This book brings together papers from the 2019 International Conference on Communications, Signal Processing, and System

*152*
*122*

*English*
*Pages 2719
[2720]*
*Year 2020*

*Table of contents : ContentsFlame Detection Method Based on Feature Recognition Abstract 1 Introduction 2 The Process of Flame Identification 3 Color Feature Recognition of Flame 4 Dynamic Feature Recognition of Flame 4.1 Irregularity 4.2 Similarity 4.3 Stability 5 Conclusion Acknowledgements ReferencesSmall Cell Deployment Based on Energy Efficiency in Heterogeneous Networks Abstract 1 1 Introduction 2 2 System Model 3 3 Optimal Small Cell Deployment Scheme 3.1 Single Cell 3.2 Multiple Cells 4 4 Simulation Results 4.1 Single Cell 5 5 Conclusion ReferencesResearch on Knowledge Mining Algorithm of Spacecraft Fault Diagnosis System Abstract 1 1 Introduction 2 2 Background 3 3 Introduction of Expert System 4 4 Requirement Analysis 5 5 Overall Design 5.1 Analog Telemetry Information Mining 5.2 Digital Telemetry Information Mining 6 6 Experimental Results 7 7 Conclusion ReferencesPerformance Analysis of SSK in AF Relay over Transmit Correlated Fading Channels 1 Introduction 2 System Model 3 BER Performance Analysis 4 Simulation Results 5 Conclusion ReferencesThe JSCC Algorithm Based on Unequal Error Protection for H.264 Abstract 1 Introduction 2 H.264 Data Segmentation 3 Design and Implementation of Unequal Error Protection Scheme 4 Performance Evaluation 4.1 Campare the Function Between UEP and EEP 4.2 Performance Analysis of UEP with Different Bit Rate 5 Conclusion Acknowledgements ReferencesMean-Field Power Allocation for UDN Abstract 1 1 Introduction 2 2 Problem Description of Dynamic Stochastic Game 3 3 Mean-Field Solution to the Problem 4 4 Simulation Results 5 5 Conclusion Acknowledgements ReferencesDesign of Gas Turbine State Data Acquisition Instrument Based on EEMD Abstract 1 1 Introduction 2 2 Hardware Design 2.1 Temperature Signal Measuring Circuit 2.2 Speed Signal Measuring Circuit 2.3 Vibration Sensor Protection Circuit 2.4 Data Transmission Circuit 3 3 Software Design 3.1 EEMD Algorithm 3.2 Vibration Analysis 4 4 Conclusions ReferencesCramér–Rao Bound Analysis for Joint Estimation of Target Position and Velocity in Hybrid Active and Passive Radar Networks Abstract 1 Introduction 2 Signal Model 2.1 LFM Signal Model in Active Radar Networks 2.2 FM Signal Model in FM-Based Passive Radar Networks 3 Joint Cramér–Rao Lower Bound 3.1 Non-coherent FIM for LFM-Based Active Radar Networks 3.2 Non-coherent FIM for FM-Based Passive Radar Networks 3.3 Non-coherent CRLB for Hybrid Radar Networks 4 Simulation Results and Analysis 5 Conclusion Acknowledgements ReferencesA Hinged Fiber Grating Sensor for Hull Roll and Pitch Motion Measurement Abstract 1 1 Introduction 2 2 Theory 2.1 FBG Basic Sensing Principle 2.2 Temperature Compensation Method of FBG 2.3 Arc Hinge Flexibility Theory 3 3 Sensor Structure 3.1 Structure Description 3.2 Ansys Analysis 4 4 Sensor Test Experiment 5 5 Conclusions Acknowledgements ReferencesNatural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER Abstract 1 Introduction 2 Related Work 2.1 Generating Candidate Connected Areas 2.2 Training Text Classifier 3 Experimental Results and Analysis 3.1 Data Sets and Evaluation Criteria 3.2 Experimental Results and Analysis 4 Conclusion Acknowledgements ReferencesCoverage Probability Analysis of D2D Communication Based on Stochastic Geometry Model Abstract 1 1 Introduction 2 2 System Model 3 3 Performance Analysis of Downlink 3.1 Coverage Probability of Cellular Links 3.2 Coverage probability of D2D links 4 4 Simulation Analysis 5 5 Conclusions Acknowledgements Appendix 1: Proof of Theorem 1 B: Proof of Theorem 2 A: Proof of Theorem 3 ReferencesStudy of Fault Pattern Recognition for Spacecraft Based on DTW Algorithm Abstract 1 1 Introduction 2 2 Principle of DTW Algorithm 3 3 Test Data Analysis Method Based on DTW Algorithms 3.1 Data Analysis Workflow 3.2 Analysis of Test Results 3.3 Threshold Determination Method 4 4 Fault Recognition Method Based on DTW Algorithms 4.1 Workflow of Fault Recognition System 4.2 Analysis of Test Results 5 5 System Performance Optimization 6 6 Conclusion ReferencesA Joint TDOA/AOA Three-Dimensional Localization Algorithm for Spacecraft Internal Abstract 1 1 Introduction 2 2 Localization Algorithm 3 3 Localization Scenario 4 4 Conclusion ReferencesA Study on Lunar Surface Environment Long-Term Unmanned Monitoring System by Using Wireless Sensor Network Abstract 1 1 Introduction 2 2 Design of Lunar Surface Environment Detection System 2.1 The Impact of the Lunar Environment 2.2 System Design 2.3 Energy Balance Routing Technology 3 3 Feasibility and Advantage Analysis 4 4 Concluding Remarks ReferencesA Study on Automatic Power Control Method Applied in Astronaut Extravehicular Activity Abstract 1 Introduction 2 Communication System of EVA 3 Automatic Power Control Method for the Backward Signal 3.1 Open-Loop Automatic Power Control 3.2 Closed-Loop Automatic Power Control 3.2.1 Outer-Loop Automatic Power Control 3.2.2 Inner-Loop Automatic Power Control 4 Conclusion ReferencesDesign of EVA Communications Method for Anti-multipath and Full-Range Coverage Abstract 1 Introduction 2 EVA Communications Method 2.1 Design of Antenna Array 2.2 DS-CDMA 2.3 Time Diversity 2.4 Space Diversity 3 Conclusion ReferencesHigh Accurate and Efficient Image Retrieval Method Using Semantics for Visual Indoor Positioning Abstract 1 1 Introduction 2 2 System Model 2.1 Visual Indoor Positioning System Overview 2.2 SCBIR Method Overview 3 3 Proposed Method 3.1 Semantic Segmentation Network Framework 3.2 Precise Semantic Segmentation 3.3 Efficient Image Retrieval 4 4 Implementation and Performance Analysis 4.1 Experiment Environment 4.2 Experiment Results 5 5 Conclusion Acknowledgements ReferencesMassive MIMO Channel Estimation via Generalized Approximate Message Passing 1 Introduction 2 System Model and Channel Characteristics 3 Parameters Learning Through Generalized Approximate Message Passing Based EM 3.1 EM-based Sparse Signal Learning 3.2 E-Step 3.3 GAMP for Posterior Statistics 3.4 M-Step 4 Simulations Results 5 Conclusion ReferencesStudy of Key Technological Performance Parameters of Carbon-Fiber Infrared Heating Cage Abstract 1 Introduction 2 Structural Design of Carbon-Fiber Heating Cage and Layout of Heat-Flow Meter Used in Testing 2.1 Structural Design of Carbon-Fiber Heating Cage 2.2 Layout of Heat-Flow Meter Used in Testing 3 Testing and Analysis of the Performance in a Thermal-Vacuum Environment 3.1 Test Preparation 3.2 Testing 3.2.1 Measurement of Heat-Flow Uniformity in Carbon-Fiber Infrared Cage 3.2.2 Testing the Heating Capacity of Carbon-Fiber Heating Cage 3.2.3 Comparison of Heating Capability with Traditional Nickel-Chromium Alloy Heating Cage 3.3 Test Results and Analysis of Heat-Flow Uniformity 3.3.1 Calculation of Heat Flux 3.3.2 Calculation of Heat-Flow Uniformity 3.4 Comparison and Analysis of Two Kinds of Heating Cage 3.4.1 Test Results and Analysis 4 Simulation Analysis of Heat-Flow Uniformity 4.1 Monte Carlo Method 4.2 Parameter Setting 4.3 Simulation Results 4.4 Comparison of Test and Simulation Analysis Data 5 Conclusion ReferencesResearch on Switching Power Supply Based on Soft Switching Technology Abstract 1 Introduction 2 The Soft Switching Realization of Switching Power Supply 2.1 Section Heading (“H1”) 2.2 TMS320F2812 Control Hardware Implementation 2.3 Software Implementation of TMS320F2812 Control 2.4 System Simulation Model 3 Simulation Results and Analysis 4 Conclusion Acknowledgements ReferencesGrid Adaptive DOA Estimation Method in Monostatic MIMO Radar Using Sparse Bayesian Learning 1 Introduction 2 System Model 2.1 MIMO Radar Signal Model 2.2 Traditional On-Grid Model 2.3 The Proposed Off-Grid Model 3 Grid Adaptive DOA Estimation Method 3.1 Sparse Bayesian Model 3.2 The Proposed GADE Algorithm 4 Numerical Simulations 4.1 Spatial Spectrum 4.2 Robustness Against Measurement Noise 4.3 Sensitivity to Initial Grid Granularity 5 Conclusion ReferencesGlobal Deep Feature Representation for Person Re-Identification Abstract 1 1 Introduction 2 2 The Proposed Method 3 3 Experiments 3.1 Datasets 3.2 Comparison of Four Backbone Networks on GDCN 3.3 Comparison with Prior Methods 4 4 Conclusion Acknowledgements ReferencesHybrid Precoding Based on Phase Extraction for Partially-Connected mmWave MIMO Systems 1 Introduction 2 System Model 2.1 System Model 2.2 Channel Model 3 Hybrid Precoding for the Partially-Connected Structure Based on Phase Extraction (HPP-PE) 3.1 Analog Precoder Design of HPP-PE 3.2 Digital Precoder Design of HPP-PE 3.3 Complexity Evaluation 4 Simulation Results 5 Conclusion ReferencesResearch on the Fusion of Warning Radar and Secondary Radar Intelligence Information Abstract 1 Introduction 2 Point Fusion of Warning Radar and Secondary Radar 2.1 Point-and-Shoot Fusion Structure of Warning Radar and Secondary Radar 2.2 Point Fusion Process and Algorithm 3 Track Fusion of Warning Radar and Secondary Radar 3.1 Track Fusion Structure of Warning Radar and Secondary Radar 3.2 Track Fusion Process and Algorithm 4 Simulation and Performance Analysis 5 Conclusion ReferencesAntenna Array Design for Directional Modulation 1 Introduction 2 Review of Planar Antenna Array Based Beamforming 3 DM Design for the Uniform Planar Antenna Array 4 Design Examples 5 Conclusions ReferencesCapturing the Sparsity for Massive MIMO Channel with Approximate Message Passing 1 Introduction 2 System Model 3 Learning Sparse Virtual Channel Model Parameters Through DL Training 3.1 Problem Formulation 3.2 Expectation Step 3.3 Deriving the Posterior Statistics with AMP 3.4 Maximization Step 4 Simulations Results 5 Conclusion ReferencesAn On-Line EMC Test System for Liquid Flow Meters Abstract 1 Introduction 2 Design of Compact Liquid Flowrate Standard Facility 2.1 Design of Surge Tank 2.2 Design of Water Tank 3 Analysis of Hydraulic Resistance 4 Experimental Research of on-Line EMC Test System ReferencesResearch on Kinematic Simulation for Space Mirrors Positioning 6DOF Robot Abstract 1 The Position Analysis of 6DOF Parallel Robot 1.1 6DOF Parallel Robot Model 1.2 Inverse Solution of the Position of 6DOF Parallel Robot 1.3 Positive Solution of the Position of 6DOF Parallel Robot 2 Simulation of 6DOF Parallel Robot Based on OpenGL 3 Simulation Results 4 Conclusion ReferencesA Dictionary Learning-Based Off-Grid DOA Estimation Method Using Khatri-Rao Product 1 Introduction 2 Covariance-Based Model for Sparse DOA Estimation 2.1 Array Model 2.2 Covariance-Based Sparse Representation Model 3 Dictionary Learning-Based Off-Grid DOA Estimation Method 4 Simulation and Analysis 5 Conclusion ReferencesRadar Adaptive Sidelobe Cancellation Technique Based on Spatial Filtering Abstract 1 Introduction 2 Radar Sidelobe Cancellation Technology 3 Adaptive Cancellation Algorithm 3.1 Least Mean Square Algorithm 3.2 Sampling Matrix Inversion Algorithm 3.3 Conjugate Gradient Algorithm 3.4 Normalized Least Mean Square Algorithm 4 Simulation and Performance Analysis 5 Conclusion ReferencesOn the Spectral Efficiency of Multiuser Massive MIMO with Zero-Forcing Precoding 1 Introduction 2 System Model 3 Spectral Efficiency Analysis of System 3.1 Spectral Efficiency Analysis in Ricean Fading 3.2 Exact Expression of the Spectral Efficiency 3.3 Tight Bounds on the Spectral Efficiency 4 Numerical Results 5 Conclusion ReferencesA Signal Sorting Algorithm Based on LOF De-Noised Clustering Abstract 1 1 Introduction 2 2 Data Standardization 3 3 Outlier Removal Algorithm 3.1 Isolated Point Removal Based on Euclidean Distance 4 4 K-Means Clustering Algorithm Based on Data Field 4.1 Data Field 4.2 Determination of the Initial Cluster Center 4.3 K-Means Algorithm 5 5 Simulation Analysis 6 6 Conclusion Acknowledgements ReferencesDesign of a Small-Angle Reflector for Shadowless Illumination Abstract 1 Introduction 2 Design and Simulation 3 Conclusion ReferencesAnti-interference Communication Algorithm Based on Wideband Spectrum Sensing Abstract 1 Introduction 2 System Model 3 Proposed Algorithm 3.1 Wideband Spectrum Sensing Algorithm Based on Compressed Sensing 3.2 Spectrum Decision Algorithm Based on Frequency Domain Entropy 4 Simulation Results 4.1 Chirp Interference Signal Sparse Representation 4.2 Signal Reconstruction Performance by Different Algorithms 4.3 Comparison of Detection Performance 5 Conclusions Acknowledgements ReferencesA Multi-task Dynamic Compressed Sensing Algorithm for Streaming Signals Eliminating Blocking Effects Abstract 1 Introduction 2 SMT-SBL Algorithm 3 Experimental Results 4 Summary ReferencesThunderstorm Recognition Algorithm Research Based on Simulated Airborne Weather Radar Reflectivity Volume Scan Data Abstract 1 1 Introduction 2 2 Airborne Weather Radar Scan Mode 3 3 Thunderstorm Recognition Algorithm 4 4 Thunderstorm Identification Case Analysis 5 5 Conclusion Acknowledgements ReferencesFPGA-Based Fall Detection System Abstract 1 Introduction 2 Over View of the System 3 Fall Detection Algorithm 3.1 Background Generation 3.2 Moving Object Segmentation 3.3 Fall Detection 4 Implementation on FPGA 5 Results and Discussions 5.1 Accuracy of System 5.2 Processing Frame Rate 5.3 System Alarm Time 6 Conclusions ReferencesArtificial Intelligence and Game Theory Based Security Strategies and Application Cases for Internet of Vehicles Abstract 1 Introduction 2 Literature Survey 2.1 Structure of IoV 2.2 Attack Classification in IoV 2.3 Countermeasures for IoV Security 2.4 Artificial Intelligence and Game Theory Based Security Strategies for IoV 2.5 Case Study of Artificial Intelligence and Game Theory Based Security Strategies for IoV 3 Conclusions Acknowledgements ReferencesThe Effect of Integration Stage on Multimodal Deep Learning in Genomic Studies 1 Introduction 2 Data 3 Methods 4 Experimental Results 5 Validation 6 Discussions and Conclusions ReferencesAn Advanced Aerospace High Precision Spread Spectrum Ranging System Technology Abstract 1 Introduction 2 Basic Principle of Pseudo-Code Ranging 3 Optimization Design of Pseudo Code Ranging System 3.1 Conventional Pseudo-Code Ranging System 3.2 Advanced High Precision Pseudo-Code Ranging System 3.2.1 System Structure 3.2.2 Frequency Flow 3.2.3 Error Analysis 4 Test Verification 5 Conclusion ReferencesWeight-Assignment Last-Position Elimination-Based Learning Automata Abstract 1 Introduction 2 Related Work 3 Proposed Learning Automata 4 Simulation Results 5 Conclusion Acknowledgements ReferencesNonlinear Multi-system Interactive Positioning Algorithms Abstract 1 Introduction 2 System Modeling 3 Nonlinear Multi-system Interactive Positioning Algorithm 3.1 Multiple System Interaction 3.2 Multiple System Parallel Filtering 3.3 System Probability Update 3.4 System Fusion Output 4 Analysis of Simulation Experiment 5 Conclusion Analysis ReferencesBandwidth Enhancement of Waveguide Slot Antenna Array for Satellite Communication Abstract 1 Introduction 2 Antenna Design and Fabrication 3 Results and Discussion 4 Conclusion ReferencesDesign of an Enhanced Turbulence Detection Process Considering Aircraft Response Abstract 1 Introduction 2 The Estimation of the Vertical Load Factor 3 Enhanced Turbulence Detection Process Based on Vertical Load Factor 3.1 Turbulence Model and Its Power Spectral Density Function 3.2 Predicting Vertical Load Factor 4 Numerical Examples 4.1 Analysis of Examples 4.2 Application Analysis 5 Conclusion Acknowledgements ReferencesRain-Drop Size Distribution Case Study in Chengdu Based on 2DVD Observations Abstract 1 Introduction 2 Instruments and Data 2.1 Instrument Introduction 2.2 Data Processing 3 Characteristics of Three Precipitation Raindrop Spectra 3.1 Raindrop Size Distribution 3.2 Total Particle Density Characteristics 3.3 Median Volume Diameter Feature 4 Analysis of Related Parameters 5 Conclusion Acknowledgements ReferencesAnalysis of the Influence on DPD with Memory Effect in Frequency Hopping Communication System Abstract 1 Introduction 2 Theory Analysis 2.1 Basic Theory of DPD 2.2 Basic Principle of Frequency Hopping Communication 2.3 The Influence of Frequency Hopping on DPD with Memory Effect 3 Experiment Analysis 3.1 Experiment Scheme 3.2 Generation of the Frequency Hopping Signal 3.3 Analysis of the Test Data 3.3.1 Analysis of the Test Data Before and After Single Hop 3.3.2 Analysis of the Whole Frequency Band 3.3.3 Analysis of the Time Domain Signal 4 Conclusion ReferencesFPGA-Based Implementation of Reconfigurable Floating-Point FIR Digital Filter Abstract 1 Introduction 2 Methods 2.1 Filtering Processing Module 2.2 Data Rearrangement Module 2.3 Overlap-Add Module 3 Result Analysis 4 Conclusion Acknowledgements ReferencesHigh Precision Spatiotemporal Datum Design Based on Ground Observation Position Abstract 1 1 Introduction 2 2 Definition 3 3 Calculation of the Observation Position of Celestial Bodies on the Ground Station 4 4 Simulation 5 5 Conclusion ReferencesStudy on Two Types of Sensor Antennas for an Intelligent Health Monitoring System Abstract 1 1 Introduction 2 2 Two Types of in-Body Sensor Antenna 3 3 In-Body Antenna Transmission Characteristic 4 4 Conclusion Acknowledgements ReferencesA Fiber Bragg Grating Acceleration Sensor for Measuring Bow Slamming Load Abstract 1 1 Introduction 2 2 Theory 2.1 Basic Fiber Grating Sensor Theory 2.2 Temperature Characteristic 2.3 Axial Strain 3 3 Sensor Structure Analysis 3.1 The Structure of the Sensor 3.2 Working Principle of Sensor 4 4 Vibration Testing of Sensors 5 5 Conclusion Acknowledgements ReferencesImproving Indoor Random Position Device-Free People Recognition Resolution Using the Composite Method of WiFi and Chirp Abstract 1 Introduction 2 Composite Preamble Scheme 3 Indoor Device-Free People Recognition 3.1 Scenario and Calculation Setting 3.2 Signal Setting 3.3 Result of the Experiment 4 Conclusions Acknowledgements ReferencesOptimal Design of an S-Band Low Noise Amplifier Abstract 1 Introduction 2 Design and Analysis 2.1 Performance Indexes 2.2 Design Proposal 3 Realization and Measurement 4 Conclusion Acknowledgements ReferencesA Triangular Centroid Location Method Based on Kalman Filter Abstract 1 Introduction 2 RSSI 2.1 RSSI and 802.11 Protocol 2.2 Measurement Process and Ranging Principle of RSSI Value 3 Location Algorithm 3.1 Principle of Improved Triangular Centroid Algorithm 3.2 Algorithm Implementation Steps 4 Kalman Filter 4.1 Principle of Kalman Filter 4.2 Algorithm Implement Steps 5 Experimental Results and Analysis 6 Conclusion Acknowledgements ReferencesResearch on Spatial Network