time. In this paper, we design an adaptive threshold method to improve the accuracy rate. At moment t, the set ε of adaptive thresholds is computed for the smoothed residual vector es . z in the above equation is a set of predefined values.The final adaptive thåreshold ε is obtained by solving the following equation in the set of threshold values ε. μ(es ) denotes the mean of es ; μ(es ) denotes the difference between the mean of es and the mean of normal values in es that are not greater than the threshold ε; σ(es ) denotes the standard deviation of es ; σ(es ) denotes the standard deviation of es and the difference between the standard deviation of normal values in es that are not greater than the threshold ε; ea denotes the set of values in es that are greater than the threshold ε; n(ea ) denotes  the number of elements in ea ; Eseq denotes the set of all abnormal  sequences; n Eseq denotes the number of elements in Eseq . ε = μ(es ) + zσ (es ) ε = arg max(ε) =

μ(es )/μ(es ) + σ (es )/σ (es )   n(ea ) + n2 Eseq

(11) (12)

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μ(es ) = μ(es ) − μ{es ∈ es |es ≤ ε}

(13)

σ (es ) = σ (es ) − σ {es ∈ es |es ≤ ε}

(14)

ea = {es ∈ es |es > ε}

(15)

Eseq = continuous sequences of ea ∈ ea

(16)

4 Image Anomaly Detection Test Results When the image data is the real test data of the camera, the image will change because of the integration time gain, etc. Even if the imaging conditions remain unchanged, there will be small changes in the sensor sampling of the camera CCD, that is, the camera image appears anomaly. Image feature values will show significant differences, the following chart is the actual test camera data (Figs. 4 and 5).

Fig. 4. Remote sensing camera anomaly camera image

Fig. 5. Camera abnormal image detection results

According to the anomaly detection method of adaptive threshold mentioned above, the real remote sensing satellite anomaly camera image is received and processed by

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the fast vision system to output the anomaly detection result extraction as shown in the figure below. The actual test results show that the image anomaly detection rate of the system is as high as 95%. At present, the system has been successfully used in the wholesatellite test of high-definition multi-mode satellite cameras, which greatly improves the efficiency of camera data interpretation and also saves the cost of test manpower (Fig. 6).

Fig. 6. Read out the display positioning results of abnormal flower blocks

5 Conclusion By facing the actual situation of whole-star testing and satellite operation in orbit, this paper proposes an adaptive anomaly detection method of spacecraft remote sensing data integrated time series based on Transformer model for the outstanding problems of poor real-time [9], low accuracy and low implementation efficiency of remote sensing satellite payload data. Through the processing of image data time series coding and Transformer model input part, it can effectively improve the effectiveness of comprehensive time series modeling of massive image data and enhance the data embedding capability. In addition, for the problem of high false alarm rate in anomaly target detection, adaptive thresholding and anomaly clipping methods are designed to improve the accuracy of anomaly detection [10]. The real test data of the camera combined with the adaptive interpretation method is used for the detection of anomalous blobs, and the effectiveness of the proposed method in this paper is demonstrated by the experimental results. The research results of this paper can provide important technical support for real-time autonomous anomaly detection of remote sensing image data.

References 1. Fernandez, M., Yue, Y., Weber, R.: Telemetry anomaly detection system using machine learning to streamline mission operations. In: International Conference on Space Mission Challenges for Information Technology, pp. 70–75 (2017) 2. Hundman, K., Laport, C.: Detecting spacecraft anomalies using nonparametric dynamic thresholding. In: International Conference on Knowledge Discovery and Data Mining, pp. 1–9 (2018) 3. Yairi, T., Oda, T., Nakajma, Y.: Evaluation testing of learning-based telemetry monitoring and anomaly detection system in SDS-4 operation. In: The International Symposium on Artificial Intelligence, Robotics and Automation in Space, pp. 1–10 (2014) 4. Pang, G., Shen, C., Cao, L.: Deep learning for anomaly detection. In: International Conference on Space Mission Challenges (2020). 2007.02500 5. Akoglu, L.: Anomaly mining–past, present and future. Trans. Internet Technol. (2021). 2105.10077

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6. Ma, Y., Wu, H., Wang, L.: Remote sensing big data computing: challenges and opportunities. Future Gener. Comput. Syst. 51, 47–60 (2015) 7. Tang, P.: Practice and thoughts of the automatic processing of multispectral images with 30 m spatial resolution on the global scale. J. Remote Sens. 18(2), 231–253 (2014) 8. Ma, J., Le, S., Hua, W.: Supervised anomaly detection in uncertain pseudoperiodic data streams. Trans. Internet Technol. 16(1), 1–20 (2016) 9. Sha, W., Zhu, Y., Chen, M.: Statistical learning for anomaly detection in cloud server systems: a multi-order Markov chain framework. IEEE Trans. Cloud Comput. 6(2), 401–413 (2018) 10. Silva, N., Soares, J., Shah, V., Santos, M.Y., Rodrigues, H.: Anomaly detection in roads with a data mining approach. Procedia Comput. Sci. 121(2), 415–422 (2017)

A Study on Determination Method of Safety Band in the Translational Closing Section of Rendezvous and Docking Honghui He1(B) and Changqing Chen2 1 China Academy of Space Technology, Beijing, China

[email protected] 2 Beijing Institute of Control Engineering, Beijing, China

Abstract. This paper studies the determination method of safety band in the translational closing section of rendezvous and docking. The safety band is designed for the 6-DOF linear approximation translation-close section, including no-control band, correction band, warning band and emergency evacuation band. The boundary parameters of safety band are designed according to the measurement characteristics of navigation sensor, the coupling characteristics of attitude and position, and the control characteristics of translation and drawing section. Based on the safety band, six degrees of freedom control scheme is designed, and the collision avoidance strategy is designed. The simulation results verify the effectiveness and accuracy of the design of control and collision avoidance strategies. Relevant research results have been applied to the flight control mission of Tianzhou series cargo spacecraft, which has been proved remarkable. Keywords: Rendezvous and docking · Translational converging section · The safety band · The six degree of freedom control

1 Introduction In the process of rendezvous and docking, and in the trend of increasing requirements for autonomous rendezvous, trajectory safety has become an important research topic. At present, studies on the safety of rendezvous and docking trajectory mainly fall into the following categories: The first category is to give the trajectory safety conditions for special types of trajectory [1–3]; The second is to obtain safe trajectories by online numerical calculation [4–6]. The third category offers sufficient conditions or necessary and sufficient conditions [7, 8] of the trajectory safety based on some analysis on the typical working conditions, and serves as the basis for the judgment on the trajectory safety and the active safety control. The fourth category serves other purposes, such as solving the collision probability [9, 10]. In the translational closing section of rendezvous and docking, the six degrees of freedom control principle in the continuous control mode is adopted for rendezvous and docking control because the distance between the aircrafts is relatively close. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 823–828, 2023. https://doi.org/10.1007/978-981-19-9968-0_99

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existing safety band determination method based on the guidance pulse principle cannot be applied to the translational moving closing section, so the design of safety band in the translational closing section needs to be discussed.

2 The Description of Dynamics Equation and Problems This paper aims to research the trajectory safety issue in the process of close rendezvous and docking. The relative motion can be described by the CW equation. The CW equation [9] for free drift in the orbital plane can be obtained as follows: ⎧ x¨ + 2ω˙z = 0 ⎪ ⎨ y¨ + ω2 y = 0 (1) ⎪ ⎩ 2 z¨ − 2ω˙x − 3ω z = 0 where x, y, z are three relative positions of trace direction, normal direction and radial direction respectively, and ω is the orbital angular velocity of the reference orbit. As shown in Fig. 1, above or below the nominal track and starting from the nominal track, the no-control band, correction band, warning band and emergency evacuation band are respectively set from inside to outside.

Fig. 1. Schematic diagram of rendezvous and docking safety band

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3 Design of Control Method and Collision Avoidance Strategy Based on Safety Band 3.1 Design of Nominal Trajectory of Translational Converging Section The relative positions are described by CW equation, and the nominal trajectories of the three axes are as follows: ⎧ ⎪ ⎨ x = xR + x˙ R t y=0 (2) ⎪ ⎩ z=0 where xR is the initial position of the track direction, x˙R is the nominal speed, and t is the time of flight. The above formula shows that the tracking spacecraft approaches the target spacecraft in a straight line along the track. The relative attitude φ, θ, ψ represents rolling, pitching and yawing directions respectively, and its nominal trajectory is as follows: ⎧ ⎪ ⎨φ = 0 θ =0 (3) ⎪ ⎩ ψ =0 The above equation shows that in the process of the tracking spacecraft approaching the target spacecraft in a straight line, the attitude of the two spacecraft is consistent, that is, the relative attitude is zero. 3.2 Design of Control Law and Collision Avoidance Algorithm The phase plane control strategy is adopted, and the phase plane control algorithm is shown in Fig. 2. The selection of control parameters θD > Ri1 is guaranteed in the absence of control band, no control; The selection of control parameters θD < θB < Ri2 ensures that under normal circumstances, as long as the trajectory exceeds θB , it will exert full control to make it less than θB . In case of failure or other unconsidered factors, the trajectory exceeds θB but cannot be controlled so that the trajectory is less than θB , the warning is issued. If the trajectory exceeds the docking corridor, the emergency evacuation condition is triggered. The large pulse opposite to the flight direction is executed to make the two spacecraft gradually move away from each other, and the evacuation pulse is selected as −1 m/s–2 m/s. A typical example is Vx = −2 m/s.

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Fig. 2. Schematic diagram of phase plane control algorithm

4 Simulation Analysis 4.1 Control Effect The initial relative position of the two spacecraft is [150,0,0]m, the initial relative attitude, and the control law and collision avoidance operation designed by the six-DOF control band are adopted. The normal flight trajectory is shown in Fig. 3 and Fig. 4, in which Fig. 3 shows the variation trend of three relative positions, and Fig. 4 shows the variation trend of three relative positions.

Fig. 3. Relative attitude change trend

Fig. 4. Relative position change trend

4.2 Analysis of Collision Avoidance Examples In the process of close linear approach, the relative attitude out-of-tolerance enters the emergency evacuation safety band and triggers the emergency evacuation instruction, as shown in Figs. 5 and 6. The emergency evacuation pulse is applied to change the direction of the approach trajectory and ensure the trajectory is safe.

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Fig. 5. Emergency evacuation track

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Fig. 6. Emergency evacuation track

4.3 Simulation of Collision Avoidance Calculation Examples The distance between the two spacecraft in the X-axis direction is 30 m, triggering the emergency evacuation condition. The evacuation track is shown in Figs. 7 and 8. It can be seen that the evacuation track comes out of the no-fly zone to ensure that the two spacecraft do not collide.

Fig. 7. Emergency evacuation trajectory

Fig. 8. Emergency evacuation trajectory

5 Conclusion In this paper, a concept of track safety band is proposed for the translational section of rendezvous and docking. No control band, correction band, warning band and emergency evacuation band are designed near the nominal track of typical translation converging section to realize fault detection, real-time control and early warning. Aiming at the attitude position coupling problem, the flight deviation information corresponding to the relative position was obtained according to the measurement error parameters corresponding to the relative position. According to the nominal trajectory equation of the relative position of the translation-closing segment, the measurement error parameters corresponding to the relative attitude and the relative position, and the flight deviation information corresponding to the relative position, the boundary parameters of the 6-DOF corresponding to the relative attitude and the relative position, namely, the no-control zone, correction zone, warning zone and emergency evacuation zone, were designed.

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The control law and collision avoidance algorithm are designed on the basis of safety band, and the corresponding control law and safety collision avoidance strategy are designed for no control band, correction band, warning band and emergency evacuation band. The simulation results show that the designed safety band determination method can monitor the safety condition of the mission in real time, evacuate the mission in real time in case of failure, and give the mission recovery strategy after evacuation, ensuring the safety of the rendezvous and docking mission from the design, monitoring, active protection and mission recovery. The proposed method has been applied to the rendezvous and docking scheme design and flight control tasks of Tianzhou series cargo spacecraft and has broad application value.

References 1. Yamanaka, K., Yokota, K., Yamada, K., et al.: Guidance and navigation system design of R-bar approach for rendezvous and docking. In: AIAA Paper pp. 98–1299 (1998) 2. Chen, C., Xie, Y.: Study on trajectory safety of v-BAR negative monopulse evacuation for rendezvous and docking. Acta Astronaut. Sin. 29(3), 5 (2008) 3. Xie, Y., Chen, C.: Research on design method of safe exit track for a class of no-fly zone Space Control Technol. App. 35(3) (2009) 4. Richards, A., Schouwenaars, T., How, J.P., Feron, E.: Spacecraft trajectory planning with avoidance constraints using mixed-integer linear programming. J. Guid. Control Dyn. 25(4), 755–764 (2002) 5. Breger, L., How, J.P.: Safe trajectories for autonomous rendezvous of spacecraft. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Keystone, Colorado, 21–24 August 2006 6. Luo, Y.Z., Lei, Y.J., Tang, G.J.: Optimal multi-objective nonlinear impulsive rendezvous. J. Guid. Control. Dyn. 30(4), 994–1002 (2007) 7. Fehse, W.: Automated Rendezvous and Docking of Spacecraft. Cambridge University Press, Cambridge (2003) 8. Xie, Y., Chen, C., Liu, T., Wang, M.: Principles and Methods of Spacecraft Rendezvous and Docking Guidance and Navigation Control. National Defense Industry Press (2018) 9. Chen, C., Xie, Y.: Research on the safety judgment of rendezvous and docking free drift trajectory. Space Control Technol. Appl. 37(6), 12 (2011) 10. Chen, C., Xie, Y., Liang, H.: A method for determining seat belts of rendezvous and docking trajectories. Space Control Technol. Appl. 43(3) (2017)

Prospect Analysis of Satellite Application of Long-Distance Wireless Energy Transmission System Sen Wang(B) , Xiaofei Li, Xiaochen Zhang, Yudan Liu, and Bingxin Zhao China Academy of Space, Beijing 100094, China [email protected]

Abstract. With the rapid development of aerospace technology and the continuous improvement of load equipment index, higher requirements are put forward for electricity demand, and the cable laying outside the cabin is increasing, but the problem of cable protection has not been solved. Satellite in-orbit work often meets the shadow period, and the task of load in the shadow period will be restricted, and the working performance will be limited in emergency. In-orbit satellites will encounter the situation that the solar wings can’t be deployed and the whole satellite has no energy. If there is no other energy source, the satellite will lose its working ability permanently. Therefore, a new type of power supply mode is needed to provide solutions for three failure modes-wireless energy transmission. Keywords: Cable protection · Restricts satellite · Wireless energy transmission

1 Summary With the rapid development of aerospace science and technology, the power level of satellites will continue to increase, the load volume will increase, the load performance will continue to improve, and the configuration of satellites will continue to be complex [1]… Based on the above factors, the satellite’s energy supply scheme puts forward higher requirements, and the traditional scheme of transmitting energy to electrical equipment through cables is far from meeting the requirements, or poses a huge threat to the reliability and security of satellites. There is bound to be a better energy supply scheme to replace it-wireless energy transmission [2] (WPT). At present, the existing wireless energy transmission is aimed at the energy transmission of millimeter distance [3], which can only solve the energy transmission between adjacent devices, but can’t meet the energy supply scheme of meter in the future. For the future SAR payload [4], its two-dimensional area will increase to tens of square meters or even hundreds of square meters, and the length of power supply cable will increase from several meters to tens of meters, which will lead to the continuous increase of voltage drop on the cable, serious power loss, and a large number of cables exposed to space, which will increase the probability of being attacked by space debris, seriously reducing the reliability of the payload. For future high-precision loads (cameras, radiometers, etc.), their sensitivity to electrical signals is very high. In order to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 829–837, 2023. https://doi.org/10.1007/978-981-19-9968-0_100

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achieve higher and more accurate measurement results, it is often necessary to be physically isolated from the service cabin, and traditional cables cannot meet the requirements. For high-orbit satellites with strict weight requirements, if the cable is replaced with wireless energy transmission, at least one hundred kilograms of weight can be saved, which will be a very effective way to reduce weight. Moreover, the weight of the cable no longer increases with the complexity of satellite functions, which can increase more payload on the satellite. There are two main types of wireless energy transmission modes, namely, far-field wireless energy transmission and near-field wireless energy transmission [5]. Far-field wireless energy transmission can be divided into microwave and laser. This paper mainly studies the wireless energy transmission modes suitable for future satellites, and forms an energy supply scheme for future satellites.

2 Wireless Energy Transmission System This chapter investigates the development of wireless energy transmission systems [6] at home and abroad, summarizes their advantages and disadvantages, and analyzes the scenarios applicable to future space applications. 2.1 Wireless Laser Energy Transmission Laser power transmission (LPT) transmits power at visible or near infrared frequencies. It uses highly concentrated laser aiming at the energy receiver to realize long-distance and efficient power supply. The laser-powered receiver uses a special photovoltaic cell to convert the received laser into electric energy [7]. LPT has the advantage of energy concentration, so the beam pointing controller (also called universal mirror) is often needed in engineering applications to realize the real-time tracking [8] of photovoltaic cells and always maintain high energy conversion efficiency. At present, the best form of laser technology is fiber laser, which can generate hundreds of kilowatts of peak power and has a good application prospect [9]. Figure 1 shows the block diagram of wireless power transmission system based on laser. Sun Zhiyu [10], Nanjing University of Science, uses GaAs solar cells as the receiver of laser wireless energy transmission, and conducts wireless energy transmission experiments. By testing the photoelectric conversion efficiency under different laser power densities, the results show that the energy conversion efficiency reaches the maximum value of 46.6% when the density is 54.9 mW/cm2 , and wireless energy and signals are simultaneously transmitted, all of which achieve good results. Sheng Q et al. designed a cavity laser with cat’s eye retroreflector and verified its feasibility through experiments. It was found that spherical aberration had a significant impact on the output power of the system, and finally the transmission efficiency of 37% was achieved at a transmission distance of 5 m. Ericsson has established a 5G radio unit system based on laser wireless energy transmission [12], which is called the first wireless power supply base station in the world. Its main purpose is to release the laser within milliseconds in case of temporary power failure, so as to ensure that the base station does not lose power. Japan Aerospace Exploration Agency (JAXA) is engaged in research to determine the feasibility of using

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Fig. 1. Laser wireless power transmission system

laser and microwave transmitters to transmit solar energy from LEO and GEO satellite systems. When dealing with space-based laser WPT system, it is very important to find the alignment between the system and the ground receiver. In order to realize this, JAXA conceived a beam control method, which used the guided laser on the ground to drive the mirror quickly, and finally reached the accuracy of 0.1 rad (Fig. 2).