Routing Model Based on Price Game Abstract 1 1 Introduction 2 2 Definition of System Model 2.1 Spatial Network Topology Structure 2.2 Routing Node Storage Resource Allocation Scheme 2.3 Price Game Model 2.4 “Selfish” Node Penalty Mechanism 3 3 Routing Model Design 3.1 Routing Game Model Equilibrium Analysis 3.2 Routing Algorithm Design 4 4 Simulation Analysis 4.1 Transmission Success Rates Analysis 4.2 Transmission Delay Analysis 4.3 Network Spending Ratio Analysis 5 5 Conclusion ReferencesThe TDOA and FDOA Algorithm of Communication Signal Based on Fine Classification and Combination Abstract 1 Introduction 2 The TDOA and FDOA Estimation Accuracy of Communication Signal 3 The Fine Classification and Combination Estimation Algorithm 3.1 The TDOA and FDOA Estimation Model 3.2 The Fine Classification and Combination Estimation Algorithm 4 Simulation and Analysis Results 5 Summary ReferencesAn Adaptive DFT-Based Channel Estimation Method for MIMO-OFDM Abstract 1 1 Introduction 2 2 DFT Channel Estimation Principle DFT Channel Estimation Principle 2.1 LS Channel Estimation 2.2 DFT-Based LS Channel Estimation 3 3 Proposed Methods 4 4 Simulation Results and Analysis ReferencesA Novel Gradient L0-Norm Regularization Image Restoration Method Based on Non-local Total Variation Abstract 1 Introduction 2 Models and Methods 2.1 The Non-local TV Model 2.2 Image Division Using L0-Norm of Image Gradient 2.3 The Proposed Model 3 Experimental Results 4 Conclusions Acknowledgements ReferencesStudy on Interference from 5G System to Earth Exploration Satellite Service System in High Frequency Abstract 1 Introduction 2 System Modeling and Analytic Procedure 2.1 The Interference Model 2.2 Propagation Model 2.3 Method of Deterministic Calculation 3 Simulation Experiment and Result Analysis 3.1 Parameters of Simulation Experiment 3.1.1 Parameters of 5G Base Station 3.1.2 Parameters of Earth Station 3.2 Simulation 4 Conclusions Acknowledgements ReferencesSparse Planar Antenna Array Design for Directional Modulation 1 Introduction 2 Review of Planar Antenna Array Based Beamforming 3 Sparse Planar Antenna Array Design for DM 4 Design Examples 5 Conclusions ReferencesResearch on the Linear Interpolation of Equal-Interval Fractional Delay Filter Abstract 1 Introduction 2 Ideal Digital Fractional Delay Filter 3 Design of the Equal-Interval Delay Filter 3.1 Ideal Equal-Interval FD Filter 3.2 Equal-Interval FD Filter 3.3 Interpolation of the Equal-Interval Delay Filter 4 Simulation and Verification 4.1 Simulation of Equal-Interval Delay Filter 4.2 Simulation of the EIFD Filter Interpolation Algorithm 5 Conclusion ReferencesSingle-Channel Grayscale Processing Algorithm for Transmission Tissue Images Based on Heterogeneity Detection Abstract 1 1 Introduction 2 2 Grayscale Processing and Experimental Preparation 3 3 Experiments 3.1 Experimental Device 3.2 Experimental Process 4 4 Analysis of Experimental Results 5 5 Conclusions Acknowledgements ReferencesHandwriting Numerals Recognition Using Convolutional Neural Network Implemented on NVIDIA’s Jetson Nano Abstract 1 Introduction 2 Related Work 3 Handwritten Hindi Digits Recognition 4 Results and Discussion ReferencesImplementation of Image Recognition on Embedded Systems Abstract 1 Introduction 2 Technical Background 2.1 ImageNet Dataset 2.2 Jetson Nano Embedded Development Board 2.3 Convolutional Neural Networks 3 Method 3.1 Convolutional Neural Network Classification Model 3.2 Convolutional Neural Network Model Construction 4 Result 5 Conclusion ReferencesA Precise 3-D Wireless Localization Technique Using Smart Antenna Abstract 1 1 Introduction 2 2 3D Estimation of an Object’s Location by AOA Measurement 3 3 The Position Estimation 4 4 Consider the Influence of Distance 5 5 Simulations and Results Analysis 6 6 Conclusion Acknowledgements ReferencesA Two-Phase Fault Diagnosis Algorithm Based on Convolutional Neural Network for Heterogeneous Wireless Abstract 1 Introduction 2 CNN-Based Diagnosis Model 2.1 The First State: Monitoring Phase 2.1.1 Feature Selection 2.1.2 Diagnosis of Abnormal Symptoms 2.2 Second Stage: Diagnosis Stage 2.2.1 Base Station Selection 2.2.2 Fault Diagnosis Model 3 Simulation and Performance Evaluation 3.1 Simulation Environment 3.2 Performance Analysis 4 Conclusions Acknowledgements ReferencesA Wireless Power Transfer System with Switching Circuit of Power Grid and Solar Energy Abstract 1 Introduction 2 Wireless Power Charging System 3 Electricity Grid Charging Mode 4 Solar Energy Charging Mode 5 Charging Modes Switching Circuit 6 Experiments 7 Conclusion Acknowledgements ReferencesA Fiber Bragg Grating Stress Sensor for Hull Local Strength Measurement Abstract 1 1 Introduction 2 2 The Basic Principle of Fiber Grating Sensor 3 3 Short Base Stress Sensor Structure 4 4 Sensor Experiment Process 5 5 Conclusion Acknowledgements ReferencesDirect Wave Parameters Estimation of Passive Bistatic Radar Based on Uncooperative Phased Array Radar 1 Introduction 2 Signal Model 3 Proposed Method 4 Experimental Results 5 Conclusion ReferencesNoncooperative Radar Illuminator Based Bistatic Receiving System Abstract 1 1 Introduction 2 2 Framework of the Bistatic Receiving System 3 3 Experimental Scheme, Hardware Setup and Results 3.1 Deinterleaving of Direct Path Signals 3.2 Coherent Processing Results 4 4 Conclusions ReferencesResearch on Simulation Technology for Remote Sensing Image Quality Abstract 1 Introduction 2 System Construction Ideas 3 System Scheme and Composition 3.1 Geometric Simulation Subsystem 3.2 Radiation Simulation Subsystem 3.3 Compression and Decompression Subsystem 3.4 Ground Processing and Subjective Evaluation Subsystem 4 Applications 5 Subsequent Improvement and Consideration 6 Conclusion ReferencesDistributed Measurement of Micro-vibration and Analysis of the Influence on Imaging Quality Abstract 1 Introduction 2 Requirements of Micro-vibration Based on Imaging Quality 2.1 Division of Micro-vibration Frequency 2.2 Characteristics of Optical Remote Sensing Camera Imaging 2.3 Calculation of the Effect of Micro-vibration on Imaging Quality 2.4 Requirements of Imaging Quality for Micro-vibration 3 Micro-vibration Measurement Based on Optical System 4 Results and Analysis of Micro-vibration Test 4.1 Results of Micro-vibration Measurement 4.2 Effect of Micro-vibration on Image Quality 5 Conclusion ReferencesAnalysis and Verification of the Effect of Space Debris on the Output Power Decline of Solar Array Abstract 1 1 Introduction 2 2 Analysis of Output Power Decline Process of the Solar Array 2.1 Method for Calculating Output Power of Solar Array 2.2 Shunting Principle of Solar Array 2.3 Analysis of the Decline Process of Output Power 2.4 Summary 3 3 Simulation Validation 3.1 Attitude Variation Mechanism of Space Debris Impact Disturbing 3.2 Mechanics Analysis of Space Debris Impact Disturbance 4 4 Impact Effect of Space Debris on the Solar Array 5 5 Conclusions ReferencesA New Nonlinear Method for Calculating the Error of Passive Location Abstract 1 Introduction 2 Analysis of GDOP Based on Linear Method 3 GDOP Based on UT 4 Conclusions Acknowledgements ReferencesA Static Method for Stack Overflow Detection Based on SPARC V8 Architecture Abstract 1 1 Introduction 2 2 Introduction of SPARC V8 2.1 Register Windows 2.2 Stack Management 3 3 Static Method of Stack Overflow Detection 3.1 Principle Introduction 3.2 Analysis of Function Stack Usage 3.3 Analysis of Function Call Relationships 3.4 Implementation of Stack Overflow Detection Algorithm 4 4 Case Verification and Result Analysis 5 5 Conclusion ReferencesEnhanced Double Threshold Based Energy Detection Abstract 1 1 Introduction 2 2 System Model 2.1 General Model 2.2 Energy Detection (Single Threshold Based Detection) 2.3 Double Threshold 3 3 Proposed Algorithm 4 4 Simulations and Results 5 5 Conclusion Acknowledgements ReferencesSelf-generating Topology Coloring Scheduling for Interference Mitigation in Wireless Body Area Networks Abstract 1 Introduction 2 The Inter-WBAN Interference and Resource Scheduling Scheme 2.1 Inter-WBAN Interference 2.2 Inter-WBAN Interference Resource Scheduling Scheme 3 Self-generating Topology Colouring Scheduling 4 Experimental Analysis 5 Conclusion ReferencesSmart Parking and Recommendation System Under Fog Calculation Abstract 1 Introduction 2 Fog Computing 3 Fog Computing Architecture of Smart Parking System 3.1 Hierarchical Framework of Cloud Computing for Smart Parking Systems 3.2 Cloud Computing Architecture for Smart Parking Systems 3.3 Key Technologies of Fog Calculation in Smart Parking System 4 Conclusions ReferencesSpeech Synthesis Method Based on Tacotron + WaveNet Abstract 1 Introduction 2 Speech Synthesis Model Based on Tacotron 2.1 CBHG Module 2.2 Encoding-Decoding Model 2.3 Attention Mechanism 2.4 Griffin-Lim Algorithm 3 Speech Synthesis Model Based on WaveNet 3.1 Feature Selection 3.2 WaveNet Network Architecture 4 Experiment and Results 4.1 Experimental Data 4.2 Experimental Process 4.3 Experimental Results 4.4 Contrast Experiment ReferencesA Novel Spatial Domain Based Steganography Scheme Against Digital Image Compression Abstract 1 Introduction 2 Proposed Method 2.1 Embedding Procedure 2.2 Extracting Procedure 2.3 Cover Image Recovery Procedure 3 Experimental Results 4 Conclusion Acknowledgements ReferencesLosen: An Accurate Indoor Localization System by Integrating CSI of Wireless Signal and MEMS Sensors 1 Introduction 2 Related Work 3 System Description 3.1 Locating the Target Based on AoA 3.2 Step Detection and Velocity Estimation 3.3 Heading Estimation 3.4 AOA/PDR Integration 4 Experimental Results 5 Conclusion ReferencesA Direct Target Recognition Algorithm for Low-Resolution Radar with Unbalanced Samples Abstract 1 Introduction 2 Direct Recognition Algorithm of Low-Resolution Radar Target Based on Focal Loss Function 2.1 CNN 2.2 Focal Loss Function 2.3 Direct Recognition Algorithm of Low-Resolution Radar Target Based on Focal Loss Function 2.3.1 The Structure of CNN in This Paper 2.3.2 Algorithm Steps 3 Experimental Results and Analysis 3.1 Experimental Data Set 3.2 Focal Loss Function Parameter 3.3 The Influence of Different Loss Functions on the Recognition Effect 3.4 Recognition Effect of Low-Resolution Radar Target Direct Recognition Algorithm Based on the Focal Loss Function 4 Conclusion ReferencesDFT-Spread Based PAPR Reduction of OFDM for Short Reach Communication Systems Abstract 1 Introduction 2 Theoretical Analysis 3 Simulation Setup 4 Results and Discussions 5 Conclusion Acknowledgements ReferencesUnderdetermined Mixed Matrix Estimation of Single Source Point Detection Based on Noise Threshold Eigenvalue Decomposition Abstract 1 1 Introduction 2 2 Underdetermined Mixed Signal Model 3 3 Algorithm Principle 3.1 Traditional Algorithm Principle 3.2 Improved Algorithm in This Paper 4 4 Simulation 4.1 Evaluation Criteria 4.2 Experimental Verification 5 5 Conclusion ReferencesOptimization of APTEEN Routing Protocol for Wireless Sensor Networks Based on Genetic Algorithm Abstract 1 Introduction 2 Related Work 2.1 Energy Consumption Model 2.2 Genetic Algorithm 2.3 Density Adaptive Algorithm 3 GA-APTEEN Optimization Agreement 3.1 Cluster Heads Optimization 3.2 Genetic Optimization Algorithm 3.3 Select the Cluster Heads for the Second Time 3.4 Node Sleep and Clustering Mechanism 3.4.1 Node Sleep Mechanism 3.4.2 Node Clustering Optimization Mechanism 4 Simulation Analysis 5 Conclusion Acknowledgements ReferencesOptimization of APTEEN Routing Protocol in Wireless Sensor Networks Based on Particle Swarm Optimization Abstract 1 Introduction 2 Particle Swarm Optimization and Wireless Communication Model 2.1 Particle Swarm Optimization 2.2 Wireless Communication Model 3 Particle Swarm Optimization Cluster Head Selection Algorithm 3.1 Network Model 3.2 Energy Position Equalization-Adaptive Threshold-Sensitive Energy Efficient Sensor Network Protocol (EPE-APTEEN) 3.2.1 Pre-clustered 3.2.2 Optimize Cluster Head 4 Simulation and Analysis 5 Conclusion Acknowledgements ReferencesResearch Status of Wireless Power Transmission Technology Abstract 1 Introduction 2 Magnetically Coupled Wireless Power Transfer Technology 2.1 Fundamentals 2.2 Key Technology 2.3 Application Status 3 Laser Wireless Energy Transfer Technology 3.1 Fundamentals 3.2 Key Technology 3.3 Application Status 4 Microwave Wireless Energy Transmission Technology 4.1 Fundamentals 4.2 Key Technology 4.3 Application Status 5 Summary ReferencesFlexible Sparse Representation Based Inverse Synthetic Aperture Radar Imaging 1 Introduction 2 Sparse Representation Based ISAR Imaging 2.1 Basic Geometry of ISAR and the Recieved Radar Signal 2.2 Sparse Probing Frequencies 2.3 Discussions 3 Sparse Bayesian Learning 3.1 Probabilistic Models 3.2 Unknown Variable Inference 4 Simulation Results 5 Conclusion ReferencesLocalization Schemes for 2-D Molecular Communication via Diffusion 1 Introduction 2 System Model 3 Localization Schemes 4 Numerical Results 5 Conclusions ReferencesResearch on Support Vector Machine in Estimating Source Number Abstract 1 1 Introduction 2 2 Signal Number Estimation Based on Support Vector Machine 2.1 Feature Extraction of Source Number Estimation Based on SVM 3 3 Implementation of Source Number Estimation Algorithm 3.1 Establishment and Optimization of Classifier Parameters 3.2 Simulation Experiment 4 4 Conclusions Acknowledgements ReferencesWireless Electricity Transmission Design of Unmanned Aerial Vehicle Charging Systems Abstract 1 Introduction 2 Results and Discussion 2.1 Magnetic Coupling Resonance Method 2.2 Electromagnetic Induction Coupling Method 3 Conclusion Acknowledgements ReferencesAn ITD-Based Method for Individual Recognition of Secondary Radar Radiation Source Abstract 1 1 Introduction 2 2 Secondary Radar Source Signal Model 3 3 The Core Idea of the ITD Approach 4 4 ITD Method for Individual Recognition of Secondary Radar Radiation Source 4.1 Methods the Thought 4.2 The Basic Principle of Fast Sample Entropy Algorithm 5 5 Performance Simulation Analysis 5.1 Experimental Data of Secondary Radar Radiation Source 5.2 Classification Recognition Performance Analysis 6 6 Conclusion ReferencesGaussian Mixture Model Based Multi-region Blood Vessel Segmentation Method Abstract 1 1 Introduction 2 2 Blood Vessel Segmentation Method 2.1 NSCT Transform 2.2 Gaussian Mixture Model Based Multi-region Blood Vessel Segmentation Method 2.2.1 Gaussian Mixture Model 2.2.2 Multi-region Segmentation 2.3 Adaptive Filling Filtering 3 3 Experimental Evaluation 3.1 Results Description 3.2 Parameter Description 3.2.1 Selection of High Frequency Parameters and Low Frequency Parameters 3.2.2 Selection of Optimal Threshold 4 4 Conclusion ReferencesResearch on the Enhancement of VANET Coverage Based on UAV 1 Introduction 2 System Model 2.1 RSU Deployment in Non-congested State 2.2 RSU and UAV Joint Deployment in Congested State 3 Proposed Approach 3.1 Greedy Algorithm Applied to RSU Deployment in Non-congested State 3.2 Joint Deployment in Congested State Based on Markov 4 Simulation Results and Analysis 4.1 RSU Deployment in Non-congested State 4.2 RSU and UAV Joint Deployment in Congested State 5 Conclusion ReferencesResearch on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos Abstract 1 1 Introduction 2 2 Wavelet Transform 3 3 Chaotic System 4 4 Encryption Process 5 5 Simulation Results and Analysis 5.1 Histogram Analysis 5.2 Information Entropy Analysis 5.3 Correlation Analysis 5.4 Key Sensitivity Analysis 5.5 Key Space Analysis 5.6 Noise Attack Analysis 6 6 Conclusion Acknowlegments ReferencesA Design of Satellite Telemetry Acquisition System Abstract 1 1 Introduction 2 2 Design and Implementation 2.1 Composition of Satellite Telemetry Acquisition System 2.2 TM Space Data Link Protocol Telemetry Format 2.2.1 Space Pack 2.2.2 Virtual Channel Data Unit (VCDU) 2.2.3 Channel Access Data Unit (CADU) 3 3 Design Example 3.1 Space Package Organization 3.2 Spatial Packet Scheduling 3.3 Virtual Channel Data Unit Organization 3.4 Virtual Channel Scheduling 4 4 Conclusion ReferencesFingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection Abstract 1 1 Introduction 2 2 Fingerprint Feature Recognition Algorithm 2.1 Feature Selection Algorithm Based on Mutual Information 2.2 SVM Parameter Optimization Based on Quadratic Grid Search Algorithm 3 3 Simulation Experiment and Analysis 3.1 Experimental Data 3.2 Feature Selection Experiment 3.2.1 Experiment 1: Analysis of Feature Selection Algorithms Under Conventional Features 3.2.2 Experiment 2: Analysis of Feature Selection Algorithms Under Higher-Order Spectral Feature SIB 3.3 SVM Parameter Optimization Experiment 4 4 Conclusion ReferencesIntegrated Design of High Speed Uplink and Emergency Telemetry and Control for LEO Satellite Abstract 1 1 Preface 2 2 System Design 2.1 Systematic Composition 2.2 Data Stream Design 2.3 Link Design 2.3.1 Synchronization and Channel Coding 2.3.2 Data Link Protocol 2.3.3 Emergency Measurement and Control Link Establishment 3 3 Design Examples 3.1 System Design 3.2 System Design 4 4 Conclusion ReferencesImaging Correction Based on AIS for Moving Vessels in Spaceborne SAR Images Abstract 1 1 Introduction 2 2 Performance of Moving Vessels in SAR Images 3 3 Parameter Settings of the Simulation and Results Analysis 4 4 Moving Targets Scene Simulation and Association Analysis Combined with AIS Information 5 5 Conclusion ReferencesResearch on Flying Catkins Detection and Removal in Target Video Abstract 1 1 Introduction 2 2 Analysis of Flying Catkins Characteristics 2.1 Physical Characteristics of Flying Catkins 2.2 Brightness Characteristics of Flying Catkins 2.3 Time Domain Characteristics of Flying Catkins 3 3 Flying Catkins Detection and Removal 3.1 Frame Difference Method Raindrop Removal Algorithm 3.2 A Flying Catkins Removal Algorithm Based on Time Domain and Brightness Characteristics 3.3 Analysis of Results 4 4 Summary and Prospect ReferencesRobust Context-Aware Tracking with Temporal Regularization 1 Introduction 2 Related Works 3 Context-Aware Correlation Filter Framework 4 Proposed Method 4.1 Rich Context Aware Tracker 4.2 Multi-channel Features 5 Experiment 5.1 Quantitative Analysis 5.2 Qualitative Evaluation 6 Conclusion ReferencesResearch on Motor Speed Estimation Method Based on Electric Vehicle Abstract 1 1 Introduction 2 2 Completely Dependent on the Physical Parameters of the Motor and the Electromagnetic Equation 3 3 Partially Dependent on the Physical Parameters of the Motor and the Electromagnetic Equation 3.1 Model Reference Adaptive System 3.2 Luenberger Observer 3.3 Extended