Fig. 2. Solar space power laser system

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2.2 Wireless Transmission of Microwave Energy Microwave wireless energy transmission is based on electromagnetic radiation, which uses the far-field radiation effect of electromagnetic field to transmit power in free space [13]. Therefore, microwave wireless energy transmission is less affected by environmental factors such as atmosphere; The microwave beam has great divergence in the process of space propagation, so it is necessary to increase the aperture of the antenna to meet its high gain and form a narrow beam to ensure that the energy can be transmitted in a small space. When deployed with geosynchronous transmitting and receiving satellites, this technology can enhance the power of objects obtained from base stations [14]. The block diagram of microwave energy transmission is shown in Fig. 3.

Fig. 3. Block diagram of wireless energy transmission based on microwave

Research on microwave wireless energy transmission has been carried out abroad for a long time, and some achievements have been made [15]. As early as 1968, the United States put forward the space solar power station (SPS) with wireless energy transmission. In 2012, the United States, Japan and the United Kingdom jointly put forward the arbitrary phased array space solar power station. This new type of power station uses the idea of modularization, reduces the transportation difficulty, and innovatively puts forward the concept of uncontrolled concentrating system, which makes it possible to apply the transmitting antenna of future space solar power stations (Fig. 4). In 2015, Japan realized the microwave wireless energy transmission test twice [16], accurately transmitted it to the receiving device 55 m away with 18,000 W transmission power, and successfully started the electric kettle; Successfully lit the LED lamp 500 m away with 10 kW power. Japan has seen a great prospect in wireless energy transmission, and has focused its attention on solar power stations. It is planned to complete the verification of aerospace wireless energy transmission in 2045 (Fig. 5). In recent years, China has made a series of achievements in the field of wireless energy transmission. In 2014, Xi’an Branch set up a demonstration and verification system for wireless energy transmission [17], which adopted a 2.45 GHz carrier frequency and a 2.4-m-diameter transmitting antenna, and achieved a high transmission efficiency of more than 16% over a transmission distance of 12 m (Fig. 6).

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Fig. 4. Arbitrary phased array space solar power station

Literature [18] By designing an S-band circularly polarized rectifier antenna, this antenna can effectively suppress the energy loss caused by polarization mismatch. The experimental results show that the conversion efficiency reaches 71.6% when the input power of the antenna is 16.3 dBm, which is 22.7% higher than that of the circularly polarized antenna in reference [19]. It is of great significance to the application of microwave wireless energy transmission system (Fig. 7). 2.3 Quasistatic Cavity Resonance Wireless Energy Transmission Quasistatic cavity resonance (QSCR) wireless energy transmission is an iterative optimization based on magnetic resonance coupling, which aims to solve the disadvantage that magnetic resonance coupling can’t be efficiently transmitted in three-dimensional space. Quasi-cavity resonance (QSCR) uses a room-scale cavity with a central pole and an inserted lumped capacitor [20, 21]. The current widely distributed in the structure produces 3D magnetic field distribution in the room. Only relying on a single current and magnetic field distribution will bring the characteristics of low transmission efficiency and uneven distribution. Therefore, adopting two or more magnetic field modes will increase the energy transmission efficiency and have a broader application prospect. This energy transmission mode is also called multi-mode Quasistatic cavity resonance (M-QSCR). In 2021, Tokyo University of Japan completed a room-level (3 m × 3 m × 2 m) wireless energy transmission system. This system combines two magnetic field modes, namely, pole-dependent mode (PI) and pole-independent mode (PD), to ensure that there are strong magnetic fields around the space and at the center, and the energy transmission efficiency exceeds 50% in 98% of the room volume. A large number of experiments have proved that this combined mode is far superior to the transmission efficiency of using only one magnetic field. The transmission mode is shown in Fig. 8.

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Fig. 5. Microwave wireless energy transmission test

2.4 Summary Through the investigation of three kinds of wireless energy transmission systems, it can be found that the whole system frame of the laser wireless energy transmission system is relatively small in size and good in operability. GaAs photovoltaic cells can be used at the laser receiving end, which is relatively friendly to the satellites that use photovoltaic cells to generate electricity, but its transmission efficiency is easily affected by environmental factors such as the atmosphere. Due to the strong divergence of microwave signal transmission in wireless energy transmission system, the aperture of transceiver antenna is large, which increases the difficulty of system construction. Quasistatic cavity resonance wireless energy transmission system is suitable for roomlevel space, and its transmission efficiency is higher than the former two, so it is more suitable for wireless energy transmission in a fixed space with special requirements for transmission cables.

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Fig. 6. 12-m microwave wireless energy transmission test

Microwave source Direconal coupler

Ohm box

Dynamometer

trumpet Power amplifier circulator

Recfying antenna

end Fig. 7. (a) Transmitting end (b) (receiving end)

Fig. 8. Multi-mode Quasistatic cavity resonance transmission system

3 Inspiration Because the receiving end of the laser wireless energy transmission system is photovoltaic cell, and the main energy source of spacecraft is photovoltaic cell, which can

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directly receive laser energy transmission. However, the existing satellite working mode is seriously affected by illumination. If the laser wireless transmission system is used on the ground to transmit energy to the satellite, the satellite can continue to output electric energy only by turning the direction of the windsurfing during the shadow period, providing energy for the heavy load, greatly enhancing the response capacity of the load, and no longer being limited by the illumination conditions. Moreover, the weight of the battery can be reduced by this mode, thus reducing the limit to the load. The microwave wireless energy transmission system has a wide diffusion range around, and the receiving end can receive the energy transmission without accurate alignment. When the solar wing of the satellite can’t be unfolded normally or the battery energy is exhausted and the energy supply is insufficient, the microwave wireless energy transmission system can provide emergency rescue for the satellite, which can save a satellite and save huge economic losses in extreme cases. With the continuous increase of satellite load volume, the continuous improvement of load performance, and the increasing number of exposed cables outside the cabin, it is difficult to solve the safety protection of cables. Therefore, if the satellite can be equipped with a room-level Quasistatic cavity resonance wireless energy transmission system, and the outer contour of the satellite can be designed as the outer wall of the room, the equipment in the whole room can get energy through the wireless energy transmission system, and the distribution characteristics of magnetic field inside the satellite can be changed by adjusting the form of electromagnetic field, so as to improve the energy transmission efficiency of key equipment, thus solving the problem of cable damage outside the cabin. Imagine that three kinds of wireless energy transmission systems are applied to the aerospace field, so that the satellite’s on-orbit maneuverability, on-orbit fault tolerance and on-orbit mission robustness will be greatly improved, and the development of the aerospace industry will surely reach a higher level and achieve another milestone goal.

References 1. Chen, Z.: Consideration on the development of intelligent networked multifunctional satellite system technology in the future. Aerospace Shanghai 38(3) (2021) 2. Strassner, B., Chang, K.: Microwave power transmission: historical milestones and system components. Proc. IEEE 101, 1379–1396 (2013) 3. Li, L., Zhang, P., et al.: Key technologies of microwave wireless power transfer and energy harvesting based on electromagnetic metamaterials. J. Aerosp. 50(10) (2021) 4. Yang, Q., Li, X., et al.: Main bearing structure design of satellite with external large SAR payload. Spacecraft Eng. 6(26) (2017) 5. Okoyeigbo, O., Olajube, A.A., et al.: Wireless power transfer: a review. Earth Environ. Sci. 6(655) (2020) 6. Sidiku, M.B., Eronu, E.M., et al.: A review on wireless power transfer: concepts, implementations, challenges, and mitigation schemes. Niger. J. Technol. 39(4), 1206–1215 (2020) 7. Arai, K.: Wireless power transfer solution for ‘things’ in the Internent of Things. In: Proceedings of the Future Technologies Conference (FTC), vol. 1 (2018) 8. Guo, L., Zhao, C., et al.: Research on closed-loop control of high-efficiency laser wireless power transmission system. Laser Technol. 45(5), 576–584 (2021)

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9. Badr, B.M., Makosinski, A., et al.: Controlling wireless power transfer by tuning and detuning resonance of telemetric devices for rodents. Wirel. Power Transfer 7(1), 13–32 (2020) 10. Zhiyu, S., Jian, L., et al.: Performance test of solar cell under laser energy transmission and signal transmission. Infrared Laser Eng. 51(2), 20210888-1 (2022) 11. Sheng, Q., Wang, M., et al.: Continuous-wave long distributed-cavity laser using cat eye retroreflectors. Opt. Express 29(21), 34269–34277 (2021) 12. Anonymous. Could laser-based wireless power transmission be taking center stage. IET Power Electron. 10(13) (2020) 13. Jawad, A.M., Nordin, R., Gharghan, S.K.: Opportunities and challenges for near-field wireless power transfer: a review. MDPI J. 10(7), 1–28 (2017) 14. Krikidis, I.: From wireless power transfer to wireless powered communications. In: International Conference, p. 114 (2017) 15. Zhou, Y.C.: Summary on space microwave wireless energy transfer technology of Japan. Space Electron. Technol. 5, 52–59 (2017) 16. Sasaki, S., Tanaka, K., Maki, K.: Updated technology road map for solar energy from space. IAC-11-C3.1.4, ISAS/JAXA (2011) 17. Ma, H., Yang, Y., et al.: Research progress of wireless power transmission technique for space network, pp. 2–88 (2018) 18. Li, X., Xu, L., et al.: Design of an S-Band circular polarized rectenna applied to microwave wireless power transmission. Appl. Sci. Technol. 1(48) (2021) 19. Guo, C., Zhang, W.: A wideband CP dipole rectenna for RF energy harvesting. In: 2019 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), Taiyuan, China, pp. 1–3 (2019) 20. Chabalko, M.J., Shahmohammadi, M., Sample, A.P.: Quasistatic cavity resonance for ubiquitous wireless power transfer. PLoS ONE 12, 1–14 (2017) 21. Sasatani, T., Yang, C.J., Chabalko, M.J., Kawahara, Y., Sample, A.P.: Room-wide wireless charging and load-modulation communication via quasistatic cavity resonance. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2, 1–23 (2018). Article no: 188 22. Sasatani, T., Sample, A.P., et al.: Room-scale magnetoquasistatic wireless power transfer using a cavity-based multimode resonator. Nat. Electron. 4(9), 689–697 (2021)

Design of Multi-dimensional Control and Multi-subsystem Cooperative Operation Algorithm for Control Subsystem Hong Guan(B) , Jian Cai, HeLong Liu, Yong Li, and Zefeng Ma Beijing Institute of Control Engineering, Beijing 100190, China [email protected]

Abstract. Aiming at the problem of multi-dimensional control and multisubsystem cooperative control on board, this paper proposes an operation method. This method can meet the requirements of multi-dimensional tasks such as ground verification, normal attitude/orbit and panel control in orbit, on-board autonomous processing after failure. Through ground simulation and on-orbit application, it was shown that this method has the value of on-orbit application and popularization. Keywords: Control subsystem · Actuators · Ground verification · Fault handling

1 Introduction Gaofenduomo satellite is the first satellite of a common remote sensing platform. It has been in orbit for more than two years. The function of autonomous mission planning has been realized based on its maneuverability and on-board computing capability. With the gradual upgrading of the high-precision control subsystem, the requirements for the autonomous control ability are also increased [1–3]. The actuators include the control moment gyro (CMG) and magnetorquer. At the same time, the propulsion subsystem and the energy subsystem are coordinated. The control subsystem of Gaofenduomo satellite has the following capabilities: Stage1, The ground test stage. At this stage, the computer of control subsystem (GNCC) controls the target angle or angular velocity of CMGs gimbals according to the ground injection command, the same as of the energy subsystem. Then it performs target injection time and non-associated on-off valve operation of the propulsion subsystem, while controls the pulse width output of the magnetorquer. Stage2, On board stage. GNCC maintains attitude control, orbit control and panel control [4]. Stage3, Failure mode. Without ground intervention, GNCC sends no action instructions to the failure actuators. If ground intervention is required, the autonomous control of the failure actuators can be cancelled and by injection instruction. The recovery operations [5] are then carried out by manual means. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 838–843, 2023. https://doi.org/10.1007/978-981-19-9968-0_101

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Aiming at the above problems, this paper proposes an operation method of multidimensional control and multi-subsystem cooperative control. This method has been proved through ground simulation and on-orbit application.

2 Autonomous Control Method The spacecraft operates on board after the separation with the rocket. GNCC outputs control instructions to the actuator. The control force or control torque is output by the actuator as required. The research object of this paper is typical working conditions of Gaofenduomo satellite’s control subsystem. When the actuator works normally and introduced in the system, the following formula is satisfied I ω˙ + ω× Iω = T c + T

(1)

In this function, I is the moment of inertia of the principal axis. ω is the attitude angular velocity of the spacecraft. Tc represents the control torque of the actuator on the spacecraft. While T represents other. The attitude control algorithm is designed according to the system dynamics equations, T c = −k1 qe − k2 ωe − T D

(2)

In this function, k 1 and k 2 are positive constants. Td present the external interference torques, which are measurable. qe is the quaternion of systematic error. While ωe is the systematic error angular velocity. After the expected control torque of the system is determined, GNCC will distribute the output of the control torque according to the working capacity of each actuators. 2.1 A Subsection Sampl Maneuver Attitude Control Method with Single-Gimbal Control Moment Gyros CMGs is the main actuator of Gaofenduomo satellite during high precision and high stability attitude maneuver. The equation of Gimbal angular velocity command is δ˙ d = −

1

· JacTI · TCMGc + kCMGs1 (ICMGs − JacTI )

HCMG0 +K0 (ICMGs − JacTI )δ CMG

∂JD ∂δ CMG

(3)

In this function, δd represents the Gimble angular velocity change of CMGs. TCMGc is the output torque allocated to the CMGs. kCMGs1 and K0 are the correction constants. δCMG include the nominal positions. JD is the singular avoidance vector. The expected change of gimbal angular velocity was calculated, and then sent to CMGs. The selection flag is canceled when a CMG failed. The control commands are no longer sent to the faulty CMG, instead with an invalid instruction. Until disposal required, the ground system will inject control instructions to the corresponding CMG.

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2.2 Control Scheme of Magnetorquer To eliminate the accumulation of angular momentum due to changes in orbital period, there are three orthogonal magnetorquers equipped on the Gaofenduomo satellite. The calculation equation of unloading instruction is Mx = kPMU · Vmx =

Hby Bbz − Hbz Bby |Bb |2

Mx · Vmmlf kmm · Mx max

(4) (5)

In this function, Hby andHbz are the angular moments to unloaded. Corresponding to the angular moments, GNCC could calculate the magnetic torque values, which is represent as Mx . Bb represents the magnetic vector in three-axis direction of this satellite. Mxmax , kmm and Vmmlf are the amplitude limiting constants. The control voltage value can be obtained from MX . The selection flag can also be removed when a magnetorquer failed. The control voltage value is no longer sent to the faulty magnetorquer, instead with the zero voltage value instruction. The ground system is required to set ‘ground injection instruction allow’ first to get acquisitions. 2.3 Control Scheme of Propulsion Subsystem As backup of CMGs, Phase plane control method is used for propulsion subsystem. According to the error between the current attitude angle, attitude angular velocity and the expected value, the phase plane design curve is as in Fig. 1

Fig. 1. The phase plane design curve

In the above figure, θd and θR are input parameters of phase plane control. θ is the attitude angle error of actual attitude angle. GNCC can set the selection flag of propulsion subsystem invalid, when it is diagnosed as faulty. Ground system needs to set ‘onboard autonomous control prohibition’ before the test the thrusts. According to the injection setting value and the communication protocol, GNCC sends command packages to control pulse width and switch valves.

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2.4 Solar Panels Control Scheme Combined with Energy Subsystem Gaofenduomo satellite energy system consists of two rigid solar panels. Angles of solar panels are driven by control subsystem. The phase plane control algorithm is used as well. GNCC calculates the angular velocity according to the angle errors. When energy subsystem fails, GNCC operates similarly to the propulsion subsystem.

3 Design of Multi-dimensional Control and Multi-subsystem Cooperative Operation Algorithm The multi-dimensional control and multi-subsystem collaborative operation scheme proposed in this paper includes control, energy and propulsion subsystems, and Covers the following dimensions. When energy subsystem fails, GNCC operates similarly to the propulsion subsystem (Fig. 2). Different Stages

Within Subsystem CMGs GNCC autonomous control or direct forwarding

injecon CMG test instrucon

Within Subsystem magnetorquer Injecon Instrucon Allows

Allows

Inject control voltage

Inhibit GNCC autonomous control Other Subsystems˖ Propulsion and Energy

Prohibion of autonomous control Allows

Inhibit

Inject acon expectaons

GNCC autonomous control

Fig. 2. Flow chart of Multi-dimensional control, multi-subsystem collaborative operation scheme

3.1 Dimension 1 - Verification Test The current mode is not onboard. GNCC does not send control instructions to the above actuators. The CMGs component test task completes by injecting control instructions. The initial normal state of other executing agencies is to prohibit sending instructions. It is necessary to set the state of ’ on-board autonomous control prohibition ’ first to operate these three actuators. GNCC then opens the ground injection interface and Generate effective control instructions. 3.2 Dimension 2 - Onboard GNCC independently sends control instructions per cycle according to control requirements onboard.

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3.3 Dimension 3 - Failure of Actuator Occurs If there is no ground handling, GNCC will protect the faulty motor through autonomous diagnostic logic. If handling is required, ground system should inject instructions to remove protective measures from GNCC and make it release ground control functions. 3.4 Application The control method proposed in this paper can meet the control requirements within and between subsystems under different dimensions. Under the premise of ensuring that the normal actuator operation is not affected, the effective handling operation ability of the fault actuators is improved (Figs. 3 and 4).