Kalman Filter (EKF) 3.4 Sliding Mode Observer (SMO) 4 4 Independent of the Physical Parameters of the Motor and the Electromagnetic Equation 4.1 External High Frequency Signal Injection 4.2 High Frequency Signal Excitation Method Based on PWM Modulation 4.3 Artificial Intelligent Algorithm 5 5 Conclusion Acknowledgements ReferencesA Novel Virtual Cell Power Allocation and Interference Merging Algorithm in UDN 1 Introduction 2 System Model 3 Proposed Power Allocation and Interference Merging Algorithm 4 Simulation Results and Analysis 5 Conclusion ReferencesDevice-Free Sensing for Gesture Recognition by Wi-Fi Communication Signal Based on Auto-encoder/decoder Neural Network 1 Introduction 2 Experimental Setup and Data Collection 3 Methodology 3.1 Data Preprocessing 3.2 Higher-Order Cumulant Feature for Encoding 3.3 Auto-encoder/decoder Deep Neural Network 4 Experimental Results and Discussion 5 Conclusion ReferencesDetection of Sleep Apnea Based on Cardiopulmonary Coupling 1 Introduction 2 Feature Extraction 2.1 Heart Rate Variability Feature Extraction 2.2 Feature Extraction of Cardiopulmonary Coupling 3 OSA Classification Model 4 Result Analysis 5 Conclusion ReferencesStudy on a Space-Air-Ground Integrated Data Link Networks Architecture Abstract 1 Introduction 2 System Architecture 2.1 System Composition 2.2 System Functionality 3 Information Flow Process 4 Protocol Structure 5 Conclusion ReferencesSimilar Cluster Based Continuous Bag-of-Words for Word Vector Training Abstract 1 Introduction 2 Related Work 2.1 Continuous Bag-of-Words 2.2 Softmax Regression 3 Proposed Method 4 Results 4.1 Dataset 4.2 Result of Word Vectors 4.3 Result of Text Similarity Comparison 5 Conclusion Acknowledgements ReferencesResearch on Integrated Waveform of FDA Radar and Communication Based on Linear Frequency Offsets Abstract 1 1 Introduction 2 2 Integration of FDA Radar and Communication 2.1 Application Background 2.2 A Modulated Signal Loaded with Communications 2.3 MUSIC Algorithm for Multi-target Positioning 2.4 Analysis of Communication Performance 3 3 Conclusion Acknowledgements ReferencesResearch on Parameter Configuration of Deep Neural Network Applied on Speech Enhancement Abstract 1 Introduction 2 Speech Enhancement Based on DNN 3 Common Influencing Parameter 4 Experiments and Analysis 4.1 Experiments and Results 4.2 Analysis 5 Conclusion Acknowledgements ReferencesMid-Infrared Characteristic Analysis of Stability Index of Vehicle Gasoline Abstract 1 1 Introduction 2 2 Experiments 2.1 Collection of Experimental Samples 2.2 Testing Program 3 3 Results and Discussion 3.1 Correlation Analysis of Gasoline Stability Index and Infrared Spectrum During Storage 3.2 Establishment of Gasoline Quality Decay Model 4 4 Conclusion ReferencesApplication of Mid-Infrared Characteristic Analysis Technology in Gasoline Quality Control Abstract 1 Introduction 2 Experiments 2.1 Collection of Experimental Samples 2.2 Testing Program 3 Results and Discussion 3.1 Middle Infrared Spectrum Analysis of Hydrocarbon Compounds in Automotive Gasoline 3.2 Analysis of Infrared Spectrum Characteristics of Blending Components of Automotive Gasoline 3.3 Infrared Spectroscopy Analysis of Functional Group Decay in Gasoline Storage 4 Conclusion ReferencesA Generalized Sampling Based Method for Digital Predistortion of RF Power Amplifiers Abstract 1 Introduction 2 Design for Baseband Predistortion 2.1 Basic Principle and Structure of Baseband Predistortion 2.2 Undersampling 2.3 System Construction 3 Simulation and Measurement Results 4 Conclusion ReferencesOptimum Design of Intersatellite Link Based on STK 1 Introduction 2 Constellation Design 2.1 The Constellation Configuration 2.2 Visibility Analysis 3 Link Design 3.1 Domestic Satellite and Overseas Satellite 3.2 Analysis of Two Types of Links 3.3 Time Slot Design 3.4 Time-Invariant Link Planning 3.5 Time-Varying Link Planning 4 Simulation Analysis 4.1 Broadcast Authentication 4.2 Unicast Validation 5 Conclusion ReferencesIntegrated Detection and Tracking in Asynchronous Moving Radar Network Abstract 1 Introduction 2 System Model 2.1 Dynamic Model 2.2 Detection Model 2.3 Measurement Model 3 Integrated of Detection and Tracking 4 Simulation Result 5 Conclusion Acknowledgements ReferencesFault-Tolerant Decompression Method of Compressed Chinese Text Files Abstract 1 Introduction 2 Chinese Character Encoding Adaptation 3 Chinese Language Model 4 Performance Analysis 4.1 Decompression Success Rate 4.2 Normalized Edit Distance 5 Conclusion ReferencesClassification of Human Motion Status Using UWB Radar Based on Decision Tree Algorithm Abstract 1 Introduction 2 Measurement of Different Human Motion Status 2.1 The Composition of UWB Radar System 2.2 Experimental Scenario 3 Data Processing and Classification Algorithm 3.1 Data Processing 3.1.1 Background Subtraction 3.1.2 Feature Extraction 3.2 Decision Tree Classification Algorithm 4 Experimental Results 5 Conclusion ReferencesA Sub-aperture Division Method for FMCW CSAR Imaging Abstract 1 1 Introduction 2 2 FMCW CSAR Imaging Geometry and Echo Model 3 3 Analysis of Sub-aperture Selection 4 4 Simulation Analysis Based on Sub-aperture Algorithm 5 5 Conclusion Acknowledgements ReferencesAn Experimental Study of Sea Target Detection of Passive Bistatic Radar Based on Non-cooperative Radar Illuminators Abstract 1 Introduction 2 Section Heading Signal Processing Method for Passive Bistatic Radar Using Non-cooperative Pulse Radar 3 Experimental Bistatic Radar Setup for Sea Target Detection 4 Experiment Results 5 Conclusions ReferencesDesign of a Quasi-Real-Time Communication System for LEO Satellites Using Beidou Short-Message Service Abstract 1 Introduction 2 Interface Characteristics of BDS 3 Communication System Design 3.1 System Description 3.2 Working Process 4 Simulation Results and Analysis 4.1 Test Scenarios 4.2 Orbit Intervals 4.3 Communication Delays 5 Conclusion ReferencesA Physically Decoupled Onboard Control Plane for Software Defined LEO Constellation Network 1 Introduction 2 The Architecture of the Physically Decoupled Onborad SDN 2.1 CDMA Based RF Communications System for Control Plane 2.2 System Model 2.3 Mechanism 3 Controller Placement Problem 3.1 Performance Metrics 3.2 Flow Set Generation 4 Simulate Results 4.1 Simulate Scenario 4.2 Average Flow Setup Time 4.3 Average Failure Recovery Time 5 Conclusion ReferencesA Dynamic Programming Based TBD Algorithm for Near Space Targets Under Range Ambiguity Abstract 1 1 Introduction 2 2 System Model and Problem Description 2.1 System Dynamics Model 2.2 System Observation Model 3 3 Proposed Algorithm Introduction 3.1 Primary Threshold 3.2 Improved DP-TBD in Time-Range Domain 3.3 Ambiguity Resolution Procedure 4 4 Simulations and Discussion 5 5 Conclusions ReferencesResearch and Design of Home Care System of Internet of Things Based on Wireless Network Abstract 1 1 Introduction 2 2 Structure of Home Care System of IoT Based on CC3200 2.1 The Structure of the Overall Design Scheme 2.2 Design of Acquisition Terminal Node 3 3 The Design of the Specific Scheme of the System 3.1 Temperature Acquisition Scheme 3.2 Blood Pressure Collection Scheme 3.3 Blood Oxygen Collection Scheme 4 4 Software Design 5 5 Conclusion Acknowledgements ReferencesDesign of Wind Pendulum Control System Based on STM32F407 Abstract 1 Introduction 2 Analysis of Motion Model 2.1 Simple Pendulum Motion 2.2 Conical Pendulum Motion 2.3 Delay Analysis of Axial Flow Fan 3 Design and Implementation 3.1 Design of Hardware System 3.2 Design of Software System 3.2.1 PID Algorithm 3.2.2 PWM Output 3.2.3 MPU6050 Drive Function 3.2.4 Main Function 4 Function Implement and Test Result Analysis 4.1 Swing-Up 4.2 Stop-Swing 4.3 Drawing Line Segment of Specified Length 4.4 Drawing Line Segment of Specified Deflection Angle 4.5 Drawing a Circle of Specified Radius 4.6 Performance with External Interference 5 Conclusion Acknowledgements ReferencesA High-Speed Parallel Accessing Scheduler of Space-Borne Nand Flash Storage System Abstract 1 Introduction 2 Parallel Architecture of Multi-channel Flash Storage System 2.1 Traditional Parallel Architecture for Multi-channel Flash Storage System 2.2 An Optimized Accessing Scheduler for Multi-channel Nand Flash Storage System 3 Experiment Results 3.1 Flash Throughput Rate in Various Parallel Level 3.2 Flash Write Operation Speed Related with Input Data Rate 3.3 Compare with Previous Methods 4 Conclusion ReferencesTwo Dimensional Joint ISAR Imaging Algorithm Based on Matrix Completion Abstract 1 1 Introduction 2 2 Joint ISAR Imaging Model 2.1 Signal Model 2.2 Imaging Model 3 3 Algorithm of Joint ISAR Imaging 4 4 Experiments and Discussion ReferencesThe Satellite GPS Antenna In-Orbit Phase Center Calibration Method Abstract 1 1 Introduction 2 2 Background Information 3 3 Calibration Method 3.1 Antenna Phase Center Modeling 3.2 Processing Strategy 4 4 Results and Discussion 5 5 Conclusion ReferencesMigrating Target Detection Under Spiky Clutter Background Abstract 1 1 Introduction 2 2 Signal and Clutter Model 2.1 Signal Model 2.2 Clutter Model 3 3 Non-iterative Migrating Targets Detector 4 4 Performance Evaluation 5 5 Conclusion Acknowledgements ReferencesA Novel Range Super-Resolution Algorithm for UAV Swarm Target Based on LFMCW Radar 1 Introduction 2 System Model 3 Proposed Algorithm 4 Experiment 4.1 Swarm Target Simulation 4.2 Real Data Experiment 5 Conclusion ReferencesAn Improved PDR/WiFi Integration Method for Indoor Pedestrian Localization Abstract 1 Introduction 2 Approach 2.1 Pedestrian Dead Reckoning Equation and Weighted K-Nearest Neighbor Algorithm 2.2 Improved PDR/WiFi Integration Method 3 Experiments and Results 3.1 Experimental Setup 3.2 Localization Experiments 4 Conclusions Acknowledgements ReferencesAn Adaptive Radar Resource Scheduling Algorithm for ISAR Imaging Based on Step-Frequency Chirp Signal Optimization Abstract 1 1 Introduction 2 2 Prior Knowledge 3 3 ISAR-Imaging-Considered Task Scheduling Algorithm with Two Dimensions 4 4 Simulations 5 5 Conclusion Acknowledgements ReferencesA Task-Dependent Flight Plan Conflict Risk Assessment Method for General Aviation Operation Airspace Abstract 1 Introduction 2 Task-Dependent Flight Plan and Conflict Risk Assessment 2.1 Task-Dependent Flight Plan 2.2 Conflict Risk Assessment 3 Simulations 3.1 Simulation Scenario 3.2 Results and Discussion 3.2.1 Conflict Risk Assessment 3.2.2 Conflict Risk of Different Uncertainty Levels 4 Conclusion Acknowledgements ReferencesA Uniform Model for Conflict Prediction and Airspace Safety Assessment for Free Flight Abstract 1 Introduction 2 The Uniform Model 2.1 The Electrostatic Model 2.2 The Velocity Potential 3 Safety Assessment 4 Conclusion Acknowledgements ReferencesOptimization of Power Allocation for Full Duplex Relay-Assisted D2D Communication Underlaying Wireless Cellular Networks 1 Introduction 2 System Model 3 Outage Analysis 3.1 Boundary Condition A 3.2 Boundary Condition B 3.3 Boundary Condition C 4 Numerical and Simulation Results 5 Conclusion ReferencesScene Text Recognition Based on Deep Learning Abstract 1 1 Introduction 2 2 Background Knowledge 2.1 Scene Text Recognition Based on Deep Learning Method 3 3 Scene Text Recognition 3.1 Improved Sequence Recognition Algorithm 3.1.1 Image Pre-processing 3.1.2 Feature Extraction 3.1.3 Processing of Context Information 3.1.4 Transcription 4 4 Experiment and Analysis 4.1 Data Sets and Evaluation Criteria 4.2 Results and Analysis 5 5 Conclusion Acknowledgements ReferencesSpectrum Sensing Algorithm Based on Twin Support Vector Machine Abstract 1 Introduction 2 System Model 3 Spectrum Sensing Algorithm Based on TWSVM 3.1 Cognitive Process 3.2 Feature Extraction 3.3 TWSVM Training 3.4 Detection Decision 4 Simulation 5 Conclusions ReferencesApplicability Analysis of Plane Wave and Spherical Wave Model in Blue and Green Band Abstract 1 Introduction 2 Gamma-Gamma Turbulence Model of Three Kinds of Beams 3 Simulation Analysis of Turbulence Model 4 Simulation Analysis of SNR Model 5 Conclusion ReferencesA Study of the Influence of Resonant Frequency in Wireless Power Transmission System Abstract 1 Introduction 2 Related Works of WPT 3 Research Method 4 Simulation Results 5 Conclusion Acknowledgements ReferencesDirection of Arrival Estimation Based on Support Vector Regression Abstract 1 Introduction 2 Uniform Linear Array Model and Directions of Arrival Estimation Model 3 Experimental Results 3.1 SVR Test Results 3.2 Simulation Analysis of DOA Estimation Accuracy and Speed 4 Conclusions Acknowledgements ReferencesBistatic ISAR Radar Imaging Using Missing Data Based on Compressed Sensing Abstract 1 Introduction 2 Methods 2.1 Radar Echo Model 2.2 Two-Dimensional CS Decoupling Imaging Algorithm 3 Results 4 Conclusion Acknowledgements ReferencesMedical Images Segmentation Using a Novel Level Set Model with Laplace Kernel Function Abstract 1 Introduction 2 Level Set Formulation 3 Experiments 4 Conclusion Acknowledgements ReferencesResearch on Multi-UAV Routing Simulation Based on Unity3d Abstract 1 Instruction 2 Engineering Realization for Multi-UAV Routing Simulation 2.1 Simulation of Real Terrain 2.2 Simulation of UAV 2.3 Problem of Automatic Pathfinding About UAV 3 Communication Routing Network of Multi-UAV 4 Adjustment Plan When the UAV Loses Connection 5 Conclusions Acknowledgements ReferencesVideo Target Tracking Based on Adaptive Kalman Filter Abstract 1 Introduction 2 Related Algorithms 2.1 Background Subtraction Algorithm 2.2 Standard Kalman Filter Algorithm 2.3 Adaptive Kalman Filter Algorithm 3 Proposed Algorithm Steps 4 Experimental Results and Analysis 5 Conclusion Acknowledgements ReferencesCompressed Sensing Image Reconstruction Method Based on Chaotic System Abstract 1 Introduction 2 Theory of Technology 3 Establish System Model and Analysis Metrics 3.1 Model the System 3.2 Analytical Method 4 Experimental Simulation and Analysis 5 Conclusion Acknowledgements ReferencesAn Underdetermined Blind Source Separation Algorithm Based on Variational Mode Decomposition Abstract 1 Introduction 2 Variational Mode Decomposition 3 VMDSE-FastICA Algorithm 4 Simulation Experiment and Analysis 5 Conclusion Acknowledgements ReferencesA Ranking Learning Training Method Based on Singular Value Decomposition Abstract 1 Introduction 2 Application of SVD in Ranking Training 2.1 Related Algorithms 2.2 SVD Overview and Feature Extraction 3 Experimental 4 Conclusion Acknowledgements ReferencesResearch on Temperature Characteristics of IoT Chip Hardware Trojan Based on FPGA Abstract 1 1 Background 2 2 Preliminary Preparation 2.1 Ring Oscillator Principle 2.2 Circuit Configuration 3 3 Circuit Design and Data Processing Method 3.1 Circuit Design 3.2 Data Process Method 4 4 Test Results at Different Temperatures 5 5 Conclusion ReferencesWireless Communication Intelligent Voice Height Measurement System Abstract 1 Overall Design of Height Measuring Instrument 2 Principle of Ultrasonic Ranging 2.1 Frequency Characteristics 2.2 Ultrasonic Detection Error 2.3 Least Square Fitting 2.4 Least Square Correction 3 Height Measuring Instrument Module Design 3.1 Overall Hardware Circuit Schematic Diagram 3.2 Ultrasonic Ranging Module 3.3 WIFI Data Transmission Module 4 Results Display 4.1 System Function Design Drawing 4.2 Test Outcome 5 Conclusion ReferencesDesign of Intelligent Classification Waste Bin with Detection Technology in Fog and Haze Weather Abstract 1 Overall Design 1.1 The Overall Design of Haze Detection 1.2 Intelligent Waste Bin Overall Design 2 Principle of Air Quality Measurement 2.1 Single Particle Scattering Intensity Distribution Characteristics 2.2 Effects on Scattered Light in Different Situations 3 Infrared Sensor Ranging Principle 4 System Function Design 5 Haze Detector and Smart Bin Module Design 5.1 The Overall Hardware Circuit Schematic 5.2 WIFI Data Transmission Module 5.3 LJA30A3-15-Z/BX Metal Detection Module 6 Conclusion ReferencesA False-Target Jamming Method for the Phase Array Multibeam Radar Network Abstract 1 Introduction 2 Analysis of False Target Interference 3 The Establishment of Interference Model 4 Simulation Experiment and Analysis 5 Conclusion ReferencesAnalysis of TDOA Location Algorithm Based on Ultra-Wideband Abstract 1 Introduction 2 TDOA Positioning Algorithm Description 2.1 Based on Chan Algorithm 2.2 Taylor Series Expansion Positioning Algorithm 3 Algorithm Analysis Comparison 4 Conclusion Acknowledgements ReferencesAlgorithm Design of Combined Gaussian Pulse Abstract 1 Introduction 2 Combined Algorithm Design 2.1 Random Selection Algorithm 2.2 Random Selection Algorithm 3 Simulation Comparison Analysis 4 Conclusion Acknowledgements ReferencesA Network Adapter for Computing Process Node in Decentralized Building Automation System Abstract 1 Introduction 2 System Structure 3 System Design 3.1 Communication Protocol 3.2 Hardware Design 3.3 Software Design 4 Conclusion Acknowledgements ReferencesModel Reference Adaptive Control Application in Optical Path Scanning Control System Abstract 1 1 Introduction 2 2 Optical Path Scanning System Analysis 2.1 Composition of Optical Path Scanning System 2.2 State Space Model of the Controlled Object 3 3 Model Reference Adaptive Control Stability Analysis 4 4 Matlab Simulation and Results 5 5 Conclusions Acknowledgements ReferencesUAV Path Planning Design Based on Deep Learning Abstract 1 1 Introduction 2 2 Scheme Design 2.1 Source of Data 2.2 Neural Network Model 2.3 Training Parameters 3 3 Constraint Conditions of Flight 4 4 Expected Results 5 5 Conclusion Acknowledgements ReferencesResearch on Temperature and Infrared Characteristics of Space Target Abstract 1 Introduction 2 Orbit External Thermal Flux of Space Target 3 Space Target Temperature and Infrared Mathematical Model 4 Analysis of Calculation Results 4.1 Calculation Model 4.2 Effect of β on Target Temperature and Infrared Radiation Characteristics 4.3 Influence of Earth Albedo on Temperature and Infrared Radiation Characteristics 5 Conclusion ReferencesA Multispectral Image Edge Detection Algorithm Based on Improved Canny Operator Abstract 1 1 Introduction 2 2 Traditional Canny Edge Detection Algorithm 3 3 Image Acquisition Experiment 4 4 Improved Canny Edge Detection Algorithm 4.1 Laplacian and Sobel Operator Hybrid Enhancement Algorithm 4.2 5 × 5 Size Sobel Operator to Calculate the Gradient Amplitude Image 4.3 Non-maximum Suppression 4.4 Double Threshold Detection and Edge Connection 5 5 Analysis of Experimental Results 6 6 Conclusion ReferencesA Green and High Efficient Architecture for Ground Information Port with