Fig. 3. Curve of three-axis attitude angle velocity for attitude maneuver

Fig. 4. CMGs gimbal angular velocity curve

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4 Conclusion The operation scheme of Gaofenduomo satellite has verified onboard. It can identify the use requirements of different dimensions. It also can adapt to different protocol requirements within and between subsystems. This method improves the testability, onboard reliability and maintainability of the actuators. Compared with the existing autonomous scheme, the scheme proposed in this paper has more advantages. It not only considers typical working conditions onboard, but also realizes multi-dimension. This method is more suitable for ground verification and onboard operations.

References 1. Yuan, L., Lei, Y., Yao, N., et al.: Maneuver attitude control method and in-orbit verification for flexible satellites with single-gimbal control moment gyros. J. Astronaut. 39(1), 43–51 (2018). (in Chinese) 2. Research on fault mode testing method for satellite attitude and orbit control subsystem. In: The 22th National Symposium on Space Exploration. Beijing: Chinese Society of Space Research (2009). (in Chinese) 3. Zhang, H., Zhang, Y.: Statistical analysis of DFH-3 Serial satellites failure. Spacecraft Eng. 16(1), 33–37 (2007). (in Chinese) 4. Zhang, Y., Li, M., Zhang, J.-R.: Vibration control for rapid attitude stabilization of spacecraft. IEEE Trans. Aerosp. Electron. Syst. 53(3), 1308–1320 (2017) 5. Jiang, Z., Guan, H., et al.: Method of agile satellites’ attitude programming based on on-board data of CMG 2(5), 32–37 (2020). (in Chinese)

Research on Attitude Determination of Spacecraft Two-Stage Composite Control System Gaojian Lv(B) , Chao Xue, Xin Guan, and Linfeng Xing Beijing Institute of Control Engineering, Beijing 100194, China [email protected]

Abstract. The secondary composite control system of spacecraft is located between the spacecraft payload and the spacecraft body. When the secondary composite control system works, the relative attitude between the payload and the spacecraft body changes. The influence of this change on the attitude determination of spacecraft sensors is studied and then the sensors are compensated, so we can realize the accurate attitude control of spacecraft payload and spacecraft body. Keywords: Spacecraft two-stage multiplex control · Spacecraft sensors · Attitude determination

1 Reference With the continuous improvement of spacecraft payload accuracy and higher requirements for maneuverability, the application of two-stage or multi-stage composite control in remote sensing satellites is gradually increasing. In general, high-precision attitude measurement sensors (such as gyroscopes and star sensors) are fixed with cameras, which is called the Payload Platform; Attitude-controlling actuators (such as CMGs, momentum wheels, thrusters, etc.) are fixed with structural system, power supply system, thermal control system etc. in the infrastructure of the satellite, which is called the Service Platform. The active pointing vibration isolation mechanism is fixed between the two platforms to realize the secondary control based on the Service Platform, which is called the Secondary Control Platform. The two-level composite control system is shown in Fig. 1. In common spacecraft, Payload Platform and Service Platform are tied together, and after precise measured on the ground, the coordinate systems of the two platforms are unified. However, due to the joining of Secondary Control Platform, the coordinate systems of Payload Platform and Service Platform are not fixed. When the Secondary Control Platform works, it drives the Payload Platform to point to the target quickly on the basis of the Service Platform’s maneuverability, so the relative coordinate system of the Payload Platform and the Service Platform changes all the time. Fortunately, the actions of the Secondary Control Platform are measurable. So, the relative coordinate system of the Payload Platform and the Service Platform, that is, the relative attitude of the two platforms can be calculated in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 844–849, 2023. https://doi.org/10.1007/978-981-19-9968-0_102

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Fig. 1. Schematic diagram of two-stage composite control system of spacecraft

2 Attitude Determination Analysis In order to improve the pointing accuracy of the camera, it is necessary to unify the installation matrix of the sensors related to satellite attitude determination and the actuators related to attitude control into the Payload Platform coordinate system. In general, the action range of the Secondary Control Platform is small, which of the three-axis attitude angle is generally less than 0.5°. Through analysis and simulation, the attitude control of the satellite caused by the deviation of the installation matrix can be ignored. Therefore, the system design only needs to focus on the unification of sensor coordinates, and the sensors involved mainly include star sensors and gyroscopes. 2.1 Attitude Matrix Calculation of Secondary Control Platform After calibration, the action of the Secondary Control Platform can be measured precisely. Under the 123 rotation sequence, the three-axis attitude of the Secondary Control Platform is assumed to be (φ, θ, ψ), and the corresponding Euler angle attitude conversion matrix is: ⎡ ⎤ 1 0 0 (1) Rx (φ) = ⎣ 0 cos φ sin φ ⎦ 0 − sin φ cos φ ⎡ ⎤ cos θ 0 − sin θ (2) Ry (θ ) = ⎣ 0 1 0 ⎦ sin θ 0 cos θ ⎡ ⎤ cos ψ sin ψ 0 (3) Rz (ψ) = ⎣ − sin ψ cos ψ 0 ⎦ 0 0 1

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Transformation matrix of the Payload Platform coordinate system relative to the Service Platform coordinate system CPP_SP is calculated as: CPP_SP = Rz (ψ)Ry (θ )Rx (φ)

(4)

Sometimes, due to the external force of the components outside the spacecraft (mainly the cable connecting the Payload Platform and the Service Platform), when the Secondary Control Platform is not working, the Secondary Control Platform has a natural attitude (φ0 , θ0 , ψ0 ), also under the 123 rotation sequence. If the natural attitude is very small (less than 0.05°), it can be considered as the new nominal position of the Secondary Control Platform, and it needs to be calibrated; If the natural attitude is large enough to affect the normal operation range of the Secondary Control Platform, it is necessary to consider the active control torque of the Secondary Control Platform to eliminate it. This situation will not be discussed in this paper. When the Secondary Control Platform is required to conduct new nominal position calibration, it can be obtained according to the above analysis: CPP_SP0 = Rz (ψ0 )Ry (θ0 )Rx (φ0 )

(5)

Take this as the new nominal position, and the attitude matrix of the Payload Platform coordinate system relative to the Service Platform coordinate system is: T CPP_SP = CPP_SP · CPP_SP0

(6)

T where, CPP_SP0 means the transpose of CPP_SP0 .

2.2 Attitude Calculation of Star Sensor Measurement Take the installation matrix of the star sensor in the Payload Platform coordinate system as CSTS_PP ,and the attitude matrix (relative to the inertial system) of the star sensitive measurement as CSTS_AIM .The attitude matrix of the Service Platform (relative to the inertial coordinate system) can be calculated: T T · CSTS_PP · CSTS_AIM CSP = CPP_SP

(7)

In order to make the Secondary Control Platform in the new nominal position when the payload points to the target, the attitude matrix of the Service Platform needs to be modified as: T T · CSTS_PP · CSTS_AIM CSP = CPP_SP

(8)

In this way, the attitude of the Service Platform is determined, and the CMGs and other actuators drive the Service Platform to the theoretical attitude. 2.3 Calculation of Gyro Inertial Angular Velocity In general, at least one set of gyroscopes shall be installed on each of the Payload Platform and the Service Platform. The gyroscopes (named gyroscopes 1) installed on the Payload

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Platform are used to calculate the fast pointing of the Payload Platform to the target and measure the stability of the platform after pointing to the target; The gyroscopes (named gyroscopes 2) installed on the Service Platform are used to calculate the maneuvering direction of the Service Platform. In order to improve the reliability, the measurement axes of the two groups of gyros are evenly distributed (when the Secondary Control Platform has no attitude deviation, the coordinate systems of the Payload Platform and the Service Platform coincide), as shown in Fig. 2. Zb

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According to the above analysis, when the Secondary Control Platform starts to control, the attitude between the Payload Platform and the Service Platform changes. When the two sets of gyroscopes are used in combination, it is necessary to perform angular velocity conversion (123 sequence) according to the attitude and the angular velocity of the Secondary Control Platform: ωPP_inSP = ωSP + ωCP_inSP

(9)

where, ωPP_inSP is the inertial angular velocity of the Payload Platform in the Service Platform coordinate system. ωSP is the inertial angular velocity of the Service Platform in the Service Platform coordinate system. And ωCP_inSP is the angular velocity of the Secondary Control Platform action in the Service Platform coordinate system. Calculation of Inertial Angular Velocity of Loading Platform. ωPP_inSP is obtained from the angular velocity measured by the Payload Platform gyroscope. The conversion formula is: T CGyro_PP ωGyro_PP ωPP_inSP = CPP_SP

(10)

where, CGyro_PP is the installation matrix of gyroscopes 1 in the Payload Platform coordinate system. ωGyro_PP is the inertial angular velocity measured by gyroscopes 1. Calculation of Inertial Angular Velocity of Service Platform. ωSP is obtained by from the angular velocity measured by the Service Platform gyroscope. The conversion formula is: ωSP = CGyro_SP ωGyro_SP

(11)

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where, CGyro_PP is the installation matrix of gyroscopes 2 in the Service Platform coordinate system. ωGyro_SP is the inertial angular velocity measured by gyroscopes 2. Calculation of Angular Velocity of the Secondary Control Platform. ωCP_inSP is the angular velocity calculated by the Secondary Control Platform according to the attitude change, which means the angular velocity of the Payload Platform relative to the Service Platform. The calculation formula is:    (12) ωCP_inSP = R3 (ψ) ψ˙ 3ˆ + R2 (θ ) θ˙ 2ˆ + R1 (φ)φ˙ 1ˆ

T

T

T where, 1ˆ = 1 0 0 ; 2ˆ = 0 1 0 ; 3ˆ = 0 0 1 The ωCP_inSP calculation formula is: ⎤ φ˙ cos ψ cos θ + θ˙ sin ψ = ⎣ −φ˙ sin ψ cos θ + θ˙ cos ψ ⎦ φ˙ sin θ + ψ˙ ⎡

ωCP_inSP

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The three-axis attitude angle of the Secondary Control Platform is small, so formula (13) can be simplified as: ⎤ φ˙ ≈ ⎣ θ˙ ⎦ ψ˙ ⎡

ωCP_inSP

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⎤ φ˙ = ⎣ θ˙ ⎦, so Eq. (9) may become: ψ˙ ⎡

Let ωCP

T CPP_SP CGyro_PP ωGyro_PP = CGyro_SP ωGyro_SP + ωCP

(15)

We can deduce: T ωGyro_PP = CGyro_PP CPP_SP (CGyro_SP ωGyro_SP + ωCP )

(16)

Therefore, if the gyroscopes installed on the Payload Platform fail, the gyroscopes installed on the Service Platform can be used for inertial angular velocity replacement. Similarly: T T ωGyro_SP = CGyro_SP (CPP_SP CGyro_PP ωGyro_PP − ωCP )

(17)

If the gyroscopes installed on Service Platform fail, the gyroscopes installed on the Payload Platform can also be used for inertial angular velocity replacement.

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3 Conclusion The introduction of the two-stage composite control system of the spacecraft makes the relative attitude between the Payload Platform and the Service Platform change in real time, thus affecting the attitude determination and control of the spacecraft by the sensors on different platforms. By studying the attitude change of the two-stage composite control platform and the influence of the angular velocity on the Payload Platform and the Service Platform, the method of application using high-precision sensors is deduced, so as to realize the control law calculation of the Service Platform actuator and the two-stage composite control platform, and achieve the high-precision attitude control of the spacecraft.

References 1. Wang, Y., Tang, L., He, Y.: Integrated control method for vibration isolation and pointing control of ultra quiet platform. Aerosp. Control 34(6), 33–39 (2016). (in Chinese) 2. Anderson, E.H., Leo, D.J., Holcomb, M.D.: Ultraquiet platform for active vibration isolation. In: 1996 Symposium on Smart Structures and Materials. International Society for Optics and Photonics, pp. 436–451 (1996) 3. Li, W., Huang, H., Huang, Z.: Active isolation/suppression for satellite micro-vibration with stewart platform. Mech. Sci. Technol. Aerosp. Eng. 34(4), 629–635 (2015). (in Chinese) 4. Zhang, R.: Satellite Orbit Attitude Dynamics and Control. Beijing University of Aeronautics and Astronautics Press, Beijing (2005). (in Chinese) 5. https://zhidao.baidu.com/question/1740224090560401987.html. Accessed 21 May 2022

Design of Dual Robot Collaborative Assembly Scheme for Aerospace Products Lijian Zhang1,2(B) , Dingwen Pan1,2 , and Ruiqin Hu1,2 1 Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China

[email protected] 2 Beijing Engineering Research Center of Intelligent Assembly Technology and Equipment for

Aerospace Product, Beijing 100094, China

Abstract. For the automatic installation requirements of spacecraft components, an automatic assembly scheme based on the cooperation of dual robots A large load robot is used to grab large weight components, and accurately transport it to the installation location. A small load robot is used for the fastener installation between the component and the spacecraft cabin. The large load robot is equipped with a flexible gripping end effector, which can be adapted to spacecraft component with different size. An indoor positioning system is used to realize the coarse positioning between the various parts of the system, and An indoor positioning system is used to realize the coarse positioning between the various parts of the system, and the precise positioning of the components and screw holes can be realized by robot vision. The designed scheme can provide reference for the automation process for China’s spacecraft development. Keywords: 3D model · Cable network · Marking

1 Introduction Spacecraft products usually do not come in batches, so it is difficult to automate the production of batch industrial products like automobiles. The current assembly operation of spacecraft mainly relies on manual operation and is supplemented by simple tools for the installation of different parts. The manual operation method is highly flexible and suitable for the development of diversified and low-volume products. However, manual operation mainly relies on the engineering experience and individual skill level of the operator, which has disadvantages such as poor adaptability, low efficiency, low consistency, and high technical risk. Since it is difficult to repair and maintain the spacecraft after launch, for the production and assembly of spacecraft, it is necessary to strictly ensure the product quality at the ground assembly production stage, and a large number of manual operations bring high uncertainty to the product quality. In addition, for the installation of special components such as radioactivity in spacecraft, direct manual operation will damage the operator’s personal health. L. J. Zhang (1983-)—Male, senior engineer, mainly engaged in the research of spacecraft assembly technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 850–857, 2023. https://doi.org/10.1007/978-981-19-9968-0_103

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For the transformation and upgrading of the aerospace product assembly production field, the application of robotics and other intelligent technologies is also an inevitable development trend [1–11], this paper addresses the current demand for upgrading the development capability of aerospace products, and through research on the application of robotics in the foreign aerospace field, proposes a collaborative automatic assembly method of dual robots for aerospace products, through the collaborative cooperation of two industrial robots to complete the special parts of spacecraft The automated assembly of special spacecraft components is accomplished through the collaboration of two industrial robots.

2 The Current Status of Robotics in the Aerospace Field The automation and intelligence level of spacecraft assembly process in the United States, Russia, European spacecraft and other international developed countries and regions is high. They use advanced automated assembly systems for precision component installation and large component assembly. Generally speaking, the automated assembly system uses the measurement system to determine the relative position relationship, and under the monitoring of the measurement and tracking system, the industrial robot is controlled to complete the conveying, positioning, screw connection and other assembly work. At present, in NASA, ESA and other space sectors, complex assembly processes have been more often used industrial robots, and Fig. 1 is examples of applications of industrial robots for spacecraft assembly.

a) NASA uses industrial robots to assemble bulkheads. b) NASA industrial robot installs hatch. c) NASA industrial robot installing an engine. d) ESA assembly with industrial robots.

Fig. 1. Application of industrial robot in complex assembly

NASA and ESA use industrial robots for space in-orbit assembly and maintenance tests, in-orbit refueling tests, and simulated docking tests between cargo spacecraft and ISS, as shown in Fig. 2. The mode of industrial robots to realize automated assembly mainly adopts the mode of remote integrated control, which mainly includes virtual simulation, positioning and fixing, and real-time monitoring and tracking, as shown in Fig. 3. In addition, in order to achieve complex functions, a lot of research work has been carried out in foreign space fields on the coordinated work of multiple robots and has been applied in the development process of several spacecraft. For example, Canada researched and designed a space station teleoperated robot system consisting of two

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a) NASA industrial robot in-orbit assembly and repair test. b) NASA industrial robot in-orbit assembly simulation test. c) ESA industrial robot simulates spacecraft docking with space station. d) NASA industrial robot refueling test in orbit.

Fig. 2. Application of industrial robot in space

Fig. 3. NASA industrial robot remote integration control

seven-degree-of-freedom robots with a vision camera mounted on the robot and powerful sensors and teleoperators at the end. This system can realize the installation, maintenance and detection of large equipment on the space station. DLR, the German space agency, designed dual robots mounted on the upper and lower rails, respectively, to coordinate the operation of satellite maintenance and ground simulation experiments of the cabin circuit board. The U.S. JPL laboratory developed a teleoperated multirobot system, which consists of two eight-degree-of-freedom AAI-type robots, wrist dexterous hands, the headquarters distributed control system and a teleoperated system. The system allows the operator to perform two-handed coordinated teleoperation, space-oriented operations, and more complex tasks such as replacing the main electrical control box of a satellite. Tohoku University in Nikki built a multi-space robot system, DARTS, which consists of two seven-degree-of-freedom PAl0 robots, two Barrett hands, and six-dimensional force/moment sensors mounted at the end of the arms. The whole system is a teleoperated robotic system consisting of a ground system, a space system, and a software development system. In addition to aerospace, coordinated control of multiple robots has a wide range of applications in the nuclear field above ground and in the deep-sea field below ground. In areas such as nuclear power plants and nuclear weapons, there is a large amount of equipment that must be dismantled at the nuclear material processing site, in addition to the transportation and packaging of heavy lead metals, tasks that take a long time and require the use of a large number of tools. The use of teleoperated redundant multirobots can greatly reduce handling costs and personnel involvement. In the deep sea, multi-robots are also needed for sampling, recovering crashed aircraft, protecting the marine environment, etc.