SDN Abstract 1 Introduction 2 Background Information 2.1 Space-Ground Integrated Information Network 2.2 Ground Information Port 2.3 Remote Sensing Data Open Policy 2.4 SDN 3 An Overall SDN-Based Green and Efficient Architecture for Ground Information Port 4 Efficiency Analysis 4.1 Green Evaluation: Energy Consumption Reduction 4.2 High Efficiency: Efficiency Improvement 5 Ground Information Port Development Roadmap 6 Conclusions Acknowledgements ReferencesMarked Watershed Algorithm Combined with Morphological Preprocessing Based Segmentation of Adherent Spores Abstract 1 1 Introduction 2 2 Materials and Methods 2.1 Data Acquisition 2.2 The Watershed Algorithm Combined with Morphology Algorithm 2.2.1 The Brief Framework of the Propose Method 2.2.2 The Concrete Description of the Propose Method 3 3 Experimental Results and Analysis 3.1 Experimental Results 3.2 Result Analysis 4 4 Conclusion Acknowledgements ReferencesData Storage Method for Fast Retrieval in IoT 1 Introduction 2 Storage Method for Fast Retrieval 3 High-Frequency Queries Statistics 4 Experimental Results 5 Conclusion ReferencesEquivalence Checking Between System-Level Descriptions by Identifying Potential Cut-Points 1 Introduction 2 Preliminary 2.1 Symbolic Simulation 2.2 Program Slice 2.3 Program Dependence Diagram 3 Equivalence Checking Algorithm 3.1 Generation of Potential Cut-Points 3.2 Selection of Potential Cut-Points and Program Slicing 3.3 Symbolic Simulation 4 Experiment Results 5 Conclusion ReferencesAn Improved Adversarial Neural Network Encryption Algorithm Against the Chosen-Cipher Text Attack (CCA) Abstract 1 Introduction 2 Improved Adversarial Neural Network Encryption Algorithm Based on CCA (CCA-ANC) 2.1 Algorithm Principle 2.2 Model Structure 2.3 Adversarial Neural Network Architecture 2.4 Improvement of Network Structure and Loss Function Design 3 Model Experiment Simulation and Safety Analysis 3.1 Model Experiment Simulation 3.2 Model Safety Analysis 4 Conclusion Acknowledgements ReferencesHardware Implementation Based on Contact IC Card Scalar Multiplication Abstract 1 Introduction 2 Scalar Multiplication Module 2.1 Scalar Multiplication Theory 2.2 Jacobi Projective Coordinate System 2.3 Montgomery Modular Multiplication 3 Design of Scalar Multiplication 3.1 Design of Modular Addition Module 3.2 Design of Scalar Multiplication Module 3.3 Simulation Results 4 Introduction to ISO7816 Communication Protocol 4.1 System Module Design 4.2 System Simulation of Communication Modules 4.3 Hardware and Software Co-simulation Screenshot 5 FPGA Validation 6 Conclusions ReferencesTiered Spectrum Allocation for General Heterogeneous Cellular Networks 1 Introduction 2 System Model 3 Area Spectral Efficiency Optimization 3.1 Problem Formulation for Spectrum Partitioning 3.2 Spectrum Sharing 4 Conclusion ReferencesHuman Action Recognition Algorithm Based on 3D DenseNet-BC Abstract 1 Introduction 2 3D Densenet-BC Construction 2.1 3D-CNN 2.2 DenseNet-BC 2.3 3D DenseNet-BC 3 Experimental Results and Analysis 3.1 Data Sets 3.2 Experimental Environment Settings 3.3 Experimental Results and Analysis 4 Conclusion ReferencesColor Image Encryption Based on Principal Component Analysis Abstract 1 Introduction 2 2D-Logistic Chaos System and PCA 2.1 2D-Logistic Chaos System 2.2 PCA (Principal Component Analysis) 3 The Scheme of Image Encryption and Decryption 3.1 Image Encryption Algorithm Structure 3.2 Encryption Result 4 Safety Analysis and Experimental Results 4.1 Key Space 4.2 Histogram Analysis 4.3 Sensitivity Analysis 5 Conclusion ReferencesResearch on Transmitter of the Somatosensory Hand Gesture Recognition System Abstract 1 1 Introduction 2 2 Overall Design of Hand Gesture Recognition System 3 3 Hand Gesture Recognition Attitude Algorithm Principe 3.1 Attitude Algorithm of the Six-Axis Sensor MPU6050 3.2 Transformation from Quaternion to Euler Angle 3.3 Complementary Filter Correction Algorithm 4 4 Data Acquisition Process 5 5 Functional Verification Experiments 6 6 Conclusion ReferencesResearch on Image Retrieval Based on Wavelet Denoising in Visual Indoor Positioning Algorithm Abstract 1 1 Introduction 1.1 Visual Positioning Technology 1.2 Image Denoising 2 2 Traditional Denoising Algorithm 2.1 Spatial Domain Filtering 2.2 Frequency Domain Filtering 3 3 Wavelet Denoising Algorithm 3.1 Modulus Maxima Algorithm 3.2 Correlated Denoising Algorithm 3.3 Wavelet Threshold Denoising Algorithm 3.4 Selection of Wavelet Basis in Wavelet Threshold Denoising Algorithm 3.5 Contour Wave Denoising 4 4 Algorithm Performance Analysis 5 5 Conclusion ReferencesAnalysis of the Matching Pursuit Reconstruction Algorithm Based on Compression Sensing Abstract 1 1 Signal Reconstruction 2 2 Matching Pursuit Algorithm and Its Improvement 3 3 Reconstruction Algorithm Performance Analysis 4 4 Conclusion ReferencesSuper-Resolution Based and Topological Structure for Narrow Road Extraction from Remote Sensing Image Abstract 1 1 Introduction 2 2 Background 3 3 Procedure of Narrow Road Extraction 3.1 Road Extraction 3.2 Remove Noise Points by Topological Structure 4 4 Experiment Results and Analysis 5 5 Conclusion Acknowledgements ReferencesEvaluation on Learning Strategies for Multimodal Ground-Based Cloud Recognition 1 Introduction 2 Method 3 Experiments 3.1 Multimodal Ground-Based Cloud Dataset 3.2 Experiment Setup 3.3 Results and Analysis 4 Conclusion ReferencesSAR Load Comprehensive Testing Technology Based on Echo Simulator Abstract 1 1 Introduction 2 2 SAR Load Echo Simulator Works 3 3 SAR Load Test Mode, Test Project and Test Method Design 3.1 SAR Load Test Mode 3.1.1 Planar Near Field Test Mode 3.1.2 Full Power Test Mode 3.1.3 SAR Load Mode Test 3.2 SAR Load Test Project 3.3 SAR Load Test Method 3.3.1 Power Interface Check 3.3.2 Remote Command Check 3.3.3 Telemetry Parameter Check 3.3.4 SAR Load Sub-system Power Test 3.3.5 SAR Load Sub-system Frequency Test 3.3.6 SAR Load Subsystem PRF Test 3.3.7 SAR Performance Index Test 3.3.8 Image Quality Inspection 4 4 SAR Load Sub-system Test Equipment 5 5 SAR Load Satellite Test Application 6 6 Conclusion ReferencesA New Traffic Priority Aware and Energy Efficient Protocol for WBANs Abstract 1 1 Introduction 2 2 Background and Motivation 3 3 An Improved Proposed Protocol 4 4 Simulation 5 5 Conclusion ReferencesDesign of Modulation and Demodulation System Based on Full Digital Phase-Locked Loop Abstract 1 Introduction 2 The Design of FSK Modulation 3 The Design of Demodulation of Full Digital Phase-Locked Loop 3.1 Composition of the Full Digital Phase-Locked Loop 3.2 The Design of the Full Digital Phase-Locked Loop 3.3 The Design of FSK Demodulation 4 System Testing 5 Conclusion ReferencesEthanol Gas Sensor Based on SnO2 Hierarchical Nanostructure Abstract 1 Introduction 2 Experimental Part 3 Result and Discussion 4 Conclusion ReferencesGenerative Model for Person Re-Identification: A Review 1 Introduction 2 Approach 2.1 Overview of GAN 2.2 Generating Unlabeled Samples 2.3 Style Transferring 2.4 Learning Features 3 Experiments 3.1 Database 3.2 Evaluation 4 Conclusion ReferencesLocation Fingerprint Indoor Positioning Based on XGBoost Abstract 1 Introduction 2 Location Fingerprint Indoor Positioning Based on XGBoost 2.1 The Principle of XGBoost Algorithms 2.2 Modeling of Localization Algorithm 2.3 Implementation of Fingerprint Positioning Based on XGBoost 3 Performance Analysis of Location Algorithms 4 Conclusion Acknowledgements ReferencesAn Information Hiding Algorithm for Iris Features Abstract 1 Introduction 2 Secure Iris Feature Using Steganography 2.1 Hiding Phase 2.2 Extracting Phase 3 Experiment Results and Analysis 4 Conclusion Acknowledgements ReferencesThin Film Transistor of CZ-PT Applied to Sensor Abstract 1 Introduction 2 Experiment 3 Results and Discussion 4 Conclusion ReferencesAn Image Dehazing Algorithm Based on Single-Scale Retinex and Homomorphic Filtering Abstract 1 Introduction 2 Retinex Theory Overview 2.1 Single-Scale Retinex Algorithm 2.2 Multi-scale Retinex Algorithm 2.3 Multi-scale MSRCR Algorithm with Color Recovery 3 Homomorphic Filtering 3.1 Homomorphic Filtering Principle 3.2 Improved Gaussian Homomorphic Filtering 4 Proposed Algorithm 4.1 Algorithm Principle 4.2 Objective Performance Indicators 4.3 Simulation Results 4.3.1 Processing Colorful Images 4.3.2 Processing Images with Uneven Illumination 4.3.3 Processing Images with Uniform Illumination 4.4 Objective Analysis 5 Conclusion ReferencesSurvey of Gear Fault Feature Extraction Methods Based on Signal Processing Abstract 1 Introduction 2 Mechanism of Gear Fault Diagnosis 3 Gear Fault Feature Extraction Method Based on Signal Processing 3.1 Short Time Fourier Transformation, STFT 3.2 Autoregressive Moving Average, ARMA 3.3 Cohen Type Distribution 3.4 Wavelet Transform, WT 3.5 Hilbert-Huang Transform 4 Comparison of Various Signal Processing Based Gear Fault Feature Extraction Methods 5 Conclusion ReferencesHyperspectral Image Classification Based on Bidirectional Gated Recurrent Units Abstract 1 Introduction 2 Background of Theory 2.1 Bidirectional Recurrent Neural Network 2.2 Gated Recurrent Units 3 Method Based on Bidirectional Gated Recurrent Units 4 Experimental Result and Discussion 4.1 Data Description 4.2 Comparison Based on Vector Classification Method 5 Conclusion Acknowledgements ReferencesA Survey of Pedestrian Detection Based on Deep Learning Abstract 1 Introduction 2 Related Research 3 Dataset 4 Detection Framework 4.1 Evaluation 5 Conclusion Acknowledgements ReferencesDetection of Anomaly Signal with Low Power Spectrum Density Based on Power Information Entropy 1 Introduction 2 Detection of Anomaly Signal with Low Power Spectrum Density 2.1 Analysis on Information Content of Overlapped Signals 2.2 Anomaly Detection Model Based on OCSVM 3 Results and Analysis 3.1 Analysis the Effect of Histogram Resolution 3.2 Analysis the Effect of Classifier Gamma Parameter 3.3 Analysis the Effect of Power Ratio of the DSSS Signal to the Noise 4 Conclusion ReferencesA Hybrid Multiple Access Scheme in Wireless Powered Communication Systems 1 Introduction 2 System Model 3 Problem Formulation 4 Simulation Results and Discussions 5 Conclusion ReferencesGas Sensing Properties of Molecular Sieve Modified 3DIO ZnO to Ethanol Abstract 1 Introduction 2 Experimental Section 2.1 Fabrication of ZnO Films 2.2 Characterization 2.3 Fabrication and Measurement of Gas Sensing Properties 3 Results and Discussion 3.1 Morphological and Structural Characteristics 3.2 Working Temperature and Selectivity of Gas Sensor 4 Conclusion Acknowledgements ReferencesFiberEUse: A Funded Project Towards the Reuse of the End-of-Life Fiber Reinforced Composites with Nondestructive Inspection Abstract 1 Introduction 2 Hyperspectral Imaging and Data Acquisition 3 Inspection of Metal Corrosion 4 Inspection of Erosion on Wind Turbine Blade 5 Conclusion Acknowledgements ReferencesAutonomous Mission Planning and Scheduling Strategy for Data Transmission of Deep-Space Missions 1 Introduction 2 Planning Request of Data Transmission Task 3 Problem Modeling 3.1 Link Establishment (Model 1) 3.2 Antenna Pointing Control (Model 2) 3.3 Variable Rate Transmission (Model 3) 3.4 Storage Scheduling (Model 4) 4 Logical Relations and Connections of Planning Models 5 Conclusion ReferencesPreparation of TiO2 Nanotube Array Photoanode and Its Application in Three-Dimensional DSSC Abstract 1 Experimental Part 2 Results and Discussion 2.1 Effect of Preparation Parameters on Morphology of TiO2 Nanotubes 2.1.1 Ammonium Fluoride Content in Electrolyte 2.1.2 Water Content in the Electrolyte 2.1.3 Oxidation Voltage 2.1.4 Oxidation Duration 2.2 Annealing 2.3 Solar Cell Assembly and Performance Test 3 Conclusion ReferencesBlock-Based Data Security Storage Scheme Abstract 1 Introduction 2 Formation of Lightweight Blocks 2.1 Block Data Structure 2.2 Data Structure of Singly Linked List 2.3 Light Blockchain Formation 3 Hash Salt Encryption Based on Large Prime Numbers 3.1 Large Prime Generation 3.2 Distribution of Prime Numbers 4 The Realization of the Fourth Block Blockchain Security Storage Scheme 4.1 Process Implementation 4.2 Algorithm Implementation 5 Program Testing and Analysis 5.1 Rainbow Table Attack Principle 5.2 Test and Analysis 6 Conclusion Acknowledgements ReferencesChaos Synchronization and Voice Encryption of Discretized Hyperchaotic Chen Based on Euler Algorithm Abstract 1 Introduction 2 Discretized Hyperchaotic Chen System and Synchronization 2.1 Discretized Hyperchaotic Chen System Based on Euler Method 2.2 Nonlinear Feedback Synchronization 3 Voice Encryption 4 Conclusion Acknowledgements ReferencesMultiple UAV Assisted Cellular Network: Localization and Access Strategy 1 Introduction 2 System Model 3 Multi-UAV Localization Technique 3.1 Dynamic State-Space Model 3.2 Predictor 3.3 Corrector 4 Access Strategy 5 Numerical Simulation Result 6 Conclusion ReferencesWiFi Location Fingerprint Indoor Positioning Method Based on WKNN Abstract 1 Introduction 2 Location Fingerprint Location Method 2.1 WiFi Location Fingerprint Positioning Implementation Principle 2.2 Signal Strength Measurement 2.3 WKNN Matching Algorithm 3 Simulation Result Analysis 4 Conclusion Acknowledgements ReferencesThe Digital Design and Verification of Overall Power System for Spacecraft Abstract 1 Introduction 2 Electric Overall Digital Design 2.1 Design Ideas 2.2 Model-Based Electrical Overall Digitalization Scheme 3 Verification Example 4 Innovation Points 5 Conclusion ReferencesThe Analysis and Practice of Backup Spacecraft Tele Command Based on Chang’E-4 Abstract 1 Introduction 2 Mission Simulation 3 Design Analysis 4 Experimentation in Lab 5 Practice Onboard 5.1 Use the Shelter 5.2 Reduce Power 5.3 Mode Switch Onboard 5.4 Summary 6 Conclusion ReferencesA Modified Hough Transform TBD Method for Radar Weak Targets Using Plot’s Quality Abstract 1 Introduction 2 General Description of the Proposed Method 3 The Definition and Calculation of Radar Plot Quality 3.1 The Definition of Radar Plot Quality 3.2 The Calculation Algorithm of Plot Quality 3.2.1 The Calculation of q_{EP} 3.2.2 The Calculation of q_{SNR} 3.2.3 The Calculation of q_{RA} 3.2.4 The Calculation of q_{D} 4 Simulation and Results Analysis 4.1 The Generation of K-Distributed Sea Clutter 4.2 The Validation of PQ Calculation Algorithms 4.3 The Performance of the PQ-HT TBD Method 5 Conclusion ReferencesAnalysis of the Effects of Climate Teleconnections on Precipitation in the Tianshan Mountains Using Time-Frequency Methods Abstract 1 1 Introduction 2 2 Method 2.1 EEMD Decomposition 2.2 Wavelet Coherence Analysis 3 3 Study Area and Data 3.1 EEMD Decomposition 3.2 Wavelet Coherence Between Precipitation and Climate Indices on the Northern Slope 3.3 Wavelet Coherence Between Precipitation and Climate Indices on the Southern Slope 4 4 Conclusions Acknowledgement ReferencesAn Improved Cyclic Spectral Algorithm Based on Compressed Sensing Abstract 1 Introduction 2 Cyclic Spectrum Algorithm 3 An Improved Cyclic Spectrum Algorithm Based on CS Theory 3.1 The Theory of Compressive Sensing 3.2 An Improved Cyclic Spectrum Algorithm Based on CS Theory 4 Simulations 5 Conclusion ReferencesVideo Deblocking for KMV-Cast Transmission Based on CNN Filtering Abstract 1 1 Introduction 2 2 Related Work 2.1 CNN in Image Reconstruction 2.2 KMV-Cast 3 3 Proposed Method 3.1 Instance Normalization 3.2 L2 Regularization 4 4 Experiments 5 5 Conclusion ReferencesImproved YOLO Algorithm for Object Detection in Traffic Video Abstract 1 1 Introduction 2 2 Data Acquisition and Preprocessing 3 3 Algorithm Implementation and Improvement 3.1 YOLOv3 Algorithm 3.2 Improvement of YOLOv3 Algorithm 3.2.1 Anchor Reselection 3.2.2 Focus Loss 4 4 Analysis of Experimental Results 5 5 Conclusion ReferencesTask Allocation for Multi-target ISAR Imaging in Bi-Static Radar Network Abstract 1 1 Introduction 2 2 Bi-Static ISAR Signal Model 3 3 Task Allocation Optimization Model 4 4 Experiments 5 5 Conclusion Acknowledgements ReferencesA New Tracking Algorithm for Maneuvering Targets Abstract 1 Introduction 2 Kalman Filter Algorithm 3 Improved Algorithm Based on Law of Large Numbers 4 Simulation and Algorithm Analysis 4.1 Simulation and Analysis of Covariance Matrix Estimation Algorithms 4.2 Simulation and Analysis of Maneuvering Target Tracking 4.3 Analysis of Algorithms Under Different Noise Levels 5 Conclusion ReferencesResearch on an Improved SVM Training Algorithm Abstract 1 1 Introduction 2 2 Joint SVM 2.1 Joint Learning 2.2 Output Core 2.3 Optimal Solution 3 3 Output Kernel Learning 3.1 Linear Output Kernel 3.2 Odds Ratio Output Kernel 4 4 Simulation 5 5 Conclusion Acknowledgements ReferencesModeling for Coastal Communications Based on Cellular Networks 1 Introduction 2 System Model 3 Distribution of Cellular Link Distance 4 Coverage and Handover of Coastal Networks 5 Simulation Results and Discussions 6 Conclusion ReferencesResearch of Space Power System MPPT Topology and Algorithm Abstract 1 1 Introduction 2 2 Space MPPT Power System Design 2.1 S3MPR Circuit Topology 2.2 Conductivity Incremental Method Optimization Mechanism 3 3 Simulation and Experiment 3.1 Simulation Analysis 3.2 Test Verification 4 4 Conclusion ReferencesFar-Field Sources Localization Based on Fourth-Order Cumulants Matrix Reconstruction Abstract 1 Introduction 2 Data Model 3 The Proposed Algorithms 3.1 The TFOC-OPRM Algorithm 3.2 Complexity Analysis 4 Simulation Results 5 Conclusions Acknowledgements ReferencesONENET-Based Greenhouse Remote Monitoring and Control System for Greenhouse Environment Abstract 1 Introduction 2 System Overall Design 3 System Hardware Design 4 System Software Design 5 Conclusion Acknowledgements ReferencesDesign of Multi-Node Wireless Networking System on Lunar Abstract 1 1 Introduction 2 2 Analysis of Wireless Communication Protocol 3 3 Design of Wireless Networking 4 4 Design of Protocol Architecture 5 5 Conclusion ReferencesAlgorithm Improvement of Pedestrians’ Red-Light Running Snapshot System Based on Image Recognition Abstract 1 Introduction 2 Algorithmic Improvement Analysis 3 Pedestrian Tracking and Snapping Process Design 4 Pedestrian Tracking Algorithm 5 Face Image Quality Discrimination Algorithms 6 System Testing 7 Conclusion ReferencesA Datacube Reconstruction Method for Snapshot Image Mapping Spectrometer Abstract 1 Introduction 2 General Principle of IMS 3 Geometric Model of IMS 4 Imaging and Reconstruction Simulations 5 Conclusion ReferencesLFMCW Radar DP-TBD for Power Line Target Detection Abstract 1 1 Introduction 2 2 LFMCW Radar Theory 3 3 LFMCW Radar DP-TBD