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3 Overall Solution Design With the rapid development of aerospace technology, the integration of individual equipment and components to be assembled in the structure of aerospace products is getting higher and heavier, and some of the equipment cannot be lifted by crane due to more installation space constraints, so currently they can only rely on human lifting or special auxiliary tooling, in this assembly mode, as the measurement of the hole position and precise attitude adjustment cannot be carried out, the assembly accuracy mainly relies on experienced In this assembly mode, the assembly accuracy relies mainly on the experienced operator to carry out the assembly adjustment, and the assembly accuracy is difficult to guarantee. In addition, to meet scientific exploration, more and more detectors are arranged with radioisotope equipment. At present, all radioisotope implementation operations are carried out by professional personnel, and the operation of personnel is risky and prone to cause injury to personnel. There is a lack of domestic means to assemble space products in these special environments. The current level of automation technology for the assembly of aerospace products in China is low, still relying mainly on the engineering experience of process personnel and the individual skill level of operators, with disadvantages such as poor adaptability, low efficiency, low consistency, and high technical risk. In this paper, we present a tworobot cooperative automatic assembly method for aerospace products: one robot grabs the installed part and positions it to the installation position, the other robot installs fasteners between the part and the main structure of the spacecraft, and the two robots coordinate and cooperate to complete the automatic assembly process of the part. During the assembly process, the assembly status can be monitored in real time with the help of force and vision sensors to ensure the quality of the assembly and to complete the automatic collection of relevant data. The proposed method can provide a reference for the automation process of China’s aerospace product development. 3.1 System Composition

Fig. 4. Composition of the two-robot collaborative assembly system

The composition of the two-robot collaborative assembly system is shown in Fig. 4, with each part divided into.

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(1) Robot 1, with a universal automatic clamping end-effector mounted on the end, grips the device and transports it to the installation position. (2) Robot 2, end-mounted fasteners automatically install end-effectors for the installation of connecting fasteners between the device and the cabin. (3) Equipment transfer vehicle to transport the equipment to be installed from the periphery to the designated location around robot 1. (4) Console for drive control of the two robots and the end-effectors. (5) Indoor positioning system for coarse positioning of the position relationship between robot 1, robot 2, equipment transfer vehicle, and satellite 4 parts. 3.2 Workflow The workflow diagram of the dual-robot collaborative assembly system is shown in Fig. 5. After robot 1, robot 2 and spacecraft module are placed in position, the spatial position of the installation hole location in the coordinate system of the two robots is first obtained by measuring and positioning means for subsequent equipment positioning and fastener positioning and installation. After completing the above calibration, the equipment transfer vehicle carries the equipment to be installed to the designated location around robot 1 under the navigation of the indoor positioning system, and then the robot uses vision to locate the placement of the equipment and then automatically grasp it. After the robot 1 has finished grasping the equipment, it uses the target position data obtained from the pre-determination to complete a series of actions such as putting the equipment in place and fastener installation to complete the installation of the equipment. According to the workflow of the system shown in Fig. 5, in order to ensure the accurate delivery of equipment and fasteners in place, a total of the following links need to be measured and positioned. (1) Determination of the spatial position of the mounting hole position in the robot coordinate system. (2) Determination of the position of the equipment on the transfer vehicle in the robot 1 coordinate system.

The robot and spacecraft capsule are in position

Visual measurement of the coordinates of the mounting holes on the cabin body in the coordinate system of robot 1 and 2

The equipment transfer vehicle moves to the designated position under the guidance of the indoor positioning system

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Fig. 5. Workflow diagram of the collaborative assembly system with two robots

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4 The Main Parts of the Program Design 4.1 Flexible Automatic Gripping of Parts The end-effector of an industrial robot is a mechanism mounted on the arm of an industrial robot that enables it to pick up an object and has functions such as handling, transferring, gripping, placing and releasing the object to an exact position. Most end-effectors are designed in structure and size according to their different operational task requirements, resulting in a wide variety of structural forms. Aerospace products are usually produced in single pieces, and the shapes and sizes of the equipment vary. To realize robotic assembly, designing a dedicated end-effector for each device would cause a great waste, therefore, flexible automatic gripping end-effectors that can be adapted to different sizes of equipment are needed. 4.2 Automatic Installation of Fasteners Automatic fastener assembly is an essential work step of automated assembly, and the fastener assembly process is: positioning - pressing - tightening. After analyzing the action of the industrial robot to install fasteners, the fastener tightening end-effector must have the following functions: ➀ receive the delivered fasteners, and only one set of fasteners can be received per action; ➁ have accurate fastener orientation and guidance, and can realize the position compensation of fasteners and threaded holes; ➂ have a fixed torque function for screws when fastener tightening. In addition, the fastener assembly end-effector requires compact structure, small size and light weight, and does not exceed the rated load of the industrial robot spindle (Fig. 6). 4.3 Visual Positioning of Mounted Parts

Fig. 6. Schematic diagram of equipment placement

Equipment placement is determined in accordance with the following methods. (1) The robot takes pictures at a pre-set location for each movement, in which case the height of the photo point to the ground is known.

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(2) The position of the robot in the field is determined by the indoor positioning system, and the position of the “fixed photo area” in the field can be calculated according to the current position of the robot. (3) The equipment transfer vehicle enters the “fixed photo area” under the guidance of the indoor positioning system, so that the equipment to be installed is captured by the camera on the robot. 4.4 Visual Positioning of Mounting Holes Figure 7 shows an image of the equipment mounting surface on a spacecraft module. The features corresponding to the threaded holes in the plate need to be identified in the image, and the location of the center of the circle in the image is given.

Fig. 7. Photo of flat plate with threaded holes

Image preprocessing

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The image processing process is shown in Fig. 8. Finally, the error between the original contour and the fitted ellipse is calculated, and the contour with the error less than a certain value is recognized as an ellipse, completing the recognition and localization of the threaded hole feature [11].

5 Conclusion In this paper, an automated assembly method based on dual-robot collaboration is proposed for the automated installation requirements of special spacecraft components. Dual industrial robots are used, in which the large-load robot is used to grasp largeweight spacecraft components and transport them precisely to the installation position, while the small-load robot is used to install the fasteners between the components and the spacecraft module. The large-load robot is equipped with a flexible gripping endeffector, and the gripping size of the end-effector needs to be adjustable in order to adapt

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to different sizes of spacecraft components. The small-load robot is equipped with an automatic fastener installation end-effector to realize a series of actions such as positioning, pressing and tightening to complete the fastener installation. An indoor positioning system is used to achieve coarse positioning between system parts, and robot vision is used to achieve part gripping and positioning, and precise positioning of the fastener installation link. The designed solution can provide reference for the automation process of China’s aerospace product development.

References 1. Wei, Y., Zhang, C., Mengwei, et al.: Study on flexible force control on robot arm for spacecraft assembly. Aeronaut. Manuf. Technol. (21), 147–152 (2014) 2. Bu, R., Sun, G., Hu, R., et al.: Flexible force control on robot arm for spacecraft assembly. Spacecraft Environ. Eng. 31(4), 430–435 (2014) 3. Zhang, M., Yu, M., Zhang, Y.: Aeronautical manufacturing technology (20), 26–29 (2013) 4. Qiu, T., Zhang, M., Zhang, L., et al.: The satellite board assembly with robot. Spacecraft Environ. Eng. 29(5), 579–585 (2012) 5. Joubair, A., Bonev, I.A.: Non-kinematic calibration of a six-axis serial robot using planar constraints. Precis. Eng. 40, 325–333 (2014) 6. Klimchik, A., Furet, B., Caro, S., et al.: Identification of the manipulator stiffness model parameters in industrial environment. Mech. Mach. Theory 90, 1–22 (2015) 7. Zhu, Q., Zhong, X., Zhang, Z.: Dynamic collision-avoidance planning of mobile robot based on velocity change space. Robot 31(6), 539–547 (2009) 8. Khalil, W., Dombre, E.: Modeling, Identification and Control of Robots, vol. 23, pp. 447–473. Talyor & Francis (2004) 9. Xiaobing, L.I.: Study of robot active assembly method for round peg-hole. J. Univ. Electron. Sci. Technol. China 30(1), 49–52 (2001) 10. BRANKO SARH: Assembly techniques for space vehicles, 13028 (2000) 11. Hu, R.Q., Zhang, L.J., Meng, S.H.: Research on image ellipse feature recognition and localization based on OpenCV. Comput. Meas. Control 24(12), 116–118 (2016)

Study on the Application of Robot-Assisted Assembly in Satellite Board Assembly Tiecheng Qiu1,2(B) , Qian Zhang1,2 , and Liang Guo1,2 1 Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China

[email protected] 2 Beijing Engineering Research Center of Intelligent Assembly Technology and Equipment for

Aerospace Product, Beijing 100094, China

Abstract. For there were some instruments on the satellite board which were connected to the satellite structure by some cable, there is a proper brace which could uphold it and overturn to achieve satellite board assembly, and there must be many ship-fitters who worked in close cooperation to ensure the assembly precision. This paper made an analysis of the process of the satellite board assembly and raised the project that the assembly process with robot automatic and halfautomatic, to advance the assembly precision and the work efficiency and push the automatic assembly development on the satellite field. Keywords: Robotics · Assembly · Satellite board

1 Introduction Because of the complexity of satellite product development technology, involving control, propulsion, thermal control, payload, mechanism structure, measurement and other multidisciplinary field knowledge, the satellite production is mainly developmental and is a single-piece production [1]. China’s spacecraft development work is based on the development of foreign experience through continuous exploration and innovation [2], relative to the early satellite manufacturing work, the technical level has been greatly improved, but should also be clearly aware that the current level of satellite assembly technology still exists a large gap. Communications, remote sensing satellites, etc. will still be in a research and development mode of work, while the second generation of navigation Phase I and Phase II construction projects, the satellite system reached several pieces of the scale, and several series of satellite technology state is completely consistent, already has the characteristics of the satellite batch production development. Therefore, the development direction of the starship assembly profession should focus on improving the automation, specialization and standardization of the assembly process, in order to improve the effectiveness of the spacecraft assembly work and the technical capabilities in the assembly work [3–10]. This paper presents a detailed analysis of the key process of satellite assemblybulkhead assembly, and proposes a robot-assisted assembly solution to improve assembly efficiency, reduce work intensity and save manpower, and promote the process of automated assembly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 858–865, 2023. https://doi.org/10.1007/978-981-19-9968-0_104

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2 Robot-Assisted Assembly System Solution Design 2.1 Design Ideas According to the generalized design idea, the following ideas are proposed for the ground mechanical support equipment that needs to be used during the final assembly of the bulkhead. 1) For the assembly operation in the horizontal state, design the cabin plate parking rack car, which can be universal and reused, and only need to have the parking function to complete the assembly and electrical testing of the cabin plate in the unassembled state. 2) In the process of hatch assembly, the robot arm takes down the hatch from the parking car, finds the target position through the three pin holes on the main structure of the satellite, calculates the movement path according to its own position, and is monitored by the operator to gradually approach the propulsion module to complete the assembly. 3) Designing hatch docking frames that are not generic or reusable, and for each new model of hatch, only one new hatch docking frame needs to be manufactured, each of which needs to be designed to have a docking with the parking rack car and a flange to connect with the robot arm. Based on the above ideas, the following advantages can be achieved. 1) Only a one-time investment in a hatch assembly robot arm and several parking rack carts according to the model demand, and when the new model comes, just a few simple hatch adapter frames can be produced, which can greatly save costs. 2) Using the robot’s positioning system, it can effectively ensure the accuracy of the bulkhead assembly, which can improve both the quality of the assembly and the work efficiency. 3) Semi-automatic and even automatic assembly, saving manpower and taking another step towards assembly line type final assembly. 2.2 Overall Solution Design Based on the actual working conditions, a robot-assisted assembly scheme is proposed, as Fig. 1 shown, which mainly consists of a mobile platform, robot operation arm, control cabinet, laser tracker and soft-smoothing device, etc. The specific functions are as follows. 1) A mobile platform that transports the robot to different mounting stations, enabling multiple uses of one machine. 2) Laser tracker for position detection of pin holes in the main structure and bulkhead of the satellite. 3) The robot completes the assembly according to the planned movement path of the bulkhead.

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4) The smoothing device provides tolerance amplification in assembly, making assembly easier to achieve. The entire robotic assembly system provides two modes of automatic and semiautomatic assembly. The semi-automatic assembly mode is similar to the current manual assembly mode in that the position and attitude of the panels can be adjusted by the assembler through the hand controller, but the difference is that the robot can directly adjust the position and attitude of the panels through multi-axis linkage, whereas the current assembly mode can only adjust the single axis of the inherent tooling, and the changes in the position, attitude and movement speed of the panels are not intuitive or quantifiable, whereas the use of the robotic The current assembly model can only adjust the individual rotating axes of the inherent tooling. The automated assembly mode is where the robot automatically assembles the pods based on the pin hole locations detected by the laser tracker and the pod motion path programmed offline, i.e., the desired pod motion is programmed to occur.

Fig. 1. Overall schematic diagram of the program

For the position detection requirement of the pin holes during the assembly process, the vision positioning mode is performed by the joint application of the vision system and the robot, considering that the pods may not be able to perform position detection at special positions (horizontal or vertical positions) due to the limitation of the connection lines. The vision inspection system includes two cameras with parallel binoculars to detect the pin hole positions on the main structure of the satellite. The two cameras are fixed to the robot delivery platform, so the mutual position of the cameras and the robot does not need to be recalibrated during each work, which can improve the efficiency of the system. The scheme is shown in Fig. 2 is shown. Visual measurement steps. 1) Position calibration between the camera and the robot. The calibration provides a matrix of the “hand (robot operator end) - eye (vision positioning system)” position conversion relationship, according to which the coordinates of a point obtained from the vision system can be easily converted to the robot coordinate system to facilitate robot position control. This step is performed after both the camera and the robot are fixed on the moving platform, and once the position calibration is completed, the calibration does not need to be repeated without adjusting the position relationship between the robot and the camera in case of changing the workpiece or even the workplace.

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Fig. 2. Automatic assembly solution based on vision inspection

2) Detection of the pin hole position on the main structure of the satellite. Detection method: Before the robot is connected to the battery plate, binocular parallel vision is used to obtain information on the position of the pin holes on the main structure of the satellite relative to the robot coordinate system. 3) Detection of the pin hole position on the bulkhead. Detection method: After the assembler completes the connection between the bulkhead and the interior of the main structure of the satellite, the robot end is connected and fastened to the battery plate by a flexible device; using the binocular parallel vision method, the position information of the pin hole on the bulkhead can be obtained, and the attitude angle of the battery plate in the robot coordinate system can be obtained according to the attitude of each robot joint, and then the pin hole on the bulkhead in the robot coordinate system can be obtained The exact position information of the pin hole on the pod can be obtained. 4) Correlation of the pin hole positions of the satellite main structure and the bulkhead. Detection method: According to the data obtained from step 2) and step 3), the pin holes on the satellite main structure and the bulkhead plate can be associated in the robot coordinate system. 5) The control robot, using the difference between the position of the main structure of the satellite and the pin holes on the bulkhead in the base coordinate system as feedback, closes the loop to control the robot and aligns the main structure of the satellite with the pin holes on the bulkhead.

3 Key Technologies The feasibility of this robot-assisted assembly solution is closely related to certain key technologies and certain equipment selection options, as described below. 3.1 Basic Motion Path Algorithm The assembly idea of the bulkhead and the main structure of the satellite, the coordinates of the three pin holes on each of the bulkhead and the main structure of the satellite are measured by a laser tracker, as Fig. 3 shown. Plane A can be regarded as the plane where the main structure of the satellite is to be assembled. Plane A is stationary during the whole process, and it is the target position of Plane 1, which is called the target plane

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in this paper; m4 , m5 , md , me is two points on two planes, m4 , m5 can be determined according to the relationship between the pin holes on the service module plate and the lower bottom edge of the service module, md , me can be determined according to the relationship between the pin holes on the propulsion module and the lower bottom edge of the service module. the relationship between the pin holes of the propulsion module and the lower bottom edge.

ka ( xa , ya , za )

k1 ( x1 , y1 , z1 ) Plane1

k3 z

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y x

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( x3 , y3 , z3 ) m5

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Fig. 3. Measured coordinates of the six basic pin holes

From this, the position equations of plane 1, plane A, the line k2 k3 , and the line kb kc can be calculated as follows. The plane 1 equation is given by (y1 − y2 )(z1 − z) (x1 − x1 )(z1 − z) (x1 − x2 )(y1 − y1 ) (z − z1 ) = 0 (x1 − x1 ) + (y − y1 ) + (y1 − y1 )(z1 − z) (x1 − x1 )(z1 − z) (x1 − x1 )(y1 − y1 )

(1) The plane A equation is given by (xa − xc )(za − zb ) (xa − xb )(ya − yc ) (ya − yb )(za − zc ) (x − xa ) + (y − ya ) + (z − za ) = 0 (ya − yc )(za − zb ) (xa − xb )(za − zc ) (xa − xc )(ya − yb )

(2) The parametric equation of the straight line m4 m5 is ⎧ ⎪ ⎨ x = (x4 − x5 )t + x4 y = (y4 − y5 )t + y4 ⎪ ⎩ z = (z4 − z5 )t + z4

(3)

Directional vectors. n1 = {(x4 − x5 ), (y4 − y5 ), (z4 − z5 )} The parametric equation of the straight line md me is ⎧ ⎪ ⎨ x = (xd − xe )t + xd y = (yd − ye )t + yd ⎪ ⎩ z = (zd − ze )t + zd

(4)

(5)

Directional vectors. na = {(xd − xe ), (yd − ye ), (zd − ze )}

(6)

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After the equations of the two planes and the lines in the two planes according to the initial establishment, the initial plane, if it is to reach the target plane, is required by the following steps. 1) Calculate the angle between the line m4 m5 and the line md me , i.e. |(x4 − x5 )(xd − xe ) + (y4 − y5 )(yd − ye ) + (z4 − z5 )(zd − ze )|  φ = arccos  (x4 − x5 )2 + (y4 − y5 )2 + (z4 − z5 )2 • (xd − xe )2 + (yd − ye )2 + (zd − ze )2

(7) After finding φ and transmitting it to the robot, the robot control system calculates the angle to be rotated and completes the actual rotation so that the line m4 m5 is parallel to the line md me , i.e.. φ = 0. 2) Calculate the vertical distance between the point k3 and the point kc , i.e. d = zc − z3

(8)

Send d to the robot, the robot control system calculates the corresponding motion trajectory and completes the corresponding motion so that the Z coordinates of the point k3 and the point kc are equal, i.e. d = 0. 3) Calculate the distance in y-direction between the point k3 and the point kc , i.e. D = yc − y3

(9)

Send D to the robot, the robot control system calculates the corresponding motion trajectory and completes the motion so that the y-coordinate of the point k3 and the point kc are equal, i.e. D = 0. The relative positions of the initial plane and the target plane at this time. 1) Calculate the angle between plane 1 and plane A, at this time, the straight line m4 m5 is parallel to the straight line md me , according to the drawings of the service module and propulsion module, it can be concluded that the straight line k1 k3 is perpendicular to the straight line m4 m5 , and the straight line ka kc is perpendicular to the straight line md me , so the angle between the straight line k1 k3 and the straight line ka kc is the angle between the plane 1 and the plane A, that is |(x1 − x3 )(xa − xc ) + (y1 − y3 )(ya − yc ) + (z1 − z3 )(za − zc )|  δ = arccos  (x1 − x3 )2 + (y1 − y3 )2 + (z1 − z3 )2 • (xa − xc )2 + (ya − yc )2 + (za − zc )2

(10) After finding δ and transmitting it to the robot, the robot control system calculates the angle to be rotated along the Y-axis and completes the actual rotation so that the line k1 k3 is parallel to the line ka kc , i.e. δ = 0.