Algorithm Simulation 3.1 Target Motion Model 3.2 Target Measurement Model 3.3 DP-TBD Algorithm Simulation 4 4 LFMCW Radar DP-TBD Algorithm Verification 5 5 Conclusion ReferencesReview of ML Method, LVD and PCFCRD and Future Research for Noisy Multicomponent LFM Signals Analysis Abstract 1 Introduction 2 Review of the ML Method, LVD and PCFCRD 3 Comparisons Based on Theoretical Analyses 3.1 Cross Term 3.2 Computational Cost 3.3 Resolution and PSL 3.4 Anti-noise Performance 4 Simulations and Some Discussions 5 Conclusion ReferencesResearch on Vision-Based RSSI Path Loss Compensation Algorithm Abstract 1 1 Introduction 2 2 Indoor Crowded Scene Algorithm Based on RSSI Model 2.1 The Impact of the Human Body 2.2 Individual Quantity Detection 2.3 Consider a New Indoor Transmission Model of the Human Body 3 3 Testing and Evaluation 4 4 Conclusion Acknowledgements ReferencesEfficient Energy Power Allocation for Forecasted Channel Based on Transfer Entropy Abstract 1 Introduction 2 Granger Causality Test 3 Channel Forecasting Based on Transfer Entropy 4 IWF Algorithm 5 Simulations and Analysis 6 Conclusion Acknowledgements ReferencesA Modular Indoor Air Quality Monitoring System Based on Internet of Thing Abstract 1 Introduction 2 IoT Structure and Sensor Selection 3 Platform Software Development 4 Experimental Results and Analysis 5 Conclusion ReferencesPerformance Analysis for Beamspace MIMO-NOMA System 1 Introduction 2 System Model 2.1 Channel Model 2.2 Beamspace MIMO-NOMA System Model 3 Relevant Algorithms and Methods 3.1 Beam Selection Algorithm 3.2 Clustering Method 3.3 Power Allocation of Intra-cluster and Inter-cluster 3.4 Precoding Matrix 4 Simulation Results 4.1 System with Various Beam Selection Algorithms and Amplitude-Clustering 4.2 System with Various Beam Selection Algorithms and Correlation-Clustering 5 Conclusions ReferencesA Novel Low-Complexity Joint Range-Azimuth Estimator for Short-Range FMCW Radar System Abstract 1 1 Introduction 2 2 FMCW Radar Signal Model 3 3 Proposed Algorithm 4 4 Experimental Results and Analysis 5 5 Conclusion ReferencesComparative Simulation for Nonlinear Effect of Hybrid Optical Fiber-Links in High-Speed WDM Systems 1 Introduction 2 System Model 3 Problem Description and Analysis 4 Simulation and Analysis 5 Conclusion ReferencesPOI Recommendation Based on Heterogeneous Network 1 Introduction 2 Representation Learning Model Based on Heterogeneous Network 3 Base on Deep Neural Network Recommendation Framework 4 Experiments 5 Conclusion ReferencesA Survey on Named Entity Recognition Abstract 1 Introduction 2 Rule-Based and Dictionary-Based Methods 3 Statistical Learning Based Method 3.1 HMM 3.2 CRF 4 Hybrid Method 5 Deep Learning Based Approach 6 Latest Method 6.1 Attention Mechanism 6.2 Transfer Learning 6.3 Semi-supervised Learning 7 Summary and Outlook Acknowledgements ReferencesA Hybrid TWDM-RoF Transmission System Based on a Sub-Central Station Abstract 1 1 Introduction 2 2 Architecture of the TWDM-RoF System 3 3 Simulation and Results 4 4 Conclusions Acknowledgements ReferencesOptimal Subcarrier Allocation for Maximizing Energy Efficiency in AF Relay Systems Abstract 1 Introduction 2 System Model and Problem Formulation 2.1 System Model 2.2 Problem Formulation 3 Optimal Solution 4 Simulation Results Acknowledgements ReferencesA Study on D2D Communication Based on NOMA Technology Abstract 1 Introduction 2 System Model 3 Problem Description 4 Resource and Power Allocation Algorithm 5 Simulation and Analysis 6 Conclusion Acknowledgements ReferencesResearch on Deception Jamming Methods of Radar Netting Abstract 1 1 Introduction 2 2 Radar Netting Modeling 3 3 Track Jamming of Netted Radar 3.1 The Parameter Settings on Track’s Deception Jamming 3.1.1 Interference Power 3.1.2 Time Delay on Distance 3.1.3 Time Delay on Bearing 3.1.4 The Doppler Frequency Shift 3.1.5 The Jamming Signal Form 3.2 The Track Jamming of Netted Radar 3.2.1 Algorithm Analysis 3.2.2 The Simulation Experiment 3.2.3 The Error Analysis 4 4 Conclusion Acknowledgements ReferencesCluster Feed Beam Synthesis Network Calibration Abstract 1 1 Introduction 2 2 Beam Synthesis Network Calibration 2.1 Point Frequency Amplitude-Phase Detection 2.2 Code Division Amplitude-Phase Detection 3 3 Amplitude-Phase Detection of Ground-Based Beamforming Network 4 4 Simulation Analysis 5 5 Conclusions ReferencesDesign and Optimization of Cluster Feed Reflector Antenna Abstract 1 1 Introduction 2 2 Reflector Beam Optimization of Cluster Feed 2.1 Envelope Method for Optimizing Object Modeling 2.2 Optimum Design of Target Beam 3 3 Beam Optimization by Improved Genetic Algorithm 3.1 Coding 3.2 Choice 3.3 Crossing 3.4 Variation 3.5 Treatment of Constraints 4 4 Simulation Analysis 4.1 Test Results of Reflector Principle Prototype 4.2 Optimum Design and Simulation 5 5 Conclusions ReferencesCognitive Simultaneous Wireless Information and Power Transfer Based on Decode-and-Forward Relay Abstract 1 Introduction 2 System Model 3 Problem Constraint Model 4 Simulation Result 5 Conclusion Acknowledgements ReferencesA Deep-Learning-Based Distributed Compressive Sensing in UWB Soil Signals 1 Introduction 2 LSTM-DCS Design 2.1 Traditional Recovery Methods for the DCS 2.2 Proposed LSTM-DCS 3 Experimental Results and Discussion 3.1 UWB Soil Echo Signals 3.2 Analysis and Comparison of Different Methods 3.3 Stability Analysis of the Proposed Methods 4 Conclusion and Future Work ReferencesAn Improved McEliece Cryptosystem Based on QC-LDPC Codes Abstract 1 1 Introduction 2 2 An Improved McEliece Cryptosystem 2.1 Key Generation 2.2 Algorithm 3 3 Simulation Results and Analysis 4 4 Security Analysis 5 5 Conclusion Acknowledgements ReferencesResearch on Multi-carrier System Based on Index Modulation Abstract 1 1 Introduction 2 2 Design and Analysis of OFDM-IM Model 3 3 Comparative Analysis of Simulation 4 4 Concluding Remarks ReferencesA GEO Satellite Positioning Testbed Based on Labview and STK Abstract 1 1 Introduction 2 2 CON Module and Serial Port Transmission 2.1 STK CON Module 2.2 Serial Transmission 3 3 Design and Implementation of Simulation Verification System 4 4 Conclusion Acknowledgements ReferencesSVR Based Nonlinear PA Equalization in MIMO System with Rayleigh Channel 1 Introduction 2 MIMO System Model with Nonlinear Channel 3 SVR Based Nonlinear PA Distortion Equalizer 4 Simulation Results 5 Conclusions ReferencesLoS-MIMO Channel Capacity of Distributed GEO Satellites Communication System Over the Sea Abstract 1 Introduction 2 System Model 3 Maximum MIMO Capacity 4 The Arrangement of the Antenna Position 5 Conclusions Acknowledgements ReferencesDesign and Implementation of Flight Data Processing Software for Global Flight Tracking System Based on Stored Procedure Abstract 1 Introduction 2 System Construction and Operational Procedure 3 Software Architecture and Interface Design 4 Software Implementation Based on Stored Procedure 5 Performance Test Based on Stored Procedure 6 Concluding Remarks Acknowledgements ReferencesFace Recognition Method Based on Convolutional Neural Network Abstract 1 Introduction 2 Convolutional Neural Network 2.1 Network Model Based on ResNet Design 2.2 Experimental Results and Analysis 3 Summary ReferencesAn On-Line ASAP Scheduling Method for Time-Triggered Messages Abstract 1 1 Introduction 2 2 Scheduling Models 3 3 Path Selection for Easier Scheduling 4 4 Time Slots Allocation 5 5 Request and Acknowledge Protocols to Free Conflict 6 6 Conclusions ReferencesSoil pH Value Prediction Using UWB Radar Echoes Based on XGBoost 1 Introduction 2 Data Preprocessing 3 Soil pH Value Prediction Using XGBoost Algorithm 3.1 XGBoost in Soil pH Value Prediction 3.2 Algorithm Simulation and Results Analysis 4 Conclusion ReferencesA Novel Joint Resource Allocation Algorithm in 5G Heterogeneous Integrated Networks Abstract 1 Introduction 2 System Model 3 The Proposed Algorithm 4 The Simulations Results 5 Conclusion Acknowledgements ReferencesA Vehicle Positioning Algorithm Based on Single Base Station in the Vehicle Ad Hoc Networks Abstract 1 1 Introduction 2 2 System Model 3 3 The Proposed Vehicle Positioning Algorithm Based on Single Base Station 3.1 Measuring Distance Based on Power Loss Model 3.2 Estimating the Angle of Vehicle 3.3 Vehicle Positioning Algorithm Based on Power Loss Prediction Model and ESPRIT 3.3.1 Location of Uniform Speed Vehicle 3.3.2 Location of Variable Speed Vehicle 4 4 The Simulations Results 4.1 Simulation Parameters 4.2 Simulation Results 4.2.1 Vehicle Distance Simulation Results 4.2.2 Vehicle Angle Simulation Results 4.2.3 Vehicle Angular Distance Integrated Location 5 5 Conclusion Acknowledgements ReferencesHeterogeneous Wireless Network Resource Allocation Based on Stackelberg Game Abstract 1 1 Introduction 2 2 Stackelberg Game with Multi-master and Multi-slave 3 3 Resource Allocation Based on Stackelberg Game 4 4 Nash Equilibrium Point of User Non-cooperative Game 5 5 Simulation Results 6 6 Conclusions Acknowledgements ReferencesOn the Performance of Multiuser Dual-Hop Satellite Relaying Abstract 1 Introduction 2 System Model 3 Outage Performance Analysis 3.1 Exact Outage Probability 3.2 Asymptotic Outage Probability 4 Numerical Results 5 Conclusion ReferencesArchitectures and Key Technical Challenges for Space-Terrestrial Heterogeneous Networks Abstract 1 Introduction 2 Architecture of Space-Terrestrial Heterogeneous Networks 3 Protocols Architectures 4 Integrated Routing of Space-Terrestrial Heterogeneous Networks 5 Conclusions Acknowledgements ReferencesDesign and Implementation of the Coarse and Fine Data Fusion Based on Round Inductosyn Abstract 1 Introduction 2 Principle of Inductosyn 3 Analysis of Data Fusion Algorithms 3.1 Simple Table Look-up Algorithm 3.2 Improved Table Look-Up Algorithm 4 Implementation of Data Fusion and Thermal Test 5 Conclusions ReferencesLossless Flow Control for Space Networks 1 Introduction 2 Related Works 2.1 Space Network 2.2 Flow Control 3 Motivation 4 Lossless Flow Control 5 Discuss 5.1 Implement Details 5.2 Power Saving 6 Conclusion ReferencesHeterogeneous Network Selection Algorithm Based on Deep Q Learning 1 Introduction 2 System Model 3 Markov Process of Network Selection 3.1 Definition of NSMDP 3.2 Reward Function 4 Network Selection Algorithms Based on DQN 4.1 Deep Q Network for NSMDP 5 Simulation Results 6 Conclusion ReferencesVertical Handover Algorithm Based on KL-TOPSIS in Heterogeneous Private Networks 1 Introduction 2 Heterogeneous Network System Model 3 Algorithm Description 3.1 Subjective Weight Calculation Based on AHP 3.2 Objective Weight Calculation Based on Entropy Method 3.3 Candidate Network Sorting Based on KL-TOPSIS 4 Simulation Results 5 Conclusion ReferencesA Deep Deformable Convolutional Method for Age-Invariant Face Recognition 1 Introduction 2 Related Work 3 Proposed Method 4 Experiments 4.1 Experiment on CACD Datasets 4.2 Experiment on FGNET Datasets 4.3 Experiment on LFW Datasets 5 Conclusion ReferencesWeight Determination Method Based on TFN and RST in Vertical Handover of Heterogeneous Networks 1 Introduction 2 Related Work 3 Subjective Weight Based on TFN 4 Objective Weight Based on RST 5 Comprehensive Weight 6 Simulation and Numerical Analysis 7 Conclusion ReferencesDeep Learning-Based Device-Free Localization Using ZigBee Abstract 1 Introduction 2 Proposed Localization Model 3 Experimental Setup, Results and Analyses 4 Conclusions Acknowledgements ReferencesA Modified Genetic Fuzzy Tree Based Moving Strategy for Nodes with Different Sensing Range in Heterogeneous WSN 1 Introduction 2 The GFT Moving Strategy and Moving Model 2.1 Moving Model of the Heterogeneous WSN 3 Simulations and Analysis 4 Conclusion ReferencesWireless Indoor Positioning Algorithm Based on RSS and CSI Feature Fusion 1 Introduction 2 Related Work 2.1 Positioning Algorithm Based on RSS 2.2 Positioning Algorithm Based on CSI 3 Positioning Algorithm Based on RSS and CSI Feature Fusion 3.1 Off-Line Phase 3.2 Online Phase 4 Experiment Analysis 5 Conclusion ReferencesDesign and Verification of On-Board Computer Based on S698PM and Time-Triggered Ethernet Abstract 1 Introduction 2 Introduction of the S698PM Processor and Time-Triggered Ethernet 3 Overall Design of the On-Board Computer 3.1 On-Board Computer Internal Bus Selection 3.2 Composition of the On-Board Computer 4 Design Verification 5 Conclusion ReferencesAn Optimal Deployment Strategy for Radars and Infrared Sensors in Target Tracking 1 Introduction 2 Sensor Deployment Model in Target Tracking 2.1 Basic Description 2.2 Data Fusion 2.3 Scoring System 3 Dimensional-Reduced PSO Algorithm 3.1 Drawbacks of Classical PSO 3.2 Nonlinear Inertia Weight Model 3.3 Dimensional Reduction for Particles 4 Simulation Results 5 Conclusions and Future Work ReferencesIntegrity Design of Spaceborne TTEthernet with Cut-Through Switching Network Abstract 1 Introduction 2 Safety Design of Cut-Through Switching 2.1 A Brief Introduction to TTEthernet 2.2 CRC Fast Verification Method in Cut-Through Forwarding Mode 3 Network Planning Verification by SMT 4 Conclusion and Future Work ReferencesImage Mosaic Algorithm Based on SURF Abstract 1 Image Mosaic Technology 2 SURF Feature Detection Operator 2.1 Computational Integral Images 2.2 Scale Space Construction and Feature Detection 2.3 Determination of the Main Direction and Description Sub-calculation 3 Image Mosaic Algorithms Based on SURF 4 Conclusion Acknowledgements ReferencesResearch on Dynamic Performance of DVR Based on Dual Loop Vector Decoupling Control Strategy Abstract 1 DVR Control Strategy and Mathematical Model 2 Control Design of DVR 3 Simulation Verification 4 Conclusion Acknowledgements ReferencesFacial Micro-expression Recognition with Adaptive Video Motion Magnification 1 Introduction 2 Related Works 2.1 Traditional Features and Classifiers 2.2 Deep Neural Networks 3 Method 3.1 Adaptive Video Motion Magnification 3.2 CNN Architecture 4 Experiment 4.1 Databases and Preprocessing 4.2 Adaptive Video Motion Magnification 4.3 Performance on CASMEII 5 Conclusion ReferencesComputation Task Offloading for Minimizing Energy Consumption with Mobile Edge Computing 1 Introduction 2 System Model and Problem Formulation 3 Efficient Computation Task Offloading Algorithm 4 Numerical Results 5 Conclusion ReferencesSoil pH and Humidity Classification Based on GRU-RNN Via UWB Radar Echoes 1 Introduction 2 Field Experiment 3 Soil Classification 3.1 Data Preprocessing 3.2 GRU Algorithm for Soil Classification 4 Simulation and Analysis of Soil Classification 5 Conclusion ReferencesBit Error Rate Analysis of Space-to-Ground Optical Link Under the Influence of Atmospheric Turbulence Abstract 1 1 Introduction 2 2 Analysis of Influence Factors 2.1 Influence of the Absorption and Scattering 2.2 Influence of the Background Light 2.3 Influence of the Atmospheric Turbulence 3 3 Analysis of Incoherent Space-to-Ground Optical Link 3.1 Probability Density of the Received Light Intensity 3.2 BER Analysis Under the Influence of Atmospheric Turbulence 3.3 Comprehensive Model 4 4 Conclusion Acknowledgements ReferencesPerformance Analysis of Amplify-and-Forward Satellite Relaying System with Rain Attenuation Abstract 1 Introduction 2 System and Channel Models 2.1 System Model 2.2 Channel Models 3 Outage Performance Analysis 4 Numerical Results 5 Conclusions ReferencesThreat-Based Sensor Management For Multi-target Tracking 1 Introduction 2 Threat-Based Sensor Management Method 2.1 Target Threat Model 2.2 Sensor Management Model 3 Numerical Studies 3.1 System Setup 3.2 Simulation Results 4 Conclusion ReferencesResearch on Measurement Matrix Based on Compressed Sensing Theory Abstract 1 1 Coherence Condition 2 2 Common Measurement Matrices 3 3 Analysis of Common Measurement Matrix Performance 4 4 Improved Bernoulli Matrix 5 5 Conclusion ReferencesPID Control of Electron Beam Evaporation System Based on Improved Genetic Algorithm Abstract 1 1 Introduction 2 2 System Mathematical Model 3 3 Tuning of Common PID Parameter Optimization Methods 4 4 Genetic Algorithms to Optimize the Setting of PID Parameters 5 5 Conclusion ReferencesDoppler Weather Radar Network Joint Observation and Reflectivity Data Mosaic Abstract 1 Introduction 2 Soomthing Strategy 3 Verification 4 Conclusion ReferencesNumerical Calculation of Combustion Characteristics in Diesel Engine Abstract 1 1 Introduction 2 2 Diesel Engine In-Cylinder Spray and Combustion Model 2.1 Spray Mixing Process Gas Flow Turbulence Model 2.2 Breakup Model 2.3 Spary-Wall Model 2.4 Evaporation Model 2.5 Turbulent Combustion Model 3 3 Diesel Engine Cylinder Working Process Modeling 3.1 Computational Area Meshing 3.2 Calculate Boundary Conditions 4 4 Analysis of Calculation Results 4.1 Diesel Engine Performance 4.2 Combustion Characteristic 5 5 Summary ReferencesA NOMA Power Allocation Strategy Based on Genetic Algorithm Abstract 1 1 Introduction 2 2 System Model 3 3 Power Allocation Algorithm 4 4 Simulation and Analysis 5 5 Conclusions Acknowledgements ReferencesAUG-BERT: An Efficient Data Augmentation Algorithm for Text Classification 1 Introduction 2 Related Work 3 Data Augmentation 3.1 Masked Language Model 3.2 Aug-BERT 4 Experiments 4.1 Metrics and Implementation Details 4.2 Datasets 4.3 Baselines 4.4 Results 5 Conclusion ReferencesCoverage Performance Analysis for Visible Light Communication Network 1 Introduction 2 System Model 3 QoE Probability Coverage Model 4 Simulation Results 5 Conclusion ReferencesAn Intelligent Garbage Bin Based on NB-IoT Abstract 1 1 Introduction 2 2 System Overall Design 3 3 Hardware Design 3.1 Infrared Sensor 3.2 Ultrasonic Sensor 3.3 Intelligent Mobile and Obstacle Avoidance 3.4 Motor Drive 3.5 Temperature and Humidity Sensor 4 4 Software Design 4.1 Software Design of Main Controller 4.2 NB-IoT Module Workflow 4.3 ONENET Platform 5 5 Conclusion Acknowledgements ReferencesResearch on X-Ray Digital Image Defect Detection of Wire Crimp Abstract 1 1 Background 2 2 Measurement of Typical Characteristic Parameters 2.1 Resistance Clamp Detection 2.2 Detection of Steel Core in the Connecting Pipe 2.3 Measurement of Other Characteristic Parameters 3 3 Chroma and Contrast Adjustment 4 4 Conclusion Acknowledgements ReferencesArchitecture and Key Technology Challenges of Future Space-Based Networks Abstract 1 1 Introduction 2 2 Architecture of Space-Based Networks 3 3 Physical Structure 3.1 GEO Function Nodes 3.2 MEO/LEO Function Nodes 4 4 Logical Structure 4.1 Virtual Nodes 4.2 Virtual Networks 5 5 Architecture of Space-Based Network Technology 5.1 Resource Layer 5.2 Service Layer 5.3 Application Layer 5.4 