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2) To calculate the distance between plane 1 and plane A, simply calculate the distance between the point k3 and the point kc , i.e. D = xc − x3

(11)

Send D to the robot and the robot control system calculates the distance that needs to be fed in the X direction so that the point k3 coincides with the point kc , i.e. D = 0. 3.2 Robot Motion Forward and Reverse Solutions In the previous section, it was mentioned several times that by giving the robot an angle or a distance, the robot will calculate the trajectory the robot needs to move on its own [11]. Firstly, in order to facilitate the analysis and solution of the motion of the robot parallel mechanism, a spatial coordinate system is established. The origin of the coordinates is at the center of the top surface on the waist of the robot. When the robot is in zero position, the parallel mechanism is in the dashed position in the figure, and the coordinates of the end D of the robot are (l2 , 0, l1 + l3 ). After a certain movement, the end of the robot reaches the position shown, the coordinates of the robot end at this time are ⎧ ⎪ ⎨ x = (l0 + l1 sin φ1 + l2 cos φ2 + l3 sin φ3 ) cos φ0 y = (l0 + l1 sin φ1 + l2 cos φ2 + l3 sin φ3 ) sin φ0 (12) ⎪ ⎩ z = l1 cos φ1 + l2 sin φ2 + l3 cos φ3 That is, (x, y, z) = f (φ0 , φ1 , φ2 , φ3 )

(13)

That is, the position coordinates of the end of the robot as a function of the rotation angle of each joint axis. From Eq. (13) we can also derive that (φ0 , φ1 , φ2 , φ3 ) = f −1 (x, y, z)

(14)

Therefore, after giving the coordinates of the target position at the end of the robot, we can calculate the angle that each rotation axis needs to rotate according to Eq. (14), and thus control the servo motor for controlling the motion of the robot.

4 Conclusion In this paper, we propose a movable robot-assisted satellite assembly solution, which utilizes the excellent motion performance of the robot and separates the existing hatch flipping mechanism and hatch parking mechanism. The robot can realize the hatch flipping function to realize the semi-automatic and automatic hatch docking work, which can meet the use of multiple satellite assembly in different work stations and different time periods. It can effectively reduce the production cost, reduce the manpower expenditure, improve the efficiency and promote the pace of satellite assembly automation. With the development of science and technology, this kind of digital assembly will be the inevitable trend of satellite assembly in the future.

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References 1. Xu, F., Lin, H., Hou, S.: Introduction to Satellite Engineering. China Aerospace Press, Beijing (2004) 2. Yuan, J. (ed.): Shenzhou Spacecraft System Engineering Management. Machinery Industry Press, Beijing (2006) 3. Hong, Yuan, Yi: Production and Operations Management. Wuhan University of Technology Press, Wuhan (2004) 4. Ma, Z. (ed.): Aerospace Vehicle Assembly and Testing Processes. Aviation Industry Press, Beijing (1999) 5. Klimchik, A., Furet, B., Caro, S., et al.: Identification of the manipulator stiffness model parameters in industrial environment. Mech. Mach. Theory 90, 1–22 (2015) 6. Zhu, Q., Zhong, X., Zhang, Z.: Dynamic collision-avoidance planning of mobile robot based on velocity change space. Robot 31(6), 539–547 (2009) 7. Khalil, W., Dombre, E.: Modeling, Identification and Control of Robots, vol. 23, pp. 447–473. Talyor & Francis (2004) 8. Buren, S.: Flexible force control on robot arm for spacecraft assembly. Spacecraft Environ. Eng. 31(4), 430–435 (2014) 9. BRANKO SARH: Assembly techniques for space vehicles, 13028 (2000) 10. Hu, R.Q., Zhang, L.J., Meng, S.H.: Research on image ellipse feature recognition and localization based on OpenCV. Comput. Meas. Control 24(12), 116–118 (2016) 11. Cai, Z.: Robotics, pp. 46–65. Tsinghua University Press, Beijing (2000)

3D Model-Based Design Method for Complex Cable Network Three-Dimensional Marking of Spacecraft Qian Zhang1,2(B) , Tiecheng Qiu1,2 , and Yu Fu1,2 1 Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China

[email protected] 2 Research and Production Division, Beijing 100094, China

Abstract. In this paper, for the newly adopted digital cable development and assembly mode in the process of communication platform satellite assembly, combined with the characteristics of communication platform satellite cable laying, the requirements and precautions of digital cable in the production and laying process are proposed, and the cable network model establishment and marking method is the main breakthrough point. The method of marking the cable network during model establishment is proposed according to the requirements of general installation during cable network laying, which ensures the correctness and reliability of cable laying under the new model, and makes corresponding suggestions for the follow-up work. Keywords: 3D model · Cable network · Marking

1 Introduction At present, the spacecraft development mode of our unit is in the process of changing from two-dimensional design to three-dimensional digital design, and each platform and each model is promoting the digital three-dimensional development mode in phases [1–5]. As an important component of the entire satellite cable network, the three-dimensional development mode is also being implemented gradually, but due to the complex direction of the cable network itself, wide branching, with a certain degree of flexibility in assembly and other characteristics, so that it is compared with the three-dimensional digital design, production and assembly of on-satellite instruments and equipment show certain differences [6, 7]. Communication platform satellites have been implemented in multiple models of digital three-dimensional development mode, how to complete the onboard assembly of communication satellite cable network with high efficiency and quality under the change of development mode is an urgent problem to be solved [8–11].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 866–872, 2023. https://doi.org/10.1007/978-981-19-9968-0_105

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2 Communication Platform Cable Network General Assembly Status There are two modes of communication platform satellite cable network development: the traditional two-dimensional development mode and the digital three-dimensional development mode. In the two-dimensional development mode, the overall cable network production is based on the contact table and cable orientation diagram with the wooden mold sampling, and the general assembly needs to be marked on the wooden mold for cable production before the cable transplantation (the general assembly personnel use label paper for marking), and then the general assembly will be based on the marking and cable orientation diagram and cable installation diagram for cable transplantation. In the two-dimensional development mode, cable network transplantation is mainly based on: cable orientation documents, cable installation diagrams, cable network physical identification and assembly process documents, etc., where the cable network physical identification with cable orientation documents to complete the cable network transplantation orientation, assembly process documents with cable installation diagrams to complete the assembly of the cable network with installation information and assembly work with key indicators. This model has been continued to be used in various models and platforms. In 3D development mode, the cable net is created by the design in the whole star model using 3D method, and then the corresponding nail plate drawing is produced according to the model. The cable net manufacturer processes and produces the whole star cable net according to the nail plate drawing, cable production requirements and cable net node table and other documents, and then the general assembly completes the cable net laying according to the whole star model. The cable net assembly is mainly based on: the physical cable net, the digital assembly model of the cable net and the assembly process documents. The cable net is laid on the bulkhead according to the digital assembly model, and then the cable net is accurately installed and implemented in place according to the process and the installation information in the cable net model. This assembly mode needs to solve two problems: First, it is necessary to fully consider how to lay the flexible cable to the hatch accurately according to the design model, because the cable net production is using the pegboard drawing, eliminating the wooden mold sampling work, so as an important “intermediate link” of the cable net marking work must be moved forward, in the Secondly, when implementing on site, according to the cable net model and related markings to complete the cable net laying requires a high degree of consistency between the model and the physical cable net markings, so how to make the cable net markings and the assembly site implementation process complete together becomes another problem that needs to be solved (Fig. 1).

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Fig. 1. Cable network pegboard diagram and digital model schematic

Considering the above two problems and combining with the process of cable laying in the general assembly site, this paper puts forward corresponding suggestions and implementation methods for the establishment of cable network model and cable network model identification method in digital mode.

3 Cable Network Modeling and Marking Methods In response to the new problems faced by cable network assembly in the digital development mode proposed in the previous section, this section proposes suggestions from several aspects such as cable network model, marking and marking production according to the contents involved in the assembly process, and elaborates the method of cable network assembly based on the cable network model marking, and proposes a feasible solution for the subsequent cable 3D mode assembly. 3.1 Cable Network Model Requirements After the cable network is developed in digital three-dimensional mode, the requirements for the overall input during the cable network laying process are not only the three-dimensional model of the cable network, but also various three-dimensional model requirements for the installation of instruments, installation of direct subordinate parts and cable grounding related to the cable network. reflect the real state of the cabin, as a visual reference when laying the cable. At the same time, the various cable brackets and nylon bases for cable installation are uniquely numbered, so that the cable path can be uniquely identified according to the number when the cable network is laid, thus ensuring the accuracy of laying (Fig. 2).

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Fig. 2. Cable network model schematic

3.2 Cable Network Model Marking Requirements Therefore, it is necessary to consider adding the relevant markings needed for assembly to the model when designing the cable network, and adding the relevant markings to the cable network according to the size information when processing the cable network, and then completing the cable network according to the relevant markings in the assembly stage. Then the cable network can be completed in the assembly stage according to the relevant markings. According to the method and experience of marking on the cable net template during the production mode of two-dimensional template cable net, the following main parts need to be marked. a) The information related to each cable should be marked when marking and should not be combined. b) Cable network marking can be done using cable fixing brackets for fixing cables, clamp numbers, information on structural holes in the bulkhead, etc. c) Setting a starting tying point for each cable as a starting point for laying to facilitate implementation, generally changing the point to the middle section of the main bundle of cables or at the branching point of the main bundle. d) The location of the cable penetration compartment must be marked as an important path confirmation point. e) Cable exit length is the key information, which needs to be checked during the 2D cable network, but this part of the information is hidden in the model during 3D commissioning, so for the exit cable, the last lashing point of the exit needs to be marked as a reaction to this information. f) The cable span is important as a part of the margin control when tying. For this type of tying marking, the last tying point out of the cabin and the first tying point after spanning the cabin need to be marked as a reaction to this information. Important branch points in the cable network alignment, which need to be marked before and after branching (Fig. 3).

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Fig. 3. Cable network marking schematic

When the cable network is laid, the cable network path is shown by accurately speaking according to the above cable marking method, and each cable is shown according to the initial fixed position of the marking as a benchmark, based on which the path is expanded to reflect each situation of the cable fixed path through the cabin, across the cabin, branching, etc., and finally forming the path of each cable laying as shown in Fig. 4. According to this model and the markings on the physical cable network, the laying of this cable can be completed.

Fig. 4. Schematic of cable network laying path

3.3 Cable Network Logo Production Requirements According to the above section of the cable network need to add the corresponding directional markings in the design stage, in the cable network production stage, the relevant markings need to be added to the physical cable network, as the markings are used as the cable laying path, so it needs to be accurate, simple and easier to identify, it

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is recommended to use eye-catching and distinct from other markings color markings. In addition, as the cable net is installed after the existence of cable bundle, the logo on the cable needs to be removed and difficult, and there are requirements for review, so it is recommended that the cable net logo is a permanent logo, with a flight status (Fig. 5).

Fig. 5. Illustration of the physical marking of the cable network

4 Conclusion In this paper, for the development mode of digital cable network, according to the characteristics of the total assembly implementation, and on the basis of the original two-dimensional cable network production when the wooden mold marking method, a method of digital cable network model establishment as well as path marking is proposed, the method has been with the design, process and combined with the field implementation, basically achieved the expected results, after further improvement can be used both as a standard marking method by the design reaction in the cable After further improvement, it can be used both as a standard marking method by the design to reflect in the digital model of cable network and improve the quality and efficiency of cable network implementation.

References 1. Zhen, Z.: Development of three dimensional visualization system for pipe jacking in transmission cable path. Technol. Innov. Appl. 03, 49 (2016) 2. Ding, C.: Research and system development of digital technology for aircraft cable routing. Nanjing University of Aeronautics and Astronautics (2017) 3. Liu, X.: Application research of three-dimensional wiring technology of electronic equipment based on UG NX. Mach. Design Manufact. Eng. 45(09), 67–70 (2016) 4. Zheng, C., Cao, L., Zhou, X., Cai, X.: Application of three-dimensional wiring for power electronic equipment based on Creo/Cabling. Mach. Design Manufact. Eng. 44(06), 27–31 (2015) 5. Wang, F.: Research on virtual cable routing and installation simulation technology. China Academy of Engineering Physics (2006)

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6. Gao, J.K., Liu, J., Ning, R., Yao, J.: Research on cable modeling and assembly planning technology in virtual environment. J. Syst. Simul. (04), 933–935 (2005) 7. Zhang, D.: Research on virtual assembly process design technology of aerospace products and its application. Nanjing University of Aeronautics and Astronautics (2010). 8. Chen, X., Zhang, W., Pan, Y.: Research on digital assembly design of spacecraft. Spacecraft Eng. 17(06), 64–67 (2008) 9. Liang, Y.M.: Selection of cable identification in cabling system. Inner Mongolia Sci. Technol. Econ. 20, 111–112 (2004) 10. Cabling system management. Intell. Build. Urban Inf. (02), 46–48 (2011) 11. Fan, K., Huang, Y., Liu, Z., Zhang, G.: Co-design of satellite 3D layout and cable based on Pro/E software. Spacecraft Environ. Eng. 32(04), 390–394 (2015)

Study on the Optimization of Temperature Measurement Points for Small Satellite Zhijia Li1(B) , Dawei Li1 , and Zhiming Xu2 1 Beijing Institute of Spacecraft Environment Engineering, Beijing 10094, China

[email protected] 2 DFH Satellite Co., Ltd., Beijing 100094, China

Abstract. Temperature measurement points used by small satellites make too much redundancy, which usually led to high cost of development. Measurement points classification method, necessity and optimization were studied, optimization method based on similar point, The optimization method based on similar point, optimization method based on heat pipe network and mutual reference between group satellites method were putted forward. Projects, temperature measurement points were merged and reduced, the temperature measurement points number of a CAST968 small satellite was reduced by By using these methods to projects, temperature measurement points were merged and reduced, the temperature measurement points number of a CAST968 small satellite was reduced by 19.4%, and the temperature measurement points number of an equipment netted satellite was reduced by 27.4%. The satellite thermal control subsystem of these satellites was in good condition, which effectively increased the ratio of development cost to The satellites were in good condition, which effectively increased the ratio of development cost to efficiency. Keywords: Small satellite · Temperature measurement points · Optimization

1 Introduction At the beginning of the development of China’s early small satellite platform, in order to comprehensively assess the satellite thermal design methods, products and implementation processes, the platform and payload alone are commonly arranged thermistors [1, 2] as temperature measurement points, so as to monitor and assess the correctness and reasonableness of the satellite thermal design. With the upgrade of satellite platforms, the number of temperature measurement points used in satellites has also increased, from about 100 points in CAST100 platform to about 200 points in CAST968 and CAST2000 platforms. The increase in the number of measurement points has increased the demand for hardware resources such as sensor devices, measurement channels, acquisition circuits, and cables for temperature measurement, resulting in excessive consumption of platform temperature design resources and increased engineering implementation and testing costs [3–5]. At present, the number of small satellites in orbit has exceeded one hundred, and some satellites have been flying for more than 8 years, accumulating a large amount © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 873–880, 2023. https://doi.org/10.1007/978-981-19-9968-0_106

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of flight data, especially satellite temperature data, has comprehensively verified the small platform satellite thermal design methods, products and implementation process in the selection of engineering design parameters correctness and reasonableness, the small platform satellite thermal design has become mature. The small platform satellite engineering design technology has been comprehensively verified, laying the foundation for the optimal design of satellite temperature measurement settings, and is also the best time to advance to refined design through rough design. At the present stage, the research on optimization methods of temperature measurement points at the level of single machine [6] or parts (components) [7] is more common, but there are no published literature and reference conclusions at home and abroad on the research of temperature measurement point setting methods at the level of large spacecraft systems. Therefore, this paper focuses on the optimization research of temperature measurement point setting method of small platform satellite, proposes the classification method of temperature measurement point setting, the optimization suggestion of temperature measurement point setting of each subsystem of the platform, the optimization method of laying out similar points (similar equipment), the optimization method of laying out points by using heat pipe network method and the optimization design method of laying out points by using the combination of equipment stars, so as to minimize the number of temperature measurement points used by small satellites, realize the burden reduction and cost reduction of satellite platform and increase efficiency, which is useful for It is of great significance to improve the overall design capability of small platform satellites.