Operation and Maintenance Control Domain 5.5 Security Protection Domain 6 6 Conclusions Acknowledgements ReferencesFilter Bank Design for Subband Adaptive Microphone Arrays Abstract 1 1 Introduction 2 2 Theory 2.1 Complex (DFT) Modulated Filter Banks 3 3 Implementation 3.1 Analysis of the DFT Filter Banks 3.2 Synthesis of the DFT Filter Banks 3.3 Some Programs About Different Function of the Complex (DFT) Modulated Filter Banks 3.4 Test of the Program 4 4 Application 4.1 Filter Bank Design for Subband Adaptive Microphone Arrays 4.2 Synthesis Filter Bank Design 5 5 Conclusion ReferencesCommunication System Based on DFT Spread Spectrum Technology to Reduce the Peak Average Power Ratio of CO-OFDM System Abstract 1 1 Introduction 2 2 System Theoretical Analysis 2.1 Principle of COOFDM System 2.2 Analysis of PAPR 2.2.1 DFT-Spread-OFDM 2.2.2 Computation Complexity 3 3 System Simulation 4 4 Simulation Result 4.1 PAPR Under Different Algorithms 4.2 System Analysis Without Channel 5 5 Conclusion Acknowledgements ReferencesLow-Complexity Channel Estimation Method Based on ISSOR-PCG for Massive MIMO Systems 1 Introduction 2 System Model 3 The Proposed Low-Complexity ISSOR-PCG Method 3.1 Conventional MMSE Channel Estimator 3.2 Proposed ISSOR-PCG Channel Estimation 3.3 Relaxation Parameter and Complexity Analysis 4 Simulation Results 5 Conclusion ReferencesShip Classification Methods for Sentinel-1 SAR Images Abstract 1 1 Introduction 2 2 Multi-feature Extraction of Ships for SAR 3 3 Deep CNN Networks for SAR Ship Classification 4 4 Experimental Results and Analysis 4.1 OpenSARShip Dataset 4.2 Feature Extraction and SVM Based Classification 4.3 Ship Classifier Based on Modified LeNet 5 5 Conclusions ReferencesWheat Growth Assessment for Satellite Remote Sensing Enabled Precision Agriculture Abstract 1 1 Introduction 2 2 Related Work 2.1 BP Neural Network 2.2 MR Algorithm 3 3 Proposed Framework 3.1 Data Preparation 3.2 Designed Method 4 4 Results 5 5 Conclusion Acknowledgements ReferencesAn Improved ToA Ranging Scheme for Localization in Underwater Acoustic Sensor Networks Abstract 1 Introduction 2 Design Challenges 3 Description of the Improved ToA Ranging Scheme 4 Experimental Results 5 Conclusion Acknowledgements ReferencesPerformance Analysis of Three-Layered Satellite Network Based on Stochastic Network Calculus 1 Introduction 2 System Model 3 System Model Analysis 3.1 Finite-State Markov Channel Model (FSMC) 3.2 Two-State G-E Model 3.3 Three-Layered Satellite Network Service Curve 4 Numerical Analysis 5 Conclusion ReferencesRobust Sensor Geometry Design in Sky-Wave Time-Difference-of-Arrival Localization Systems 1 Introduction 2 Basic Fundamentals 2.1 Signal Model 2.2 Cramer-Rao Bound Without Ionosphere-Layer Height Errors 2.3 Problem of Designing Sensor Geometries 3 Design of Grouped Sensor Geometry 3.1 Proposition of Grouped Sensor Geometry Scheme 3.2 Model and Analysis 4 Cramer-Rao Band of Grouped Sensor Geometry Scheme 5 Simulation Results 6 Conclusion ReferencesA NOMA Power Allocation Method Based on Greedy Algorithm Abstract 1 Introduction 2 System Model 3 Power Allocation Algorithm 3.1 Inter-carrier Power Allocation 3.2 Intra-carrier Power Allocation 4 Simulation and Analysis 5 Conclusion Acknowledgements ReferencesMulti-sensor Data Fusion Using Adaptive Kalman Filter 1 Introduction 2 Attitude Algorithm Based on Quaternion 2.1 Quaternion Representation of Attitude Angle 2.2 Updating Equation of Attitude Quaternion 3 Data Fusion Based on Multi-sensor 3.1 Multi-sensor Measurement 3.2 Adaptive Kalman Filter 4 Experimental Verification and Analysis 5 Conclusions ReferencesFeasibility Study of Optical Synthetic Aperture System Based on Small Satellite Formation Abstract 1 Introduction 2 Present Research 3 Small Satellite Formation Schemes 3.1 Single Satellite Unfolding Structure Scheme 3.2 Multi-satellite Formation Network Scheme 3.3 Multi-satellite Intersection Docking Scheme 3.4 Synthetic Aperture Light Field Imaging Scheme 4 Technology Challenges 4.1 Load Deployment 4.2 Satellite Formation 4.3 Sparse Aperture Optical Machine Technology 4.4 Image Restoration and Reconstruction Processing 5 Conclusions Acknowledgements ReferencesA New Coded-Modulated Pulse Train for Continuous Active Sonar Abstract 1 Introduction 2 GSFM-Costas Pulse Train Model 2.1 The GSFM Waveforms 2.2 The Costas Sequence 2.3 The GSFM-Costas Pulse Train 3 Simulation Result 4 Conclusion Acknowledgements ReferencesRegion Based Hierarchical Modelling for Effective Shadow Removal in Natural Images Abstract 1 1 Introduction 2 2 Shadow Image Model 3 3 The Proposed Method 3.1 Shadow Detection 3.2 Umbra Removal 3.3 Penumbra Removal 4 4 Experiment 5 5 Conclusion Acknowledgements ReferencesCollaborative Attention Network for Natural Language Inference Abstract 1 1 Introduction 2 2 Background 2.1 Structured Self-Attention 2.2 Decomposable Attention 3 3 Approach 4 4 Experiments 4.1 Data 4.2 Details 4.3 Results 5 5 Conclusion Acknowledgements ReferencesThree-Dimensional Imaging Method of Vortex Electromagnetic Wave Using MIMO Array Abstract 0 1 Introduction 0 2 Optimization Array Model 0 3 Imaging Reconstruction 0 4 Simulation 0 5 Conclusion ReferencesEnergy Storage Techniques Applied in Smart Grid Abstract 1 1 Introduction 2 2 Power System and Smart Grid 2.1 Power System Problem 2.2 Smart Grid 3 3 Energy Storage Technology 3.1 The Significance of Energy Storage Technology 3.2 Energy Storage Technology 3.3 Chemical Battery Energy Storage 4 4 Summary ReferencesA Robust Hough Transform-Based Track Initiation Method for Multiple Target Tracking in Dense Clutter Abstract 1 Introduction 2 The Theory of Hough Transform (HT) for Track Initiation 3 Real Time Hough Transform Based Track Initiation Method 4 The Proposed Algorithm 4.1 Grid-Based Velocity Test 4.2 Grid-Based Clustering Method 4.3 Hough Transform on Each Group Data 5 Experiment Result 6 Conclusion ReferencesStructure Design and Analysis of Space Omni-Directional Plasma Detector Abstract 1 1 Overview 2 2 Constraints and Requirements of Design 2.1 Constraints of Physical Design 2.2 Constraints of Electrical Design 2.3 Constraints of Mechanical Design 2.4 Design Criterion of Statics and Dynamics 3 3 Design 4 4 Simulation and Analysis by FEM 4.1 Setting Up the Model 4.2 Simulation Conditions 4.3 Modal Analysis and Frequency Response Analysis 5 5 Vibration Test 6 6 Conclusions ReferencesDesign and Implementation of GEO Battery Autonomous Management System for Lithium Battery with Balanced Control Function Abstract 1 1 Introduction 2 2 Requirements Analysis 3 3 Design of Autonomous System 3.1 Architecture Design 3.2 Autonomous Management Module 4 4 Implement the Application 4.1 Autonomous Charge and Discharge Management 4.2 Autonomous Balanced Management 4.3 Autonomous Overcharge Protection Management 4.4 Autonomous Overvoltage Protection Management 5 5 Conclusion ReferencesTarget Direction Finding in HFSWR Sea Clutter Based on FRFT Abstract 1 Introduction 2 Target and Sea Clutter Signal Model 3 Direction Finding Based on FRFT 4 Simulation 5 Conclusion Acknowledgements ReferencesAdaptive Non-uniform Clustering Routing Protocol Design in Wireless Sensor Networks Abstract 1 1 Introduction 2 2 System Model 2.1 Network Model and Assumptions 2.2 Energy Consumption Model of the Nodes 3 3 The Proposed AUCR Protocol Design 3.1 Node Clustering 3.2 Inter-cluster Multi-hop Routing 3.3 Cluster Radius Adjustment 4 4 Simulation Results 4.1 Cluster Radius Adjustment 4.2 Life Cycle and Load Balancing 5 5 Conclusion Acknowledgements ReferencesComparative Analysis of Reflectivity from an Updated SC Dual Polarization Radar and a SA System in CINRAD Network Abstract 1 1 Introduction 2 2 Equipment Introduction 3 3 Radar Data Processing and Comparative Analysis 4 4 Quantitative Precipitation Estimation and Analysis of Dual-Line Polarization Radar 5 5 Conclusion ReferencesSubcarrier Allocation-Based SWIPT for OFDM Cooperative Communication 1 Introduction 2 System Model and Problem Formularion 2.1 System Model 2.2 Problem Formulation 3 Optimal Solution 3.1 OPTIMIZING p1* with GIVEN G 3.2 Optimizing p2* with Given G 3.3 Obtaining the Optimal G 4 Simulations Result 5 Conclusions ReferencesPower Control for Underlay Full-Duplex D2D Communications Based on D. C. Programming Abstract 1 Introduction 2 System Model 3 Problem Formulation and Power Control Algorithm 4 Simulation Results 5 Conclusion Acknowledgements ReferencesPower Control for Underlay Full-Duplex D2D Communications Based on Max-Min Weighted Criterion Abstract 1 Introduction 2 System Model 3 Power Control 3.1 Problem Description 3.2 Algorithm Description 4 Numerical Results 5 Conclusion Acknowledgements ReferencesAnalysis on the Change of Dynamic Output Degree Distributions in the BP Decoding Process of LT Codes 1 Introduction 2 Preliminary and Definitions 3 Analysis of BP Decoding Process for LT Codes 4 Simulation Results 5 Conclusion ReferencesA Stable and Reliable Self-tuning Pointer Type Meter Reading Recognition Based on Gamma Correction Abstract 1 Introduction 2 The Reading Recognition of Pointer Meter 2.1 Image Enhancement 2.2 Skeleton Extraction 2.3 Target Detection Based on Hough Transform 2.4 Angle Interpretation 3 Experimental Results and Analysis 3.1 Effectiveness of the Enhanced Algorithm 3.2 Validity of Pointer Extraction 3.3 Accuracy of Interpretation Method 3.4 Real-Time Performance of the Algorithm 4 Conclusion and Future Work Acknowledgements ReferencesSpectral Efficiency for Multi-pair Massive MIMO Two-Way Relay Networks with Hybrid Processing 1 Introduction 2 System Model 3 Large M Analysis 4 Simulation 5 Conclusion ReferencesAn Improved Frost Filtering Algorithm Based on the Four Rectangular Windows Abstract 1 1 Introduction 2 2 Frost Filtering Algorithm 3 3 Improved Frost Filtering Algorithm 3.1 Construction of Four Rectangular Windows 3.2 An Improved Frost Filtering Algorithm Based on Four Rectangular Windows 4 4 Experiments and Analysis 5 5 Conclusion ReferencesWaterline Extraction Based on Superpixels and Region Merging for SAR Images Abstract 1 1 Introduction 2 2 Proposed Algorithm 3 3 Experiment Result and Evaluation 4 4 Conclusion ReferencesCoastline Detection with Active Contour Model Based on Inverse Gaussian Distribution in SAR Images Abstract 1 1 Introduction 2 2 Active Contour Model Based on Gamma Distribution 3 3 Active Contour Model Based on Inverse Gaussian Distribution 4 4 Experiments 5 5 Conclusion ReferencesFacial Expression Recognition Based on Subregion Weighted Fusion and LDA 1 Introduction 2 Proposed Algorithm 3 Experiment Results and Evaluation 3.1 Comparative Analysis of Different Dimensions 3.2 Classification Recognition Result 4 Conclusion ReferencesExtended Target Tracking Using Non-linear Observations 1 Introduction 2 System Model 3 Theoretical Basis of Single Target Tracking 3.1 Tracking Process of Single Extended Target 3.2 Generation of Nonlinear Observation Data 4 Theoretical Basis of Multiple Target 5 Numerical Results 6 Conclusion ReferencesA Coordinated Multi-point Handover Scheme for 5G C/U-Plane Split Network in High-Speed Railway 1 Introduction 2 Network Model 3 Handover Scheme 4 Performance Analysis 5 Simulation Results 6 Conclusion ReferencesA Hierarchical FDIR Architecture Supporting Online Fault Diagnosis Abstract 1 Introduction 2 Overview of Hierarchical FDIR Architecture 3 Highly Decoupled Runtime Model 4 Unified FDIR Model 5 Conclusion Acknowledgements ReferencesCollege Students Learning Behavior Analysis Based on SVM and Fisher-Score Feature Selection Abstract 1 Introduction 2 Support Vector Machine Algorithm 3 Feature Selection Based on Fisher-Score 4 Experimental Results 5 Conclusion ReferencesNetwork Traffic Text Classification Based on Multi-instance Learning and Principal Component Analysis Abstract 1 1 Introduction 2 2 Multi-instance Learning Algorithm 3 3 Feature Selection Based on Principal Component Analysis 3.1 Principal Component Analysis 3.2 Algorithm Description 4 4 Experimental Results 4.1 Experimental Condition 4.2 Experimental Data Preparation 4.3 Classification Result 5 5 Conclusion ReferencesCalculation and Simulation of Inductive Overvoltage of Transmission Line Based on Taylor’s Formula Expansion Double Exponential Function Abstract 1 Introduction 2 Lightning Current Waveform Model 3 Vertical Electric Field Analytical 4 Field Line Sensing Model 5 Analysis of Simulation Results of Inductive Overvoltage on Transmission Lines 6 Conclusion ReferencesDeep Learning Based Exploring Channel Reciprocity Method in FDD Systems Abstract 1 Introduction 2 System Model and the Existence of Uplink-CSI to Downlink-CSI Mapping 2.1 System Model 2.2 The Existence of Uplink-CSI to Downlink-CSI Mapping 3 Architecture and Principles of the Proposed CLSTM-Net 4 Experiments and Discussions 5 Conclusions ReferencesSteering Machine Learning Mechanism Based on Big Data Integrated Cooperative Collision Avoidance for MASS 1 Introduction 2 System Model 2.1 Vessel Network 2.2 Synergetic Avoidance Mechanism 3 Problem Formulation 4 Problem Solutions 4.1 Population Initialization 4.2 Calculation of Collision Risk 5 Simulation Results 5.1 The Efficiency of Improved Genetic Algorithms 5.2 Simulation Collision 6 Conclusion ReferencesA Weighted Fusion Method for UAV Hyperspectral Image Splicing Abstract 1 1 Introduction 2 2 Image Fusion 2.1 Average Fusion 2.2 Weighted Average Fusion 3 3 Experimental Results and Analysis 4 4 Conclusion Acknowledgements ReferencesHyperspectral Target Detection Based on Spectral Weighting Abstract 1 1 Introduction 2 2 Spectral Weighting Method 2.1 Statistic Characteristics Analysis of Hyperspectral Data 2.2 Estimation of Weighting Coefficients Based on Spectral Separability Criterion 2.3 Spectral Weighting Target Detection Algorithms 3 3 Experimental Analysis 4 4 Conclusion Acknowledgements ReferencesA Framework for Analysis of Non-functional Properties of AADL Model Based on PNML Abstract 1 Introduction 2 Framework Overview 3 Unified Model Transformation 4 Conclusion Acknowledgements ReferencesA Golden Section Method for Univariate One-Dimensional Maximum Likelihood Parameter Estimation 1 Introduction 2 Maximum Likelihood Parameter Estimation 3 Gradient Algorithm and One-Dimensional Search Algorithm 3.1 MLE Based on Gradient Descent Method 3.2 MLE Based on Linear Search 4 Simulation Results 4.1 Linear Dynamic System Model 4.2 Nonlinear Dynamic System Model 5 Conclusions ReferencesNetwork Service Analysis Based on Feature Selection Using Improved Linear Mixed Model 1 Introduction 2 Background and Related Work 2.1 Causal Inference 2.2 Linear Mixed Model 2.3 Parameter Estimation 3 Feature Selection Method Based on Improved LMM Algorithm 4 Dataset 5 Experiment 5.1 Feature Selection and QoE Prediction 5.2 Different QoEs Prediction 6 Discussion 6.1 Feature Selection Effect and Prediction Performance 7 Conclusion ReferencesSFSSD: Shallow Feature Fusion Single Shot Multibox Detector 1 Introduction 2 Related Work 3 SFSSD 3.1 The Novel SFSSD Architecture 3.2 Feature Fusion Module 4 Experiments 4.1 Results on PASCAL VOC 5 Conclusion and Future Work ReferencesBeamforming Based on Energy State Feedback for Simultaneous Wireless Information and Power Transmission Abstract 1 Introduction 2 The Model of Multi-user Simultaneous Wireless Information and Power Transmission System Based on Power Splitting 3 Beamforming Optimization Problem 4 Beamforming Optimization Method for Simultaneous Wireless Information and Power Transmission Based on Node Power State Feedback 5 Simulation and Numerical Analysis 6 Conclusion ReferencesResearch on Cross-Chain Technology Architecture System Based on Blockchain 1 Introduction 2 Difficulties in Cross-Chain 3 Architecture and Analysis of Cross-Chain 4 Conclusion ReferencesResearch on Data Protection Architecture Based on Block Chain 1 Introduction 2 Data Protection Architecture Based on Block Chain 3 Analysis of Data Protection Architecture Based on Block Chain 4 Conclusion ReferencesResearch on Active Dynamic Trusted Migration Scheme for VM-vTPCM Abstract 1 Introduction 2 Related Work 2.1 Active Immune Trusted Computing 2.2 VM-vTPM Migration Based on TPM 3 Security Issues and Requirements 4 Active Trusted Migration Scheme Based on AITC 4.1 Active Dynamic Trusted Migration Architecture 4.2 Active Dynamic Trusted Migration Protocol 4.2.1 Pre-Migration Preparation Phase 4.2.2 VM-vTPCM Data Migration Phase 5 Experiment 6 Conclusion Acknowledgements ReferencesMultiple Hybrid Strategies Filtrate Localization Based on FM for Wireless Sensor Networks Abstract 1 1 Introduction 2 2 FMFL Algorithm 2.1 Position Calibration Based on FM 2.2 FMFL Algorithm 3 3 Analysis of the Simulation 3.1 Experimental Environment Settings 3.2 Performance Analysis 4 4 Conclusion ReferencesLocalization Algorithm Based on FM for Mobile Wireless Sensor Networks Abstract 1 FM-MCL Algorithm 2 Performance Analysis 2.1 Anchors Heard 2.2 Motion Speed 3 Conclusion ReferencesCoherent State Based Quantum Optical Communication with Mature Classical Infrastructure Abstract 1 Introduction 2 Security of Coherent State Based QOC 3 Secret Key Rate with Discrete Modulation 4 Conclusions Acknowledgement ReferencesDesign of Codebook for High Overload SCMA Abstract 1 1 Introduction 2 2 SCMA Model Analysis 3 3 SCMA Codebook Design and Simulation Results 3.1 Phase Rotation Optimization Program 3.2 Modeled on LDPC Coding Design 4 4 Conclusion Acknowledgements ReferencesA Spectrum Allocation Scheme Based on Power Control in Cognitive Satellite Communication Abstract 1 Introduction 2 Scenario Introduction and Signal and Interference Model Description 2.1 Scenario Introduction 2.2 Signal and Interference Model 3 Joint Power and Carrier Allocation Mechanisms (JPCA) 3.1 Power Control 3.2 Joint Carrier and Power Allocation 3.2.1 Independent Carrier Allocation in Two Bands 3.2.2 Carrier Allocation Combining Two Bands 4 Performance Evaluation 5 Conclusion Acknowledgements ReferencesFruit Classification Through Deep Learning: A Convolutional Neural Network Approach Abstract 1 Introduction 2 Dataset and Materials 3 Proposed Architecture 3.1 CNN Component 3.1.1 Convolutional Layer 3.1.2 ReLU Layer 3.1.3 Pooling Layer 3.1.4 Dropout 3.1.5 Softmax 3.2 CNN Learning Algorithm 4 Results and Discussion 5 Conclusion Acknowledgements ReferencesCorrection to: Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos Correction to: Chapter “Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos” in: Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 796–810, 2020 https://doi.org/10.1007/978-981-13-9409-6_94Author Index*

Lecture Notes in Electrical Engineering 571

Qilian Liang Wei Wang Xin Liu Zhenyu Na Min Jia Baoju Zhang Editors

Communications, Signal Processing, and Systems Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems

Lecture Notes in Electrical Engineering Volume 571

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 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 & 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 Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Trafﬁc Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & 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 Junjie James Zhang, Charlotte, NC, USA

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Qilian Liang Wei Wang Xin Liu Zhenyu Na Min Jia Baoju Zhang •

•

•

•

•

Editors

Communications, Signal Processing, and Systems Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems

123

Editors Qilian Liang Department of Electrical Engineering University of Texas at Arlington Arlington, TX, USA Xin Liu School of Information and Communication Engineering Dalian University of Technology Dalian, Liaoning, China Min Jia School of Electronics and Information Engineering Harbin Institute of Technology Harbin, China

Wei Wang College of Electronic and Communication Engineering Tianjin Normal University Tianjin, China Zhenyu Na School of Information Science and Technology Dalian Maritime University Dalian, Liaoning, China Baoju Zhang College of Physical and Electronic Information Tianjin Normal University Tianjin, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-13-9408-9 ISBN 978-981-13-9409-6 (eBook) https://doi.org/10.1007/978-981-13-9409-6 © Springer Nature Singapore Pte Ltd. 2020, corrected publication 2020 This work is subject to copyright. All rights are reserved 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

Contents

Flame Detection Method Based on Feature Recognition . . . . . . . . . . . Ti Han, Changyun Ge, Shanshan Li, and Xinqiang Zhang

1

Small Cell Deployment Based on Energy Efﬁciency in Heterogeneous Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yinghui Zhang, Shuang Ning, Haiming Wang, Jing Gao, and Yang Liu

9

Research on Knowledge Mining Algorithm of Spacecraft Fault Diagnosis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianbing Huang, Wenshuo Cai, Guoliang Tian, Liling Li, and Guisong Yin

20

Performance Analysis of SSK in AF Relay over Transmit Correlated Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiyishu Li, Yaping Hu, and Xiangbin Yu

28

The JSCC Algorithm Based on Unequal Error Protection for H.264 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiarui Han, Jiamei Chen, Yao Wang, Ying Liu, Yang Zhang, and Liang Qiao Mean-Field Power Allocation for UDN . . . . . . . . . . . . . . . . . . . . . . . . Yanwen Wang, Jiamei Chen, Yao Wang, Qianyu Liu, and Yuying Zhao

35

42

Design of Gas Turbine State Data Acquisition Instrument Based on EEMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhonglin Wei, Pengyuan Liu, Feng Wang, and Tianhui Wang

48

Cramér–Rao Bound Analysis for Joint Estimation of Target Position and Velocity in Hybrid Active and Passive Radar Networks . . . . . . . . Chenguang Shi, Wei Qiu, Fei Wang, and Jianjiang Zhou

56

A Hinged Fiber Grating Sensor for Hull Roll and Pitch Motion Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Wang, Libo Qiao, Yuliang Li, Jingping Yang, and Chuanqi Liu

66

v

vi

Contents

Natural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunxue Shao and Hongyu Suo

75

Coverage Probability Analysis of D2D Communication Based on Stochastic Geometry Model . . . . . . . . . . . . . . . . . . . . . . . . . Xuan-An Song, Hui Li, Zhen Guo, and Xian-Peng Wang

83

Study of Fault Pattern Recognition for Spacecraft Based on DTW Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoliang Tian, Lianbing Huang, and Guisong Yin

94

A Joint TDOA/AOA Three-Dimensional Localization Algorithm for Spacecraft Internal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yin Long, Ke Zhu, and Cai Huang

103

A Study on Lunar Surface Environment Long-Term Unmanned Monitoring System by Using Wireless Sensor Network . . . . . . . . . . . . Yin Long and Zhao Cheng

110

A Study on Automatic Power Control Method Applied in Astronaut Extravehicular Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yin Long, Pei Guo, and Yusheng Yi

115

Design of EVA Communications Method for Anti-multipath and Full-Range Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yin Long, Kewu Huang, and Xin Qi

121

High Accurate and Efﬁcient Image Retrieval Method Using Semantics for Visual Indoor Positioning . . . . . . . . . . . . . . . . . . . . . . . Jin Dai, Lin Ma, Danyang Qin, and XueZhi Tan

128

Massive MIMO Channel Estimation via Generalized Approximate Message Passing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muye Li, Xudong Han, Weile Zhang, and Shun Zhang

137

Study of Key Technological Performance Parameters of Carbon-Fiber Infrared Heating Cage . . . . . . . . . . . . . . . . . . . . . . . . Fei Xu, Yan Xia, Guoqing Liu, Yuzhong Li, Jinming Chen, and Chun Liu

145

Research on Switching Power Supply Based on Soft Switching Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhihong Zhang and Hong He

156

Grid Adaptive DOA Estimation Method in Monostatic MIMO Radar Using Sparse Bayesian Learning . . . . . . . . . . . . . . . . . . . . . . . . Yue Wang, Kangyong You, Dan Wang, and Wenbin Guo

165

Contents

Global Deep Feature Representation for Person Re-Identiﬁcation . . . . Meixia Fu, Songlin Sun, Na Chen, Xiaoyun Tong, Xifang Wu, Zhongjie Huang, and Kaili Ni

vii

179

Hybrid Precoding Based on Phase Extraction for Partially-Connected mmWave MIMO Systems . . . . . . . . . . . . . . . . Mingyang Cui, Weixia Zou, and Ran Zhang

187

Research on the Fusion of Warning Radar and Secondary Radar Intelligence Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinliang Dong, Yumeng Zhang, Baozhou Du, and Xiaoyan Zhang

196

Antenna Array Design for Directional Modulation . . . . . . . . . . . . . . . Bo Zhang, Wei Liu, and Cheng Wang Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xudong Han, Shun Zhang, Anteneh Mohammed, Weile Zhang, Nan Zhao, and Yuantao Gu An On-Line EMC Test System for Liquid Flow Meters . . . . . . . . . . . . Haijiao An, Xin Shi, and Xigang Wang Research on Kinematic Simulation for Space Mirrors Positioning 6DOF Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Yalin, Liang Fengchao, He Haiyan, Wang Chun, Tan Shuang, and Lin Zhe

206

214

223

231

A Dictionary Learning-Based Off-Grid DOA Estimation Method Using Khatri-Rao Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weijie Tan, Chenglin Zheng, Judong Li, Weiqiang Tan, and Chunguo Li

239

Radar Adaptive Sidelobe Cancellation Technique Based on Spatial Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yumeng Zhang, Jinliang Dong, and Huifang Dong

249

On the Spectral Efﬁciency of Multiuser Massive MIMO with Zero-Forcing Precoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chenglin Zheng, Weijie Tan, and Yazhen Chen

259

A Signal Sorting Algorithm Based on LOF De-Noised Clustering . . . . Zhenyuan Ji, Yan Bu, and Yun Zhang

268

Design of a Small-Angle Reﬂector for Shadowless Illumination . . . . . . Guangzhen Wang

276

Anti-interference Communication Algorithm Based on Wideband Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minti Liu, Chunling Liu, Ran Zhang, and Yuanming Ding

284

viii

Contents

A Multi-task Dynamic Compressed Sensing Algorithm for Streaming Signals Eliminating Blocking Effects . . . . . . . . . . . . . . . . . . . . . . . . . . Daoguang Dong, Guosheng Rui, Wenbiao Tian, Ge Liu, Haibo Zhang, and Zhijun Yu Thunderstorm Recognition Algorithm Research Based on Simulated Airborne Weather Radar Reﬂectivity Volume Scan Data . . . . . . . . . . Rui Liao, Xu Wang, and Jianxin He FPGA-Based Fall Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Wang, Fanning Kong, and Hui Wang Artiﬁcial Intelligence and Game Theory Based Security Strategies and Application Cases for Internet of Vehicles . . . . . . . . . . . . . . . . . . Zhiyong Wang, Miao Zhang, He Xu, Guoai Xu, Chengze Li, and Zhimin Wu

294

303 314

322

The Effect of Integration Stage on Multimodal Deep Learning in Genomic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fariba Khoshghalbvash and Jean X. Gao

330

An Advanced Aerospace High Precision Spread Spectrum Ranging System Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Liu, Pingyuan Lu, and Xiaohang Ren

339

Weight-Assignment Last-Position Elimination-Based Learning Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiwei An, Chong Di, and Shenghong Li

348

Nonlinear Multi-system Interactive Positioning Algorithms . . . . . . . . . Xin-xin Ma, Ping-ke Deng, and Xiao-guang Zhang

355

Bandwidth Enhancement of Waveguide Slot Antenna Array for Satellite Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengfei Zhao, Shujie Ma, Peiyao Yang, Fan Lu, and Shasha Zhang

366

Design of an Enhanced Turbulence Detection Process Considering Aircraft Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuandan Fan, Xiaoguang Lu, Hai Li, and Renbiao Wu

373

Rain-Drop Size Distribution Case Study in Chengdu Based on 2DVD Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Liu, Debin Su, and Hongyu Lei

382

Analysis of the Inﬂuence on DPD with Memory Effect in Frequency Hopping Communication System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Lu, Shi Hairan, Gao Shujin, and Duan Jiangnian

390

Contents

FPGA-Based Implementation of Reconﬁgurable Floating-Point FIR Digital Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Zhang, Xin Wei, Bingyi Li, and He Chen High Precision Spatiotemporal Datum Design Based on Ground Observation Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufei Huang, Ji Gao, Dan Wang, Yong Liu, Zhengji Song, Jia Xu, and Lantao Liu

ix

400

408

Study on Two Types of Sensor Antennas for an Intelligent Health Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Li, Licheng Yang, Xiaonan Zhao, Bo Zhang, and Cheng Wang

415

A Fiber Bragg Grating Acceleration Sensor for Measuring Bow Slamming Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingping Yang, Wei Wang, Yuliang Li, Libo Qiao, and ChuanQi Liu

422

Improving Indoor Random Position Device-Free People Recognition Resolution Using the Composite Method of WiFi and Chirp . . . . . . . . Xiaokun Zheng, Ting Jiang, and Wenling Xue

431

Optimal Design of an S-Band Low Noise Ampliﬁer . . . . . . . . . . . . . . . Hai Wang, Zhihong Wang, Guiling Sun, Ming He, Ying Zhang, Ke Liang, and Rong Guo

439

A Triangular Centroid Location Method Based on Kalman Filter . . . . Yunfei Suo, Tao Liu, Can Lai, and Zechen Li

448

Research on Spatial Network Routing Model Based on Price Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ligang Cong, Huamin Yang, and Xiaoqiang Di

459

The TDOA and FDOA Algorithm of Communication Signal Based on Fine Classiﬁcation and Combination . . . . . . . . . . . . . . . . . . . Chi Zhang

469

An Adaptive DFT-Based Channel Estimation Method for MIMO-OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao Deng and Xiao Ming Wu

479

A Novel Gradient L0-Norm Regularization Image Restoration Method Based on Non-local Total Variation . . . . . . . . . . . . . . . . . . . . Mingzhu Shi

487

Study on Interference from 5G System to Earth Exploration Satellite Service System in High Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Wang, Baoju Zhang, and Wei Wang

494

Sparse Planar Antenna Array Design for Directional Modulation . . . . Bo Zhang, Wei Liu, Yang Li, Xiaonan Zhao, and Cheng Wang

503

x

Contents

Research on the Linear Interpolation of Equal-Interval Fractional Delay Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shen Zhao, Yunwei Zhang, XiWei Guo, and Deliang Liu Single-Channel Grayscale Processing Algorithm for Transmission Tissue Images Based on Heterogeneity Detection . . . . . . . . . . . . . . . . . Baoju Zhang, Chengcheng Zhang, Gang Li, Ling Lin, Cuiping Zhang, and Fengjuan Wang Handwriting Numerals Recognition Using Convolutional Neural Network Implemented on NVIDIA’s Jetson Nano . . . . . . . . . . . . . . . . Huan Chen, Songyan Liu, Haining Zhang, and Wang Cheng Implementation of Image Recognition on Embedded Systems . . . . . . . Haining Zhang, Songyan Liu, Huan Chen, and Wang Cheng

512

520

529 536

A Precise 3-D Wireless Localization Technique Using Smart Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuang Feng, Desheng Chi, Jingyu Dai, and Xiaorong Zhu

544

A Two-Phase Fault Diagnosis Algorithm Based on Convolutional Neural Network for Heterogeneous Wireless . . . . . . . . . . . . . . . . . . . . Yong Wang, Lei Zhang, and Xiarong Zhu

555

A Wireless Power Transfer System with Switching Circuit of Power Grid and Solar Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ze Song, Xin Zhang, Xiu Zhang, Ruiqing Xing, and Lei Wang

564

A Fiber Bragg Grating Stress Sensor for Hull Local Strength Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuanqi Liu, Wei Wang, Yuliang Li, Libo Qiao, and Jingping Yang

572

Direct Wave Parameters Estimation of Passive Bistatic Radar Based on Uncooperative Phased Array Radar . . . . . . . . . . . . . . . . . . . Jiameng Pan, Panhe Hu, Qian Zhu, and Qinglong Bao

579

Noncooperative Radar Illuminator Based Bistatic Receiving System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caisheng Zhang, Hai Zhang, and Xiaolong Chen

588

Research on Simulation Technology for Remote Sensing Image Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hezhi Sun, Yugao Li, Xiao Mei, Yuting Gao, and Dong Yang

596

Distributed Measurement of Micro-vibration and Analysis of the Inﬂuence on Imaging Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . Yugao Li, Hezhi Sun, Chen Ni, Xiang Li, and Dong Yang

605

Contents

Analysis and Veriﬁcation of the Effect of Space Debris on the Output Power Decline of Solar Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enzhu Bao, Li Ma, Peng Tian, Linchun Fu, and Shijie Chen A New Nonlinear Method for Calculating the Error of Passive Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuncheng Tan, Guohong Wang, Chengbin Guan, Hongbo Yu, Siwen Li, and Qian Cao A Static Method for Stack Overﬂow Detection Based on SPARC V8 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Zhang, Rui Zhang, Ruijun Li, Yanfang Fan, and Hongjing Cheng Enhanced Double Threshold Based Energy Detection . . . . . . . . . . . . . Omar Aitmesbah and Zhuoming Li

xi

614

622

629 638

Self-generating Topology Coloring Scheduling for Interference Mitigation in Wireless Body Area Networks . . . . . . . . . . . . . . . . . . . . Jiasong Mu, Yunna Wei, and Xiaorun Yang

646

Smart Parking and Recommendation System Under Fog Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiasong Mu, Yunna Wei, and Xiaorun Yang

654

Speech Synthesis Method Based on Tacotron + WaveNet . . . . . . . . . . Yingnan Liu, Qitao Ma, and Yingli Wang

662

A Novel Spatial Domain Based Steganography Scheme Against Digital Image Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Hui and Quan Zhou

671

Losen: An Accurate Indoor Localization System by Integrating CSI of Wireless Signal and MEMS Sensors . . . . . . . . . . . . . . . . . . . . . . . . Zengshan Tian, Linxiao Xie, Ze Li, and Mu Zhou

679

A Direct Target Recognition Algorithm for Low-Resolution Radar with Unbalanced Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kefan Zhu, Jiegui Wang, and Miao Wang

688

DFT-Spread Based PAPR Reduction of OFDM for Short Reach Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yupeng Li, Yaqi Wang, and Longwei Wang

696

Underdetermined Mixed Matrix Estimation of Single Source Point Detection Based on Noise Threshold Eigenvalue Decomposition . . . . . Miao Wang, Xiao-xia Cai, and Ke-fan Zhu

704

Optimization of APTEEN Routing Protocol for Wireless Sensor Networks Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Minghao Wang, Shubin Wang, and Bowen Zhang

712

xii

Contents

Optimization of APTEEN Routing Protocol in Wireless Sensor Networks Based on Particle Swarm Optimization . . . . . . . . . . . . . . . . Bowen Zhang, Shubin Wang, and Minghao Wang Research Status of Wireless Power Transmission Technology . . . . . . . Xudong Wang, Changbo Lu, Feng Wang, Wanli Xu, and Shizhan Li

722 731

Flexible Sparse Representation Based Inverse Synthetic Aperture Radar Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Wang, Guoan Bi, and Xianpeng Wang

739

Localization Schemes for 2-D Molecular Communication via Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shenghan Liu, Shijian Bao, and Chenglin Zhao

749

Research on Support Vector Machine in Estimating Source Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoli Zhang, Jiaqi Zhen, and Baoyu Guo

757

Wireless Electricity Transmission Design of Unmanned Aerial Vehicle Charging Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yashuo He, Jingjing Wu, Sumeng Shi, Ze Song, Qijing Qiao, and Cheng Wang

762

An ITD-Based Method for Individual Recognition of Secondary Radar Radiation Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianqi Li, Yu Zhang, and Xiaojing Yang

769

Gaussian Mixture Model Based Multi-region Blood Vessel Segmentation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaqing Fu, Maolin Wang, and Ting Liu