2 Temperature Measurement Point Setting Classification and Optimization Method Research 2.1 Classification of Small Satellite Temperature Measurement Point Settings Satellite projects set up the application of a certain number of temperature measurement points is necessary for the satellite thermal control closed-loop control and equipment (parts, components) performance monitoring, satellite temperature measurement points set up should be “function-oriented, monitoring as a supplement” for the principle, set up guidelines are mainly as follows [8, 9]. 1) 2) 3) 4)

Function and performance are affected by temperature Fault location interpretation Health Status Check Temperature Predictability

2.2 Small Satellite Temperature Measurement Point Optimization Method Small satellites have compact layouts, and the temperature differences between devices are often small; at the same time, thermal control subsystems usually use high emissivity coatings, heat pipe networks, and other methods for satellite internal isothermal design, further resulting in different devices often have similar temperature levels and temperature variation trends between them. When the temperature difference between different

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devices is small, setting up separate temperature measurement points for each device becomes an unnecessary redundancy, which can be optimized to reduce the use of onsatellite temperature measurement sensors. The scientific research star needs to obtain as much in-orbit data as possible considering its scientific purpose, so the optimization design is mainly carried out for the equipment star. Through the research and analysis of the thermal control design of the small satellite platform, the following optimal temperature measurement point layout design methods can be summarized. 1) Optimization methods for the placement of proximity points. 2) Optimization of the method of laying points using the heat pipe network method. 3) Optimization of design methods using equipment star combination layouts. The Method of Optimizing the Distribution Points of Similar Points When the satellite is in orbit, the temperature of the equipment on board is mainly determined by the equipment’s own thermal consumption and the thermal boundary conditions in which the equipment is located. When the two devices have similar power consumption and also have similar thermal boundary conditions, they will have comparable temperature levels. Taking advantage of this feature, the temperature measurement points of the two devices can be considered to be combined into one when the layout of the devices meets the following conditions. Layout Optimization Method Using Heat Pipe Network Method Heat pipe has the outstanding characteristics of large heat transfer, compact structure, no moving parts and no energy consumption, which is widely used in spacecraft, mainly in the realization of internal isothermal and heat utilization of spacecraft [10]. On the one hand, the “temperature equalization” effect of heat pipe makes the satellite thermal control subsystem can “connect” the equipment with different heat consumption and thermal environment to reduce their temperature differences; on the other hand, in the optimization design of small satellite temperature measurement sensor deployment, it can make full use of this On the other hand, in the optimization design of small satellite temperature measurement sensors, this feature can be fully utilized to reduce the use of equipment temperature measurement points. Optimal Design Method Using Equipment Star Combination Layout The equipment star is the successor star developed on the basis of the research star. Through the development of the research star, we have a clear understanding of the satellite in-orbit temperature distribution, and through the in-orbit temperature results, we can modify the satellite thermal control model to better predict the follow-up star in-orbit temperature, so we can optimize the ground test and the number of in-orbit temperature measurement points on the star for the equipment star. The study concluded that the odd-even cross-point method can be used to optimize the design of temperature measurement points, specifically by. 1) For a single equipped star, the optimal design of the temperature measurement point can be carried out on using the method of the two previous methods.

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2) For 2 equipment stars, in addition to some key equipment need to paste the temperature sensor, general equipment using A, B star cross-point method, such as A star on the thermal control of the lower unit paste the temperature measurement, then B star thermal control of the lower unit side can not paste;. 3) For 3 and more equipment stars, in addition to some key equipment need to paste the temperature sensor, general equipment using the odd-even cross-point method, to a group of equipment network stars, for example, the equipment stars for each group of 5, such as on-star equipment posture control computer in 1, 3, 5 equipment stars paste temperature measurement points, 2, 4 equipment stars do not paste temperature measurement points; on-star equipment magnetometer in 2, 4 equipment stars paste temperature measurement points, in 1 The magnetometer of the on-star equipment is pasted on 2 and 4 equipment stars, but not on 1, 3 and 5 equipment stars.

3 Small Satellite Temperature Measurement Distribution Analysis and Optimization Design Small satellites mainly include thermal control, attitude control, power supply, overall circuit, star service, data transmission, measurement and control, payload and other subsystems, each subsystem needs to set a certain number of relevant temperature measurement points. Based on the above classification definition and optimization design method, this paper takes a small satellite platform CAST968 electronic star as an example to study the necessity of setting temperature measurement points for each subsystem [11]. 3.1 Thermal Control Subsystem Satellite thermal control temperature measurement points are mainly distributed satellite structure plate, pre-buried or external heat pipe and some equipment installation interface, these equipment installation interface plate, is the thermal control subsystem design temperature control boundary surface, through the control of equipment installation cabin plate temperature to achieve control equipment temperature, so the cabin plate set more temperature control points, temperature measurement points, especially the load bay cabin plate temperature control points more, the number of its use with the load bay heat pipe Network size is closely related to these temperature measurement points are generally distributed in the heat pipe. Therefore these structures on the temperature control points must be, other structures on the temperature measurement points are only used for temperature monitoring, from the use of sense, can be optimized, such as the measurement points on the heat sink surface. 3.2 Attitude Control Subsystem From the distribution of temperature measurement points of the posture control subsystem, the distribution of temperature measurement points is more reasonable. For general temperature measurement point equipment such as central control unit, star-sensitive

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line box, momentum wheel and other equipment, its equipment temperature measurement point setting method can use the similar point (similar equipment) or heat pipe network method of point setting method, set the temperature measurement point on the equipment alone is not necessary, and not distributed in the heat pipe network on the heat measurement and control equipment layout temperature measurement point is necessary. 3.3 Power Subsystem From the analysis of the working principle of the power subsystem equipment, except for the battery, other equipment work without the direct participation of temperature parameters, that is, the equipment temperature measurement points only to monitor the use of equipment temperature. Considering that the power subsystem equipment is distributed in different sections of the platform or outside the cabin, and long-term work, for the arrangement of only one temperature measurement point of the equipment, it is recommended to choose to retain the temperature measurement point. Layout of equipment on the satellite bulkhead heat pipe network, generally with the platform cabin equipment integrated thermal design, its equipment temperature measurement points can be set up using similar points (similar equipment) or heat pipe network method of point setting method, set up separate temperature measurement points on the equipment is unnecessary, it is recommended to cancel the temperature measurement points. 3.4 Overall Circuit Subsystem The overall circuit subsystem only set one temperature measurement point per device, the layout of the equipment on the platform, it is recommended to retain the temperature measurement point settings; layout of the equipment on the satellite bulkhead heat pipe network, generally with the platform cabin equipment integrated thermal design, its equipment temperature measurement point setting method can be used similar points (similar equipment) or heat pipe network method point setting method, set the temperature measurement point on the equipment alone is unnecessary, it is recommended to cancel the temperature measurement It is recommended to eliminate the temperature measurement points. 3.5 Star Service Subsystem Star service subsystem only set up 1 temperature measurement point per device, the layout of the equipment in the platform, it is recommended to retain the temperature measurement point settings; layout of the equipment on the satellite bulkhead heat pipe network, generally with the platform cabin equipment integrated thermal design, its equipment temperature measurement point setting method can use the similar points (similar equipment) or heat pipe network method of point setting method, set up a separate temperature measurement point on the equipment is unnecessary, it is recommended to cancel the temperature measurement point.

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3.6 Measurement and Control Subsystem The equipment of the measurement and control subsystem is laid out in the platform cabin. General platform each equipment to form a coordinated thermal design and thermal management, its equipment temperature measurement points can be set up using the method of similar points (similar equipment) or heat pipe network method of point setting method, set up separate temperature measurement points on the equipment is not necessary, and not distributed in the heat pipe network on the heat measurement and control equipment is necessary to set up temperature measurement points. 3.7 Digital Transmission Subsystem The equipment of the digital transmission subsystem is generally laid out in the platform compartment. The general platform of each device to form a coordinated thermal design and thermal management, its equipment temperature measurement points can be set up using the method of similar points (similar equipment) or heat pipe network method of point setting method, set up separate temperature measurement points on the equipment is unnecessary, and not distributed in the heat pipe network of heat measurement and control equipment is necessary to arrange temperature measurement points. 3.8 Load Subsystem From the load subsystem equipment layout, the load subsystem most of the equipment layout in the load bay Due to the large heat consumption of the load bay equipment, the heat pipe network method is generally used to realize the isothermal design of the bay, and the equipment is distributed on the heat pipe network, and the waste heat generated during work is transferred to the radiator through the heat pipe network to dissipate heat to space. Since the load equipment is mainly dissipated by heat conduction with the cabin plate, the thermal resistance between the equipment and its mounting cabin plate is approximately constant, so the temperature difference between the equipment and the mounting cabin plate (heat pipe network) remains basically the same when the equipment heat consumption is constant. Thus, the temperature of the equipment can be estimated indirectly by measuring the temperature of the heat pipe network where the equipment layout is located. Due to the isothermal design of the heat pipe network, multiple devices are distributed in each heat pipe network, and a large number of equipment temperature measurement points can be saved by adopting an appropriate method of indirectly predicting the equipment temperature by measuring the temperature.

4 Optimization Results of Small Satellite Temperature Measurement Deployment Through the previous analysis of the necessity of setting temperature measurement points for each subsystem of the small satellite platform satellite, according to its temperature measurement point setting optimization method, a CAST968 platform for small satellites as an example of temperature measurement point settings for each subsystem to optimize

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the temperature measurement point setting analysis, temperature measurement point settings before and after the analysis results are detailed in Table 1. Table 1 Optimization results of a CAST968 platform small satellite temperature measurement point. Table 1. Temperature measurement points optimization result for a CAST968 satellite Subsystems

Number of temperature measurement points (pcs) Number of uses before optimization

Optimized number of uses

Reduce the number

Thermal control

84

63

21

Star services

12

9

3

Measurement and control

4

3

1

Power supply

8

7

1

70

66

4

Digital transmission

35

26

9

Load

14

9

4

Total

227

183

44

Control

As can be seen from Table 1, through the above optimization methods, the optimization of the single satellite temperature measurement point settings, the total number of temperature measurement points from 227 to 183, the satellite temperature measurement points can be reduced by about 19.4%. For a group of equipment network stars (5) batch development of the situation, the use of temperature measurement point optimization design, before and after the optimization of the analysis results are detailed in Table 2, a single star temperature measurement points reduced by 27.4%. Table 2. Temperature measurement points optimization result for netted satellite (5) Subsystems

Number of temperature measurement points (pcs) Number of uses before optimization

Optimized number of uses

Reduce the number

Thermal control

60

34

26

Star services

10

7

3

4

2

2

Measurement and control Power supply

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Control

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64

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Digital transmission

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Load

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4

Total

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Table 2 Optimization results of temperature measurement points of an equipment group network star (5pcs).

5 Conclusion For the current problem of excessive redundancy of temperature measurement points on small satellite platforms, this paper researches the classification of temperature measurement points and the optimization method of points, proposes the optimization method of points based on similar points (similar equipment), the optimization method of points using heat pipe network method and the optimization design method of points using equipment star combination, and carries out the optimization design of temperature measurement points on typical small satellite platforms, and the temperature measurement points are reduced by 19.4% (A CAST968 platform small satellite) and 27.4% (an equipment network star), effectively saving on-satellite resources.

References 1. Zhang, J., Wang, H., Sun, J., et al.: Analysis of thermistor application in spacecraft. Chin. Space Sci. Technol. 6, 54–59 (2004). Chinese 2. Stephan, R.A.: Overview of NASA’s thermal control system development for exploration project. In: 40th International Conference on Environmental Systems, AIAA, p. 6135 (2010) 3. Bouwmeester, J., Guo, J.: Survey of worldwide pico- and nanosatellite missions, distributions and subsystem technology. Acta Astronaut. 67, 854–862 (2010) 4. Jaekel, E.K.J., Erne, W., Soulat, G.: The thermal control system of the faint object camera (FOC). In: AIAA 15th Thermophysics Conference (2013) 5. Pearce, J.V., Rusby, R.L., Harris, P.M., et al.: The optimization of self-heating corrections in resistance thermometry. Metrologia 50(4), 345–353 (2013) 6. Deng, W., Zhang, Y., Chen, J.H.: Optimization and realization of digital temperature control system for CPT atomic frequency standard. Acta Electronica Sinica 40(9), 1889–1892 (2012) 7. Xie, L., Liu, Z., Jin, L., et al.: Research on the optimal selection method of temperature measurement points in the thermal deformation of the whole machine. Comb. Mach. Tools Automat. Mach. Technol. 2, 61–63 (2013). Chinese 8. Lei, C., Rui, Z., Zhao, W.: A new method for optimization of temperature measurement points of high-speed electric spindle unit. J. Lanzhou Univ. Technol. 39(3), 22–25 (2013). Chinese 9. Hou, Z., Hu, J.: Spacecraft Thermal Control Technology. China Science and Technology Press, Beijing (2007). Chinese 10. Li, D.: Application of heat pipe technology in spacecraft thermal control. Spacecr. Environ. Eng. 33(6), 625–633 (2016). Chinese 11. Gilmore, D.G.: Spacecraft Thermal Control Handbook, 2nd edn. The Aerospace Corporation Press, EI Segundo (2002)

Design of Model-Based Spacecraft Flight Control Support System Wang Xiaoming(B) , Gao Hongtao, Fan Daliang, Qu Xiaoyu, and Zhang Xiaogong Institute of Remote Sensing Satellites, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. According to the requirement of autonomous and controllable of the aircraft flight control tasks in the new situation, based on the modeling design method, the satellite telemetry data processing, telemetry display content and style are described in digital way. The cross-platform flight control support system based on XML technology is constructed. The industry application process is designed. The whole process of telemetry monitoring link from satellite testing and launch to in-orbit operation has been put forward, and it has been applied to the Terrestrial Ecosystem Carbon Monitoring satellite (CM-1) developed by China to serve the double carbon strategy. Keywords: Spacecraft flight control · Model-based design · Kirin operating system · XML language · Template of telemetry processing and display

1 Introduction Satellite flight control is a very important phase in aerospace engineering. It needs to complete the scheduled flight tasks such as launch and in-orbit test within the prescribed time, accurately judge the satellite status, and respond to various emergencies in time. After the flight control, the whole system should be able to smoothly transfer to the long-term in-orbit operation stage. The flight control monitoring and support system is the only way to acquire the status of the satellite after launch which runs on the platform of the flight control and command system. With the increasing of China’s comprehensive strength and the change of the world’s political and military situation, the demand of the military equipment localization is becoming more and more urgent. The corresponding national key projects arise at the historic moment. This strategic choice is in line with the national security and interests, which is a matter of the fields of information security of national army, government, and scientific research. The application of localization platform has been the trend for spacecraft flight control system. The platform software is represented by the domestic Kirin operating system and “Loongson + Kirin” has become a typical application mode of localization platform in the field of military information system. Telemetry monitoring system is applied throughout the process from test, flight control to in-orbit run. The typical way in the process of satellite development is that © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 881–889, 2023. https://doi.org/10.1007/978-981-19-9968-0_107

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the data processing and display interface are configured according to the requirements in every phase, relatively independently, which is difference between satellites. This is not conducive to the fast implementation and reuse under the situation of quantity production. The model-based interface development method is a relatively new interface development method. In model-based interface development, the user interface is described according to the designed model, and the software automatically generates the interface according to the model. The model describes the information architecture, the data parsing method, the content and layout of the user interface, etc. Users can not only use the standard template, but also develop and enrich the template library based on the standard template. Model-based development has the following advantages: users only need to pay attention to the core requirements of the system without considering the differences of different platform designs, system properties are easy to be identified and characterized by computers, and it can be implemented quickly, supporting consistency and reusability. The model-based flight control support system uses a structured method to describe the data parsing method and the user display interface. The structured data can realize the flow and drive throughout the whole process. It can be applied during the complete period by designed and verified only once, which can greatly improve the design and development efficiency.

2 Task Analysis 2.1 Requirements Analysis of Flight Control Support System The flight control and command system is developed based on Kirin operating system. The flight control support system whose product runs on the flight control and command system must be compatible with Kirin system. Usually, the flight control telemetry page of each satellite is designed and verified by the flight control team in the command center according to the characteristics of the respective model and the habits of the designer before launch. The layout design of different satellites has not carried out unified design work, and the distribution of telemetry parameters is not uniform, which is not conducive to the rapid query, the analysis of satellite in-orbit status, and the timely implementation of disposal measures in the case of multiple satellites in orbit. According to the requirements of standardized and efficient, it is urgent to unify and standardize the layout design of telemetry pages, formulate design specifications, layering and classifying the telemetry parameters, and reduce unnecessary design differences between models. 1) Unify the display style, telemetry classification, telemetry layout, organization identification and other elements of flight control telemetry page, and form a standardized and unified flight control telemetry page at the system level. 2) Based on the satellite system design and product status, develop the subsystem general telemetry page template, reduce the workload of development, and improve the development efficiency. 3) The preparation of flight control should be moved forward, the test system should be compatible, the full-cycle integrated design of test, flight control and in-orbit interface should be carried out. The design and verification of flight control page should be completed in the development stage of satellite, so as to improve work efficiency and benefit.

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The flight control support system should have the functions of telemetry page design, editing, data consistency verification, status control and baseline management. 2.2 Kirin Operating System As a key research project of the core technology, the Kirin operating system was developed by National University of Defense Technology around the “Galaxy” high performance computer since the 1980s, with independent intellectual property rights. Its goal is to break the monopoly of foreign operating system. Kirin operating system provides the kernel, device drivers, communication protocols, graphics engine, etc. for the whole software system, which is an important part of the application software running environment. It provides the abstraction and encapsulation of underlying physical hardware, organization and management of software and hardware resources, control of program execution process, the system call interface, driver, etc. for the upper application software. In order to facilitate the integration with other software, the operating system also provides the standard hardware access interface and other basic operating system services for the upper software. Kirin operating system is developed based on the idea of compatible with Linux. The structure of Kirin operating system is mainly divided into three layers. The bottom layer is the base layer which can be replaced and loaded freely to ensure system security and real-time performance, the middle layer is the FreeBSD kernel, and the upper layer is the Linux compatible library to achieve binary compatibility with Linux platform applications. The technical architecture of Kirin operating system and Windows graphics development is compared as shown in Fig. 1. The kernel of Kirin operating system is Linux kernel, and X-Window graphics service system is used, on which KDE, GNOME, GTK+ and other graphics framework libraries are extended. Compared with Windows graphics architecture, X is a kind of standard, which is easy to be supported and strengthened by third parties. It is easy to install and modify, and will not cause interference to other applications. The abnormal interrupt of the program will only affect the window system, and will not cause damage to the machine or the operating system kernel. 2.3 Extensible Markup Language (XML) XML evolved from SGML(Standard Generalized Markup Language) and retains the powerful markup capabilities of SGML without losing flexibility and openness, which allows developers to define tags freely, and separate tags and content effectively. It has gradually evolved into a cross-platform data exchange format. The prominent advantage of XML is that it can separate the user interface from the structured data, which facilitates the application and transmission of user interface between different systems and different platforms. By this way, it improves the reusability of the design, and greatly shorten the development cycle. XML has the following properties: (1) Self-descriptive: XML allows users to define their own set of tags, and these tags are not limited to a description of the display format, as is the case with HTML.

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KDE

GNOME

GTK+

Other

Windows graphics envirement

X Protocl X Server

X-Windows server

Linux kernel

Windows kernel

Linux graphics architecture

Windows graphics architecture

Fig. 1. The technical architecture of Kirin operating system

(2) Extensibility: XML allows users to define their own markup, which can be used to describe the document structure or customize the data format. (3) Wieldy: Simple to use and grasp compared to SGML. (4) Structured: XML document only describes the data itself, and the display related to the data is completed by the processing program, with the characteristics of the separation of content and display. XML should have a strict syntax specification, an XML file should at least meet the following specifications: (1) (2) (3) (4)

The entire XML document has only one root node. Each element consists of a start tag and an end tag. Elements should be reasonably nested with each other. The attributes of an element must have attribute values, and the attribute values should be enclosed in quotes.

3 Flight Control Support System Design 3.1 System Architecture As mentioned, it is possible to build a flight control support system by using properties of cross-platform of XML. Considering the application scenarios, the flight control support system includes the System-Test System (STS), the Electronical Data System (EDS), and the in-orbit Running Display System (YDS) to realize function of design, editing, verification, transmission, verification, status control of the telemetry page. The page should be transferred to the Control Center (CC) finally. The main functions of the test system are as follows: (1) importing the template of the telemetry page; (2) telemetry page editing; (3) page testing and verification; (4) convert the telemetry page into a text that meets the standard XML interface protocol and export it. The information platform system implements the following main functions: (1) template generation; (2) version control; (3) approval function; (4) verification function; (5) generation and output of

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XML file. The in-orbit monitoring system realizes the following main functions: (1) the telemetry XML file reading, processing and display; (2) page XML file read and display function. The system architecture is shown in the Fig. 2. EDS

STS XML

Baseline Telemetry Matching Verification

XML

YDS

CC

Edit XML

System Test

Approval

Delivery

XML

Launch

In Orbit

Flight Control Version

Fig. 2. System architecture of the flight control support system

3.2 Telemetry Data Processing Telemetry data typically contains the following four data types:unsigned integer, complement integer, single precision floating-point, double precision floating-point. Each type of data is transformed and processed by different formulas according to its definition. For example, the processing formula of complement integer is as follows: ⎧ n−1  ⎪ ⎪ ⎪ Ki × 2i K(n−1) = 0 ⎪ ⎪ ⎨ i=0 X = (1) n−1 ⎪  ⎪ ⎪ n ⎪ Ki × 2i ) K(n−1) = 1 ⎪ ⎩ −(2 − i=0

where X represents this type of data, n is the length of data bits, i is the number of data bits (i = 0 ~ n − 1), and Ki is the value of the bit (Ki = 0 or 1). Most of the telemetry parameters need to be calculated by linear or nonlinear way before the engineering value with practical physical significance can be obtained, including general algebra calculations, trigonometric function calculations, interpolation and fitting operation, look-up table operation. Users can also define their own operation method, implement the interpretation of remote sensing data. The following formula realizes the operation of obtaining the telemetry engineering value after piecewise linear interpolation transformation of the telemetry parameter value, and the output type is double precision floating-point type. If V < V1 X = C1 +

C2 − C1 × (V − C1 ) V2 − V1

(2)

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If Vi < V < Vj Cj − Ci × (V − Ci ) Vj − Vi

(3)

Cn − Cn−1 × (V − Cn−1 ) Vn − Vn−1

(4)

X = Ci + If Vn < V X = Cn +

where X represents the results, the parameter value array [V1, V2, V3 … Vn] is the input coefficient, [C1, C2, C3 … Cn] is the engineering value array. All the operation and processing of telemetry data can be expressed and transmitted using XML file according to the defined protocol, and the processing software based on the same protocol can realize the interpretation and output of XML file. 3.3 Telemetry Display Template The display template of satellite telemetry is divided into two parts: content template and style template. The purpose of establishing the template is to utilize the development experience, and the template library is the collection of the solidified experience. By using the template model, the corresponding relationship between objects and display elements is established. With the support of template and parameterization, the interface content, composition and layout are planned, so as to realize the automatic generation of user interface. Satellite content template is the structural description of satellite telemetry parameters by systematic carding, with strong commonality of different types of satellite. The design of the telemetry parameter is different among different satellite. Even the same type of satellite is not completely consistent. In order to form the general content template, common characteristics of the telemetry need to be found. Only this, the generalized design can be realized. Satellite platforms usually includes power supply subsystem, telemetry and tele-command subsystem, data management subsystem, thermal control subsystem, attitude and orbit control subsystem and data transmission subsystem etc. Although subsystem detailed design of different satellite differs in thousands ways, but it is close to the function of the subsystem structure. The application scenario of the telemetry is consistent also. So the subsystem telemetry can be splitted according to the function module of the subsystem. Then a hierarchical and classified telemetry structure from function module telemetry, subsystem telemetry to satellite telemetry is formed. Each telemetry display of the satellite can select the corresponding telemetry function module according to the satellite equipment configuration, and combine into a complete telemetry. In this mode, each telemetry function module is one or more page products, which can be selected by any applicable satellite. At the same time, the telemetry function module is extensible and updated iteratively with the upgrade of the product. Based on this design idea, a telemetry classification of satellite platform is shown in Table 1. The telemetry in the function module can be further classified and organized according to the needs to form more detailed telemetry groups. Usually, the number of telemetry

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Table 1. Telemetry classification specification of satellite platform Category 1

Category 2

Category 3

Category 4

Category 5 Temperature telemetry

Power supply subsystem

The important Power control Battery telemetry telemetry telemetry

Distribution telemetry

Data management subsystem

The important Health telemetry management telemetry

Task Management telemetry

Auxiliary telemetry

Telemetry and tele-command subsystem

Measurement and control telemetry

Navigation telemetry

Curve telemetry

Thermal control subsystem

Satellite temperature telemetry

Temperature control telemetry

Attitude and orbit control subsystem

Device telemetry

System condition telemetry

Operating mode telemetry

Data transmission subsystem

Power supply telemetry

Data processing telemetry

Transmit channel telemetry

Antenna telemetry

in each group can be controlled at about 20, forming structured telemetry data for quick query and analysis. Any subsystem telemetry not listed above can also be classified and grouped according to the same idea, and the final implementation effect is the same. The satellite style template is a description of the overall attributes of the user interface, such as the macro composition, layout, and style. Its attributes include name, style, content type, location, size and so on. The document structure of XML is very suitable for storing the tree structure of interface templates. Various elements, attributes, comments and so on constitute the basic components of XML files. Based on the syntax specification mentioned above, an XML document can be basically restricted to a special textual tree, which has a structural connection with the direction tree structure of the interface template. After the tree structure is textualized, interface templates can be stored, exchanged and edited freely in the form of XML files. By defining the attribute such as the background color, font, color, position, size, alignment and content type, the display style can be standardized and unified. In order to reflect the aerospace industry characteristics, the primary title usually be named after the satellite development unit and the name of the satellite, the secondary title named after the name of the page, and tertiary title named after the group name. According to the basic display demand, the telemetry display XML protocol should be drew up. The telemetry display template is developed based on the protocol. Users can clearly show content and style by filling in the XLS template file and developed software automatically transform from the XLS files into an XML file, terminal display software reads the XML file and display it to the screen in accordance with the protocol.

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3.4 Application Process By using the flight control support system consisting of STS, EDS, and YDS, the satellite team can easily and quickly generate telemetry processing methods and telemetry pages in the form of data files, and test, verify, control status, distribute, apply, and iteratively maintain the data. The telemetry page is generated based on template, edited and optimized in STS system, and verified with satellite test. The verified XML file of the page will also enter into EDS system and be verified with controlled digital telemetry processing method to ensure consistency. The verified telemetry processing method and telemetry page will be transmitted to the YDS system and the control center at the same time, which are applied to the launch flight control of the satellite and the in-orbit flight mission.

4 Application The designed satellite flight control support system has been successfully applied to the recently launched Terrestrial Ecological Carbon Monitoring Satellite (CM-1). Totally 159 flight control telemetry pages were generated by the system during the development of the satellite. Satellite engineers can generate the page rapidly and automatically on the basis of developed page templates, use the test data to complete the verification. The validated page is transferred to the terminal user in the form of an XML file, and the terminal users read the XML file and automatically display it. By this way it can solve the repeat configuration in multiple terminals with large numbers of people, therefore greatly improves the development efficiency.

5 Conclusion According to the spacecraft mission requirements, this paper designed and developed the cross-platform flight control support system compatible with Kirin operating system. Based on the design of the generic template, the system can realize fast digitization of telemetry interpret and display. It supports the reuse of different types of satellite, and the telemetry display has realized the standardization and unification. The system has successfully applied to the satellite development and launch process. It can be widely used in the development and launch of other satellites, and the development efficiency can be significantly improved in the development of mass satellites.

References 1. Hoffman, H.P.: Model-Based System Engineering Best Practices. Aviation Industry Press, Beijing (2014) 2. Zhang, B., Chen, J., Yang, L., et al.: Research and practice of model-based system engineering in aerospace products. Astronaut. Syst. Eng. Technol. (2021) 3. Shi, M., Wang, B., Cao, Y., et al.: Design and verification of big data processing system oriented spacecraft control mission. Space Eng. (2018)

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4. Guo, S., Shen, G., Wu, C.: Advanced Flight Control System. Beijing National Defence Industry Press (2003) 5. Mei, Q., Huang, D., Lu, Y.: Design method of civil aircraft functional architecture based on MBSE. J. Beijing Univ. Aeronaut. Astronaut. (2019) 6. Hua, W., Bin, S., Yang, L., et al.: A real-time display software based on QML and GPU programming. Ordnance Ind. Autom. (2022) 7. Long, Y., Yin, F.: Development of a local monitoring and control system based on independent controllable platform. Comput. Meas. Control (2021) 8. Li, C., Yao, J., Xin, C.: Design of launch vehicle ground test system based on Kirin operation system. Comput. Meas. Control (2018)

Big Data Workshop Session 1

Research and Application of 5G Massive MIMO Antenna Weight Intelligent Optimization Based on 4G/5G Coordination Zixiang Di(B) , Xinzhou Cheng, Jiajia Zhu, Yi Li, Lexi Xu, Jinjian Qiao, Lu Zhi, and Wenzhe Wang Research Institute, China United Network Communications Corporation, Beijing, China [email protected]

Abstract. This paper introduces and analyzes the principle, current situation and application of 5G massive MIMO antenna weight intelligent optimization. Further, this paper presents an intelligent optimization method of massive MIMO antenna weight based on 4G/5G coordination in order to solve the high backflow problem of 5G macro-cell, which aims to improve the dwell capacity and split-flow ratio of 5G network. Because there are many combinations of massive MIMO antenna weight, the ergodic search for the optimal solution requires high system computational power. This paper also proposes to take the prediction result of objective function that can be obtained by time series prediction algorithm as the starting condition of weight optimization. By setting startup condition, the efficiency of weight optimization can be improved, and the computational overhead and power consumption of base station also can be reduced. Finally, through the deployment and application in the demonstration area, the practicability of the method is verified. Keywords: 5G massive MIMO · Antenna weight · Intelligent optimization · High backflow

1 Introduction At present, massive multiple-input multiple-output (MIMO) technology has been widely used in the 5th generation mobile communication (5G), which can provide services to multiple users at the same time through a large antenna array with hundreds of antennas [1]. Though adjusting the weight values of massive MIMO (MM) antenna to optimize the signal coverage effect in horizontal and vertical directions of 5G cell, the dwell capacity of 5G network and user perception can be improved [2]. Considering the diversity of 5G coverage scenarios, there are many combinations of MM antenna weight configurations, which increases the difficulty of antenna feed optimization and adjustment. To improve the efficiency of network optimization and reduce labor costs, MM weight adjustment can be combined with AI technology, which has certain intelligent, dynamic and selfoptimization ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 893–901, 2023. https://doi.org/10.1007/978-981-19-9968-0_108

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2 Weight Optimization Principle 5G MM supports flexible five-dimensional antenna weight adjustment including horizontal beamwidth, vertical beamwidth, beam direction angle, beam down tilt and beam number [4]. 2.1 Beamforming 5G MM can scan in time and space with using multiple narrow beams, and can track multiple users with three-dimensional beamforming (BF) technology [5]. The main process of BF includes channel correction, weight calculation, weighting and beamforming. BF uses the principle of interference, when two wave peaks (or troughs) meet, the energy is added. Conversely, if the peak and trough meet and the amplitude is zero, as shown in Fig. 1.

Fig. 1. Downlink beamforming example

2.2 MM Weight Optimization Process The coverage and interference optimization of 5G need to take into account the optimization of synchronization signaling block (SSB) and channel state information-reference signal (CSI-RS) at the same time [6].

Fig. 2. Weight intelligent optimization process

The common process of weight intelligent optimization is shown in Fig. 2. For a single cell, the total number of weight combination can reach tens of thousands in theory, which requires very high computing power of the system for weight iteration.

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Therefore, how to make the intelligent optimization of MM weights more efficiently applied to the current network is also a problem to be solved by network optimization engineers. Some research institutions use quantum computing to solve the requirements of computing power, which can realize the exponential upgrading of computing power [7]. However, considering the cost and other practical reasons, it is impossible to deploy in the mobile communication network at the current stage. In the case of 5G non-standalone (NSA) networking, some scholars use 4G minimization of drive tests (MDT) location information (longitude, latitude and altitude) to obtain the geographical distribution of the measurement report (MR) of 5G cell, so as to optimize the combination number of weight iterations. However, at present, 5G standalone (SA) networking has dominated. Some scholars also use some bionic algorithms, such as ant-colony optimization (ACO) algorithm, to optimize the computational performance, find the relatively good solution and reduce the computation overhead [8, 9].

3 4G/5G Coordination Algorithm At present, 5G does not support UE to report global navigation satellite system (GNSS) location information. And the operator has not made protocol specifications for horizontal angle of arrival (HAoA), vertical angle of arrival (VAoA), timing advance (TA) and other auxiliary positioning fields. Therefore, in the case of 5G SA networking, the geographical distribution of 5G users is difficult to achieve accurately. In this paper, 4G/5G cooperation is used to estimate the location of high backflow area of 5G cell.

Fig. 3. High backflow 4G/5G cell pair and area identification

3.1 High Backflow Cell Pair and Area Identification 5G cell backflow ratio Bf _ratio5Gcell can be expressed as (1), Nswitch_data is the number of 5G cell to 4G cell based on switch, Nredirect_data is the number of times 5G is redirected to 4G cell, NUE_context_release is the total times of UE context release. 5G split-flow ratio Sf _ratio5G can be expressed as (2), T5G_data is the data traffic generated by 5G base

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stations in the area, T4G_data is data traffic generated by 4G base stations in the same area. Bf _ratio5Gcell = (N switch_data + Nredirect_data )/NUE_context_release

(1)

Sf _ratio5G = T5G_data /(T 5G_data + T4G_data )

(2)

Because it is difficult to obtain 5G signaling and traffic detail record (XDR) data and the low data quality, it cannot accurately match to the 4G cell which 5G users fall back to by redirection. The optimal 4G/5G cell pair can be found by some factors such as coverage area, distance and number of interoperability, as shown in Fig. 3 (A). The identification rules are as follows: • Select 5G macro-cells with high backflow ratio which need to be optimized. • Distance is the distance between 4G and 5G base stations, which can be defined as within 300 m. α is the included angle between the azimuth of 4G cell  NAB and the azimuth of connecting line of 4G/5G base station  NAC , β is the included angle between the azimuth of 5G cell  N  CD and the azimuth of connecting line of 4G/5G base station  N  CA . The 4G cell group Cell 4G_group of high backflow 4G/5G cell pairs can be identified through (3) and (4). • Query the number of 5G users in 4G cells based on the wireless side trace data or the UE capability information inquired through the network management, and select a large number of 4G cells as the best matched 4G cell group. αo =   Cell4G_group =

NAC

−

NAB , βo

=

N  CA

−

N  CD

(3)

Min(|α| + |β|), ||α| − |β|| < 90◦ , (|α| + |β|) < 180◦ , if α ∗ β < 0 Min(|α| + |β|), ||α| − |β|| < 90◦ , if α ∗ β ≥ 0 (4)

The coverage boundary of 5G cell can be estimated according to the location that UE return to 5G from 4G, as shown in Fig. 3 (B). The UE will perform service-based B1 event measurement before UE return to 5G. When the RSRP of 5G cell reaches − 114 dBm (the threshold that UE fall back to 4G), the UE position is determined by associating the location information (longitude and latitude) of the latest MDT sampling data in the first 10 s reported by the UE in the 4G network. On the premise of area gridded which can be defined as 5 m * 5 m, the sampling point falls into each grid. Then, the grid is geographically clustered by DBSCAN clustering algorithm [10], and the clustering boundary is used to identify the range of 5G high backflow area. 3.2 ACO Weight Iteration Algorithm Ant colony algorithm provides positive and negative feedback mechanism by leaving pheromones on the path, which has certain advantages in dealing with complex problems such as traveling salesman problem (TSP) [11].

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Fig. 4. ACO weight optimization process

The process of ACO weight iteration algorithm is shown in Fig. 4. Firstly, initialize the parameters, including the iteration number threshold M and the pheromones parameter a, heuristic function parameter b, pheromone volatilization factor a, pheromone intensity coefficient L. The probability of selecting the weight n in the Eth iteration PnE can be expressed by formula (5). τn (i) is the pheromone concentration at time i and weight n, ηn (i) represents the expected value of the weight and the expected degree of the weight n at time a. The initial value of weight pheromone concentration is τ0 , the expected initialization of weight is η0 , the initialization selection probability of each weight is 1/N , and the weight selection adopts the roulette probability selection method. PnE = 

τn (i)a ηn (i)b a b n∈W τn (i) ηn (i)

(5)

Weight pheromone update method can be expressed as (6). Δτ j represents pheromone concentration which was left by the kth ant on the weight. If the ant does not select the weight, the Δτ j is 0, and Δτ is the sum of all the pheromone concentrations left by ants on the weight. The more times the weights are selected by ants, the greater the pheromone concentration. The pheromone concentration release mode adopts Ant-Cycle model.  τ (i) = p ∗ τ (i) + Δτ − 1  ,0 < ρ < 1 (6) Δτ = Jj=1 Δτj Δτ j can be expressed as (7), L is a constant, indicating that the pheromone increases the strength coefficient, which determines the convergence speed of the algorithm, η determined by the value function, if η is larger, the higher the pheromone concentration left by ants.  L ∗ η, Selected Δτk = (7) 0, Other

3.3 Objective Function Prediction In order to address the issue of 5G high backflow, taking into account the factors of coverage and traffic, the weight optimization objective Fitness can be expressed as (8). wrsrp is the coverage weight, wtraffic is the 5G traffic weight, wBf _ratio is the 5G backflow ratio weight, SSBRSRP t is the average level value of cell t, Traffict is the data traffic of cell t, and Bf _ratiot is the 5G backflow ratio of cell t. Fitness = wrsrp ∗ SSBRSRP t + wtraffic ∗ Traffict + wBf _ratio ∗ Bf _ratiot

(8)

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In the process of weight optimization, the target gain of effect evaluation Fitness_Gain expressed as (9), which can be obtained by comparing the fitness before and after of weight update. After multiple rounds of iteration, the weight parameter group that maximizes the target gain is output. LSTM algorithm [12] is introduced to predict fitness. The next result is predicted by the previous T times weight optimization result, and the target gain rate can be calculated by (10). If the predicted target gain rate is greater than the threshold Thr, the weight optimization process starts. Otherwise, the weight optimization is suspended to reduce the algorithm overhead of weight iteration and improve the efficiency of weight optimization. Fitness_Gain = Fitnessnew − Fitnessold Fitness_Gainratio = |

Fitness_Gain | Fitnessold

(9) (10)

3.4 4G/5G Coordination Algorithm Process The overall intelligent optimization process of MM antenna weight based on 4G/5G coordination is shown in Fig. 5. The main steps are as follows:

Fig. 5. MM antenna weight intelligent optimization process based on 4G/5G coordination.

• To extract KPI through professional network management, delimit the area need to be optimized, and select 5G high backflow problem cell in the area. • To initialize the azimuth, horizontal lobe angle width and dip angle range of weight optimization through the identification of high backflow 5G cell and area, and combined with the initial coverage target area of 5G cell. • To set the adjustment step and iteration times for each antenna weight, adjust them accordingly in the iteration process, use ACO algorithm to perform the iteration, obtain the optimal antenna weight combination after the iteration times, and update the weight.

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• The next result is predicted by the previous T times weight optimization result. To determines whether to perform the next weight optimization process by judging whether the predicted target gain rate meets the threshold, and update the LSTM dataset synchronously.

4 Application Effect Select a demonstration area which has 149 5G macro-cells and 220 4G macro-cells in a city for evaluation and verification, and the parameter configuration information as shown in the Table 1. To execute MM antenna weight intelligent optimization algorithm weight optimization once every hour to deal with high backflow problem cells. Table 1. Parameter configuration information. Parameters

Value

gNB Antenna

64TR, height: 20–35 m

5G frequency band and bandwidth

3.5 GHz SA, 100 MHz

High backflow ratio

Bf _ratio5Gcell >6%

Fitness_Gainratio Thr

5%

LSTM timestep T

12

ACO parameters α, β, ρ, L,M

1, 5, 0.5, 500, 360

The evaluation effect of LSTM model trained with 3-month historical data is shown in Fig. 6. It can be seen that both the training set and the testing set have good fitting effect. After 500 times of epoch, the determination coefficient (R2 score) of the testing dataset can reach 0.75. If the dataset is added, the fitting effect will be better.

Fig. 6. R2 score of the training dataset and testing dataset.

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After a week of weight optimization in the demonstration area, the average RSRP of 5G macro-cells in the area increased by 2.33 db, the good coverage ratio increased by 2.15%, the split-flow ratio increased by 2.05%. And the average backflow ratio of high backflow cells decreased by 2.09%, as shown in the Table 2. Table 2. Application effect of MM weight optimization in demonstration area. KPI

Before

After

Optimized gain

Average SSB-RSRP

−78.96 dBm

−76.63 dBm

2.33 db

Good coverage ratio (SSB-RSRP> −110 dBm)

94.21%

96.36%

2.15%

5G split-flow ratio

27.44%

29.49%

2.05%

Average backflow ratio

9.06%

6.97%

2.09%

5 Conclusion In order to deal with the high backflow problem of 5G macro-cell, this paper proposes an intelligent optimization method of MM antenna weight based on 4G/5G coordination. The method can effectively improve the dwell capacity and split-flow ratio of 5G network. LSTM algorithm is used to predict the result of the objective function as the starting condition of weight optimization, which can avoid frequent weight adjustment and lower the power consumption of the base station. Finally, the practicability of this method is verified by its application in the existing network of the demonstration area. Acknowledgement. This work was supported in partially by National Key R&D Program of China under Grant 2018YFB1800800.

References 1. Bjornson, E.: Massive MIMO for 5G. In: 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Stockholm, pp. 1–58. IEEE Press (2015) 2. Adachi, F., Takahashi, R.: On understanding of massive MIMO beam-forming from multiuser joint transmit-receive diversity principle. In: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Stockholm. pp. 1–5. IEEE Press (2021) 3. Zhang, Z., Wang, X., Long, K., et al.: Large-scale MIMO-based wireless backhaul in 5G networks. IEEE Wirel. Commun. 22(5), 58–66 (2015) 4. Xu, L., et al.: Telecom big data assisted bs resource analysis for LTE/5G systems. In: 18th IUCC, Shenyang, pp. 81–88. IEEE Press (2019) 5. Simonsson, A., Sven, O., et al.: Dual polarization beamforming coverage demonstrated with 5G NR SSB. In: 32nd PIMRC, Helsinki, pp. 800–804. IEEE Press (2021)

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6. Zeng, T.Y., Chang, Y.Y., et al.: CSI-RS based joint grouping and scheduling scheme with limited SRS resources. In: 9th PIMRC, Bologna, pp. 1–6. IEEE Press (2018) 7. Travis, S., et al.: Quantum computers for high-performance computing. IEEE Micro 41(5), 15–23 (2021) 8. Ahmadi, S.: 5G Network Architecture, pp. 1–194. Academic Press (2019) 9. Xu, G., Xiao, T., et al.: Research on intelligent optimization of massive MIMO coverage based on 5G MR. In: 2020 IEEE International Conference on ISPA/BDCloud/SocialCom/SustainCom, Exeter, pp. 1455–1459. IEEE Press (2020) 10. Xu, L., Cao, Y., Yang, H., Sun, C., Zhang, T., et al.: Research on telecom big data platform of LTE/5G mobile networks. In: 18th IEEE International Conferences on Ubiquitous Computing and Communications, Shenyang, pp. 756–761. IEEE Press (2019) 11. Bu, Y., Li, T., et al.: A weighted max-min ant colony algorithm for TSP instances. IEICE Trans. Fundam. Commun. Comput. Sci. 52(2), 894–897 (2015) 12. Chao, K., et al.: Data mining based modeling and application of mobile video service awareness. In: Sun, S., Chen, N., Tian, T. (eds.) ICSINC 2017. LNEE, vol. 473, pp. 389–396. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7521-6_47

A Method of 5G Core Network Service Fault Diagnosis Shiyu Zhou1(B) , Yong Wang2 , Xiqing Liu1 , Xinzhou Cheng1 , Zhenqiao Zhao1 , and Tian Xiao1 1 China Unicom Research Institute, Beijing, China

[email protected] 2 China United Network Communications Group Corporation, Beijing, China

Abstract. Due to the virtualization of 5G core network, the network architecture is complex and there are a large number of network elements and interface. Once problems occur, fault diagnosis is difficult. Traditional operation and maintenance cannot meet the requirements of fault diagnosis based on manual and expert experience. This paper proposes a fault diagnosis method based on multi-source data, which collects and manages log data, alarm data, and performance data in a unified manner, sets up scenarios according to different service fault, and applies data to automatically locate and define faults. The method greatly improves operation and maintenance efficiency and ensures user awareness. Keywords: 5G core network · Service fault · Quality of service · Fault diagnosis

1 Introduction The network architecture of 5G (The fifth generation mobile communication network) [1] core network has undergone tremendous changes. A large number of network equipment have been virtualized. The virtual network structure is extremely complex. The network elements and interfaces of 5G core network are numerous and complicated, and the network data involved is massive and complex. There are many problems and difficulties in troubleshooting, which pose new challenges to operators’ operation and maintenance and network optimization. At present, most commonly used fault diagnosis methods rely on traditional manual locating and expert experience, which makes it difficult to develop automatic methods. On the other hand, performance data are used to determine the deterioration of indicators, it is also difficult to locate fault. In addition, the current network management is still relatively rough, alarm management and log management functions are seriously insufficient, it is hard to see the network details. Log data and alarm data are insufficient research. Therefore, based on multi-source data of 5GC (5G core network), this paper proposes a kind of fault diagnosis method, aiming at network automation and intelligent. The log data, performance data, alarm data, XDR (External Data Representation) data, are collected and managed in a unified manner. Then automation tools and scenario-based © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Wang et al. (Eds.): ICSINC 2022, LNEE 996, pp. 902–910, 2023. https://doi.org/10.1007/978-981-19-9968-0_109

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design are adopted to locate fault, which can effectively improve the fault diagnosis ability and simplify the operational process. On the basis of ensuring services capability and user experience, maximize profit on network investment and value can be realized.

2 5G Service Procedures In the study of mobile network evolution, it is a general consensus that 4G (The fourth generation mobile communication) networks will coexist with 5G for a long time. Therefore, for the traditional data services of the operators, such as, internet service, video service, download service, 4G/5G interoperation and services continuity will be the primary issue in the network evolution process [2]. The Internet access procedures involves user registration, service request, PDU session establishment, handover, policy association and other procedures. The following part takes registration procedures and 4/5G handover procedures as an example to introduce the specific signaling procedures. 2.1 The Registration Procedures Registration procedures is the basic procedures in 5G typical signaling procedures, including initial registration, mobile registration update, periodic registration update, emergency registration, etc. Initial registration is the most common registration procedures. Initial registration can be divided into: UE requests registration from the network, context acquisition, AMF selection (optional, if the AMF of the service changes, AMF selection is required), authentication, IMEI verification, register UDM/obtain signing data, notify old AMF to de-register (optional), establish PCF association, obtain access and mobility policies, the PDU Session status update or release, and registration complete. It may also include AUSF selection and PCF selection. The following figure is a brief diagram. For details, please refer to 3GPP standard protocol 23.502 [3]. 2.2 System Interworking Procedures with EPC During the coexistence of 4G and 5G, the handover procedures is also a basic procedure to ensure users’ friendly experience of data services in the scenario of non-continuous 5G coverage. The convergence deployment of AMF and MME/SGSN, SMF and GWC, UPF and GW-U, UDM and HSS, PCF and PCRF are common way in the current network. Also the N26 interface is deployed between AMF and MME to realize the interoperation procedures for meet the requirements of users to move between 4/5G and ensure service continuity [4]. The deployment scheme is shown in Fig. 1. This paper briefly introduces the 4/5G interoperability procedures by taking the handover from connected 5GC to EPS in the case of N26 interface deployment as an example. For details, please refer to 3GPP standard protocol 23.502. When UE is in the connected state, no matter the movement of 5GC to EPC or EPC to 5GC occurs, the cross-system switching process will be performed. During the switching process, HSS+UDM will no longer accept registration requests from AMF or MME for the UE.

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3 5GC Data Service Fault Diagnosis Method Based on multi-source data and combined with fault scenario construction, the diagnostic process can be divided into the following four steps. The Fig. 1 shows the fault diagnosis process. 1) Fault Scenario Construction: For different group fault scenarios, the fault scenario library is constructed according to the related sub-process of the core network. 2) Indicators Analysis: Select different indicators based on different fault scenarios to check whether the indicators are lower than normal indicators. 3) Fault Diagnosis: FM data and NE failure log data are used to fault diagnosis. 4) Signal tracking: View signaling data and trace signaling to locate faults.

Data service Group fault

Fault Scenario Selection

KPI related scenarios

Scenario Diagnostic

PM/FM data Fault Rule Database

database

association Fault Cause Determination Fault cause feedback

PM/FM/log data association

Log Fault Cause

Signal

Determination

tracking

Troubleshoot

Troubleshoot

Troubleshoot

Fig. 1. Flow chart of fault diagnosis method

Then we will introduce the proposed method in detail. A. The Data Collection Specific data include: 1) operation logs, security logs, network element run logs, user/service operation failure event logs, which can be obtained from network elements and OMC (Operations and Maintenance Center). 2) FM data from network elements 3) PM data from OMC 4) XDR data Operation logs include the network elements or application name, operation content, operation time, operation source IP address, operation user, operation result, and operation log level.

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Security logs should include operation content, operation time, operation source IP address, operation user, operation result, and operation log level. Network elements run logs should contain the print time, log level and print log information content. User/service operation failure event log should include registration, service requests, PDU session setup/update/release, DNS query, UE configuration updates, N4 session setup/change/release, handover (based on Xn or N2 interface), RAN/AMF configuration Update, 4/5G interoperability (based on N26 interface), SMS, VoNR (Voice/Video over New Radio), EPS Fallback (Evolved Packet System Fallback) and other event types. B. Group Data Service Fault Analysis According to the different group data service fault scenarios, such as access to the Internet failure, download failure, 4/5G interoperability failure, video lag, we build a scenario diagnostic library. Maintenance personnel can choose different diagnosis scenario, take the different diagnosis items, to locate fault. The final diagnosis results can be used for reference by operation and maintenance personnel. If it is a certainty result, advice solution can be provided directly. According to different scenarios and based on user 5G data service established procedures, sub-processes are refined, and problems are delimited to each profession for optimization through delimiting analysis. Access to the Internet Failure Analyze Based on the Internet access service procedures, it can be divided into sub-procedures such as registration, service request and PDU session establishment, and scenarios are constructed according to the sub-procedures (Table 1). Download Failed Analyze Based on the download service procedures, it can be subdivided into registration, service request, PDU session establishment, etc. The scenario is constructed based on the subprocedures, and the average downlink rate and low-speed ratio of service indicators are increased. Service sub-scenarios such as registration, service request, PDU session establishment, handover, and policy association are the same as sub-scenarios of Internet access. Related service indicators are added in download failure scenarios, as shown in the following table (Table 2). Video Lag Analysis Based on the video service request process, the scenario is constructed with service indicators (Table 3). 4/5G Interoperability Failure Analysis Based on 4/5G interoperability scenarios, it can be divided into 5GS to EPS handover using N26 interface, EPS to 5GS handover using N26 interface, 5GS to EPS Idle mode mobility using N26 interface, and EPS to 5GS Mobility Registration Procedure (Idle and Connected State) using N26 interface. The scenarios are constructed to analyze 4/5G interoperability failure problems (Table 4). C. Indicators Analysis According to the indicators related to the scene, we start from the occurrence time of group fault, make statistics on the indicators, and select the historical indicators 31 days

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Fault scenario

Sub-procedures

Related KPI indicators

Related network elements

Access to the Internet failure

Registration

Authentication success rate

AMF

Initial registration success rate

AMF

Registration success rate

AMF

Success number of UECM registrations initiated by AMF

AMF

Success number of UECM registrations initiated by SMF

SMF

UE context registration success rate

SMF

Service request

Service request success SMF rate Session context establishment success rate

SMF

PDU session establishment

PDU session establishment success rate

SMF

Handover

AMF handover success AMF rate

Policy association

AM policy association established success number

PCF

SM policy association established success number

PCF

PFCP sessions established success number

PCF

in the same period for comparison to find out the anomaly indicators. We use the 3-sigma algorithm as the indicators anomaly detection algorithm [5]. Assuming that the current detection indicator is Xt, the time sequence samples of the KPI in the previous 31 days corresponding to the same period of Xt are denoted as X =

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Table 2. Download failed analyze scenarios Fault scenario

Sub-procedures

Related KPI indicators

Related network elements

Download failure

Registration

–

–

Service request

–

–

PDU session establishment

–

–

Handover

–

–

Policy association

–

–

Service indicator

Downlink mean rate

AMF/SMF/UPF

Low-speed ratio

AMF/SMF/UPF

Table 3. Video lag analyze scenarios Fault scenario Sub-procedures Video lag

Related KPI indicators

Service indicator Downlink mean rate of video services RTT downlink mean latency

Related network elements AMF/SMF/UPF AMF/SMF/UPF

Average number of streaming AMF/SMF/UPF media lag Average time of streaming media lag

AMF/SMF/UPF

Number of video play failures AMF/SMF/UPF

(x1, x2, x3 … , xn), if there is insufficient data in the previous 31 days, the data will be extended day by day until 31 samples are taken. Then we calculate the mean, which is denoted as μ. μ = 1/n(X 1 + X 2 + . . . + Xn)

(1)

The standard deviation σ is calculated as follows: σ = sqrt(((x1 − μ)2 + (x2 − μ)2 + . . . . . . (xn − μ)2 )/n)

(2)

We use the following scenarios to determine outliers: If μ−3σ 0.8 γ = 0.5 if 0.3