778

Research on the Enhancement of VANET Coverage Based on UAV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianci Liu, Lixin Zhao, Bin Li, and Chenglin Zhao

787

Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiyuan Li, Aiping Jiang, and Yuying Mu

796

A Design of Satellite Telemetry Acquisition System . . . . . . . . . . . . . . . Meishan Chen, Qiang Mu, Jinyuan Ma, and Xin Li

811

Fingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongguang Li, Ying Guo, Zisen Qi, Ping Sui, and Linghua Su

819

Integrated Design of High Speed Uplink and Emergency Telemetry and Control for LEO Satellite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Mu, Hongwei Shi, Jinyuan Ma, and Meishan Chen

832

Contents

Imaging Correction Based on AIS for Moving Vessels in Spaceborne SAR Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuting Gao, Guangjun He, Tao Zhang, Dongqiang Zhou, Dong Yang, and Jindong Li Research on Flying Catkins Detection and Removal in Target Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hualin Liu, Haipeng Wang, Limin Zhang, and Xueteng Li Robust Context-Aware Tracking with Temporal Regularization . . . . . Tianhao Li, Tingfa Xu, Yu Bai, Axin Fan, and Ruoling Yang

xiii

840

848 858

Research on Motor Speed Estimation Method Based on Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jian He and Bo Li

866

A Novel Virtual Cell Power Allocation and Interference Merging Algorithm in UDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liting Song, Weidong Gao, Gang Chuai, and ZiWei Si

877

Device-Free Sensing for Gesture Recognition by Wi-Fi Communication Signal Based on Auto-encoder/decoder Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Zhong, Yan Huang, and Ting Jiang Detection of Sleep Apnea Based on Cardiopulmonary Coupling . . . . . Haojing Zhang, Weidong Gao, and Peizhi Liu Study on a Space-Air-Ground Integrated Data Link Networks Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia Guo, Shasha Zhang, Fan Lu, Jingshuang Cheng, Yuanqing Zhao, and Nuo Xu Similar Cluster Based Continuous Bag-of-Words for Word Vector Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weikai Sun, Yinghua Ma, Shenghong Li, and Shiyi Zhang Research on Integrated Waveform of FDA Radar and Communication Based on Linear Frequency Offsets . . . . . . . . . . . Lin Zhang, Kefei Liao, Shan Ouyang, Yuan Ma, Jingjing Li, Ningbo Xie, and Gaojian Huang Research on Parameter Conﬁguration of Deep Neural Network Applied on Speech Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyu Zhan, Yongjing Ni, and Ting Jiang Mid-Infrared Characteristic Analysis of Stability Index of Vehicle Gasoline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianling Ren, Hongjian Li, Lei Guo, Deyan Wang, Jianping Song, and Xin Hu

887 895

904

911

919

927

936

xiv

Contents

Application of Mid-Infrared Characteristic Analysis Technology in Gasoline Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianling Ren, Jianping Song, Hongjian Li, Caichao Deng, Lei Guo, and Xin Hu A Generalized Sampling Based Method for Digital Predistortion of RF Power Ampliﬁers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ke Li and Hairan Shi Optimum Design of Intersatellite Link Based on STK . . . . . . . . . . . . . Guanghua Zhang, Jian Li, Jingqiu Ren, and Weidang Lu

943

953 960

Integrated Detection and Tracking in Asynchronous Moving Radar Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinhui Dai, Junkun Yan, Penghui Wang, and Hongwei Liu

969

Fault-Tolerant Decompression Method of Compressed Chinese Text Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuyang Wang, Xiaoqun Zhao, and Digang Wang

977

Classiﬁcation of Human Motion Status Using UWB Radar Based on Decision Tree Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoqing Wang and Zhengliang Zhu

985

A Sub-aperture Division Method for FMCW CSAR Imaging . . . . . . . Depeng Song, Binbing Li, Yi Qu, Yijun Chen, and Heng Wang

993

An Experimental Study of Sea Target Detection of Passive Bistatic Radar Based on Non-cooperative Radar Illuminators . . . . . . . . . . . . . 1002 Jie Song, Guo-qing Wang, and Xiao-long Chen Design of a Quasi-Real-Time Communication System for LEO Satellites Using Beidou Short-Message Service . . . . . . . . . . . . . . . . . . . 1010 Fan Lu, Jingshuang Cheng, Shasha Zhang, Ning Liu, and Hongjie Zhang A Physically Decoupled Onboard Control Plane for Software Deﬁned LEO Constellation Network . . . . . . . . . . . . . . . . . . . . . . . . . . 1019 Peicong Wu, Kanglian Zhao, Wenfeng Li, Zhifeng Liu, and Zhenming Sun A Dynamic Programming Based TBD Algorithm for Near Space Targets Under Range Ambiguity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029 Hongbo Yu, Shuncheng Tan, Qian Cao, Xiangyu Zhang, Lin Li, and Qiang Guo Research and Design of Home Care System of Internet of Things Based on Wireless Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038 Xiaoguang Su, Xiangyu Zhao, Lili Yu, Jingyuan Jia, and Zhian Deng

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xv

Design of Wind Pendulum Control System Based on STM32F407 . . . . 1045 Hai Wang, Zhihong Wang, Guiling Sun, Ming He, Ying Zhang, Ke Liang, and Rong Guo A High-Speed Parallel Accessing Scheduler of Space-Borne Nand Flash Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055 Xin Li, Ji-Yang Yu, Ke Li, Mei-Shan Chen, and Jin-Yuan Ma Two Dimensional Joint ISAR Imaging Algorithm Based on Matrix Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 Jian-fei Ren, Le Kang, Xiao-fei Lu, Yijun Chen, and Ying Luo The Satellite GPS Antenna In-Orbit Phase Center Calibration Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 Ning Liu and Yufei Huang Migrating Target Detection Under Spiky Clutter Background . . . . . . . 1080 Zhiyong Niu, Tao Su, Jibin Zheng, and Wentong Li A Novel Range Super-Resolution Algorithm for UAV Swarm Target Based on LFMCW Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 Tianyuan Yang, Tao Su, and Jibin Zheng An Improved PDR/WiFi Integration Method for Indoor Pedestrian Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1096 Boyuan Wang, Xuelin Liu, Baoguo Yu, Ruicai Jia, Lu Huang, and Haonan Jia An Adaptive Radar Resource Scheduling Algorithm for ISAR Imaging Based on Step-Frequency Chirp Signal Optimization . . . . . . . 1104 Yijun Chen, Ying Luo, Yi Qu, and Hao Lou A Task-Dependent Flight Plan Conﬂict Risk Assessment Method for General Aviation Operation Airspace . . . . . . . . . . . . . . . . . . . . . . . 1112 Zhe Zhang, Li An, Xiaoliang Wang, Peng Wang, Ping Han, and Renbiao Wu A Uniform Model for Conﬂict Prediction and Airspace Safety Assessment for Free Flight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1120 Zhe Zhang, Li An, Peng Wang, Xiaoliang Wang, and Renbiao Wu Optimization of Power Allocation for Full Duplex Relay-Assisted D2D Communication Underlaying Wireless Cellular Networks . . . . . . 1128 Ranran Zhou and Liang Han Scene Text Recognition Based on Deep Learning . . . . . . . . . . . . . . . . . 1136 Yunxue Shao and Yuxin Chen

xvi

Contents

Spectrum Sensing Algorithm Based on Twin Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 Xiaorong Wang, Zili Wang, Dongyang Guo, and Huiling Zhou Applicability Analysis of Plane Wave and Spherical Wave Model in Blue and Green Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1153 Songlang Li, Zhongyang Mao, Chuanhui Liu, and Min Liu A Study of the Inﬂuence of Resonant Frequency in Wireless Power Transmission System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1162 Xiaohui Lu, Xiu Zhang, Ruiqing Xing, Xin Zhang, Yupeng Li, and Liang Han Direction of Arrival Estimation Based on Support Vector Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169 Baoyu Guo, Jiaqi Zhen, and Xiaoli Zhang Bistatic ISAR Radar Imaging Using Missing Data Based on Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174 Luhong Fan, Zongjie Cao, Jin Li, Rui Min, and Zongyong Cui Medical Images Segmentation Using a Novel Level Set Model with Laplace Kernel Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1186 Jianhua Song, Zhe Zhang, and Jiaqi Zhen Research on Multi-UAV Routing Simulation Based on Unity3d . . . . . . 1190 Cong Chen, Yanting Liu, Fusheng Dai, Yong Li, Weidang Lu, and Bo Li Video Target Tracking Based on Adaptive Kalman Filter . . . . . . . . . . 1198 Futong He, Jiaqi Zhen, and Zhifang Wang Compressed Sensing Image Reconstruction Method Based on Chaotic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1202 Yaqin Xie, Erfu Wang, Jiayin Yu, Shiyu Guo, and Xiaomin Zhang An Underdetermined Blind Source Separation Algorithm Based on Variational Mode Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 1210 Shiyu Guo, Erfu Wang, Jiayin Yu, Yaqin Xie, and Xiaomin Zhang A Ranking Learning Training Method Based on Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218 Yulong Lai and Jiaqi Zhen Research on Temperature Characteristics of IoT Chip Hardware Trojan Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222 Junru An, Zhiwei Cui, Zhenhui Zhang, Liji Wu, and Xiangmin Zhang

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xvii

Wireless Communication Intelligent Voice Height Measurement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231 Danfeng Zhao and Peidong Zhuang Design of Intelligent Classiﬁcation Waste Bin with Detection Technology in Fog and Haze Weather . . . . . . . . . . . . . . . . . . . . . . . . . 1241 Ailing Zhang, Peidong Zhuang, Yuehua Shi, and Danfeng Zhao A False-Target Jamming Method for the Phase Array Multibeam Radar Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1250 Liu Tao, Zong Siguang, Tian Shusen, and Peng Pei Analysis of TDOA Location Algorithm Based on Ultra-Wideband . . . 1257 Wenquan Li and Bing Zhao Algorithm Design of Combined Gaussian Pulse . . . . . . . . . . . . . . . . . . 1262 Xunchen Jia and Bing Zhao A Network Adapter for Computing Process Node in Decentralized Building Automation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1267 Liang Zhao, Zexin Zhang, Tianyi Zhao, and Jili Zhang Model Reference Adaptive Control Application in Optical Path Scanning Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1272 Lanjie Guo, Hao Wang, Wenpo Ma, and Chun Wang UAV Path Planning Design Based on Deep Learning . . . . . . . . . . . . . 1280 Song Chang, Ziyan Jia, Yang Yu, Weige Tao, and Xiaojie Liu Research on Temperature and Infrared Characteristics of Space Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1289 Xiang Li and Jindong Li A Multispectral Image Edge Detection Algorithm Based on Improved Canny Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1298 Baoju Zhang, FengJuan Wang, Gang Li, CuiPing Zhang, and ChengCheng Zhang A Green and High Efﬁcient Architecture for Ground Information Port with SDN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1308 Peng Qin, Jianming Li, Xiaohong Xue, Hongmei Zhang, Chang Jiang, and Yunlong Wang Marked Watershed Algorithm Combined with Morphological Preprocessing Based Segmentation of Adherent Spores . . . . . . . . . . . . 1316 Jiaying Wang, Yaochi Zhao, Yu Wang, Wei Chen, Hui Li, Yugui Han, and Zhuhua Hu

xviii

Contents

Data Storage Method for Fast Retrieval in IoT . . . . . . . . . . . . . . . . . . 1324 Juan Chen, Lihua Yin, Tianle Zhang, Yan Liu, and Zhian Deng Equivalence Checking Between System-Level Descriptions by Identifying Potential Cut-Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 1328 Jian Hu, Guanwu Wang, Guilin Chen, Yun Kang, Long Wang, and Jian Ouyang An Improved Adversarial Neural Network Encryption Algorithm Against the Chosen-Cipher Text Attack (CCA) . . . . . . . . . . . . . . . . . . 1336 Yingli Wang, Haiting Liu, Hongbin Ma, and Wei Zhuang Hardware Implementation Based on Contact IC Card Scalar Multiplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1344 Feng Liang, Yanzhao Yin, Zhenhui Zhang, Liji Wu, and Xiangmin Zhang Tiered Spectrum Allocation for General Heterogeneous Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353 Haichao Wei, Anliang liu, and Na Deng Human Action Recognition Algorithm Based on 3D DenseNet-BC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1361 Yujiao Cui, Yong Zhu, Jun Li, Luguang Wang, and Chuanbo Wang Color Image Encryption Based on Principal Component Analysis . . . . 1368 Xin Huang, Xinyue Tang, and Qun Ding Research on Transmitter of the Somatosensory Hand Gesture Recognition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1376 Fei Gao, Jiyou Fei, Hua Li, Xiaodong Liu, and Ti Han Research on Image Retrieval Based on Wavelet Denoising in Visual Indoor Positioning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 1385 Zhonghong Wang, Guoqiang Wang, and Guoying Zhang Analysis of the Matching Pursuit Reconstruction Algorithm Based on Compression Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1392 Zhihong Wang, Hai Wang, Guiling Sun, and Yangyang Li Super-Resolution Based and Topological Structure for Narrow Road Extraction from Remote Sensing Image . . . . . . . . . . . . . . . . . . . . . . . . 1402 Guoying Zhang, Guoqiang Wang, and Zhonghong Wang Evaluation on Learning Strategies for Multimodal Ground-Based Cloud Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1411 Shuang Liu, Mei Li, Zhong Zhang, and Xiaozhong Cao SAR Load Comprehensive Testing Technology Based on Echo Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1418 Zhiya Hao, Zhongjiang Yu, Kui Peng, Linna Ni, and Yinhui Xu

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A New Trafﬁc Priority Aware and Energy Efﬁcient Protocol for WBANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1429 Wei Wang, Dunqiang Lu, Xin Zhou, Baoju Zhang, Jiasong Mu, and Yuanyuan Li Design of Modulation and Demodulation System Based on Full Digital Phase-Locked Loop . . . . . . . . . . . . . . . . . . . . . . . . . . . 1438 Hongli Zhu and Jin Chen Ethanol Gas Sensor Based on SnO2 Hierarchical Nanostructure . . . . . 1445 Ming Zhu, Yongxiang Pi, and Huijun Zhang Generative Model for Person Re-Identiﬁcation: A Review . . . . . . . . . . 1450 Zhong Zhang, Tongzhen Si, and Shuang Liu Location Fingerprint Indoor Positioning Based on XGBoost . . . . . . . . 1457 Hongbin Ma, Yanlong Ma, Yingli Wang, Xiaojie Xu, and Wei Zhuang An Information Hiding Algorithm for Iris Features . . . . . . . . . . . . . . . 1465 Jiahui Feng, Hongbin Ma, Qitao Ma, Yingli Wang, Haiting Liu, and Hong Chen Thin Film Transistor of CZ-PT Applied to Sensor . . . . . . . . . . . . . . . 1474 Yongxiang Pi, Ming Zhu, and Huijun Zhang An Image Dehazing Algorithm Based on Single-Scale Retinex and Homomorphic Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1482 Hong Wu and Zhiwei Tan Survey of Gear Fault Feature Extraction Methods Based on Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1494 Hong Wu and Can Wang Hyperspectral Image Classiﬁcation Based on Bidirectional Gated Recurrent Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1505 Yong Liu, Hongchang He, Xiaofei Wang, Yu Wang, and Runxing Chen A Survey of Pedestrian Detection Based on Deep Learning . . . . . . . . . 1511 Runxing Chen, Xiaofei Wang, Yong Liu, Sen Wang, and Shuo Huang Detection of Anomaly Signal with Low Power Spectrum Density Based on Power Information Entropy . . . . . . . . . . . . . . . . . . . . . . . . . 1517 Shaolin Ma, Zhuo Sun, Anhao Ye, Suyu Huang, and Xu Zhang A Hybrid Multiple Access Scheme in Wireless Powered Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1528 Yue Liu, Zhenyu Na, Anliang Liu, and Zhian Deng Gas Sensing Properties of Molecular Sieve Modiﬁed 3DIO ZnO to Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1533 Fangxu Shen, Xinping He, Xiu Zhang, Hefei Gao, and Ruiqing Xing

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Contents

FiberEUse: A Funded Project Towards the Reuse of the End-of-Life Fiber Reinforced Composites with Nondestructive Inspection . . . . . . . 1541 Yijun Yan, Andrew Young, Jinchang Ren, James Windmill, Winifred L. Ijomah, and Tariq Durrani Autonomous Mission Planning and Scheduling Strategy for Data Transmission of Deep-Space Missions . . . . . . . . . . . . . . . . . . . . . . . . . 1548 Jionghui Li, Liying Zhu, Shi Liu, Xiongwen He, and Xiaofeng Zhang Preparation of TiO2 Nanotube Array Photoanode and Its Application in Three-Dimensional DSSC . . . . . . . . . . . . . . . . . . . . . . . 1558 Zhiwei Cui, J. R. An, and Y. W. Dou Block-Based Data Security Storage Scheme . . . . . . . . . . . . . . . . . . . . . 1567 Yina Wang, Hongbin Ma, Qitao Ma, Hong Chen, Dongdong Zhang, and Yingli Wang Chaos Synchronization and Voice Encryption of Discretized Hyperchaotic Chen Based on Euler Algorithm . . . . . . . . . . . . . . . . . . 1576 Xinyue Tang, Jiaqi Zhen, Qun Ding, Bing Zhao, and Jie Yang Multiple UAV Assisted Cellular Network: Localization and Access Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1581 Yiwen Tao, Qingyue Zhang, Bin Li, and Chenglin Zhao WiFi Location Fingerprint Indoor Positioning Method Based on WKNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1589 Xinxin Wang, Danyang Qin, and Lin Ma The Digital Design and Veriﬁcation of Overall Power System for Spacecraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1597 Ning Xia, Qing Du, Zhigang Liu, Xiaofeng Zhang, and Yan Chen The Analysis and Practice of Backup Spacecraft Tele Command Based on Chang’E-4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1605 Xiaoguang Li, Xiaohu Shen, Mei Yang, and Shi Liu A Modiﬁed Hough Transform TBD Method for Radar Weak Targets Using Plot’s Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1611 Bao Zhonghua, Tian Shusen, and Lu Jianbin Analysis of the Effects of Climate Teleconnections on Precipitation in the Tianshan Mountains Using Time-Frequency Methods . . . . . . . . 1620 Baoju Zhang, Lixing An, Yonghong Hao, and Tian-Chyi Jim Yeh An Improved Cyclic Spectral Algorithm Based on Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 Jurong Hu, Ying Tian, Yu Zhang, and Xujie Li

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Video Deblocking for KMV-Cast Transmission Based on CNN Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1639 Yingchun Yuan and Qifei Lu Improved YOLO Algorithm for Object Detection in Trafﬁc Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1647 Qifei Lu and Yingchun Yuan Task Allocation for Multi-target ISAR Imaging in Bi-Static Radar Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1656 Dan Wang, Jia Liang, Qun Zhang, and Feng Zhu A New Tracking Algorithm for Maneuvering Targets . . . . . . . . . . . . . 1666 Jurong Hu, Yixiang Zhu, Hanyu Zhou, Ying Tian, and Xujie Li Research on an Improved SVM Training Algorithm . . . . . . . . . . . . . . 1674 Pan Feng, Danyang Qin, Ping Ji, Min Zhao, Ruolin Guo, Guangchao Xu, and Lin Ma Modeling for Coastal Communications Based on Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1681 Yanli Xu Research of Space Power System MPPT Topology and Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1689 Qing Du, Ning Xia, Bo Cui, Zhigang Liu, Yi Yang, Hao Mu, and Yi Zeng Far-Field Sources Localization Based on Fourth-Order Cumulants Matrix Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1697 Heping Shi, Zhiwei Guan, Lizhu Zhang, and Ning Ma ONENET-Based Greenhouse Remote Monitoring and Control System for Greenhouse Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 1703 Wei-tao Qian, Jiaqi Zhen, and Tao-tao Shen Design of Multi-Node Wireless Networking System on Lunar . . . . . . . 1709 Panpan Zhan, Yating Cao, Lu Zhang, Xiaofeng Zhang, Xiangyu Lin, and Zhiling Ye Algorithm Improvement of Pedestrians’ Red-Light Running Snapshot System Based on Image Recognition . . . . . . . . . . . . . . . . . . . 1718 Zhiqiang Wang, Xiaodong Sun, Xiaoxu Zhang, Ti Han, and Fei Gao A Datacube Reconstruction Method for Snapshot Image Mapping Spectrometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1727 Xiaoming Ding and Cheng Wang LFMCW Radar DP-TBD for Power Line Target Detection . . . . . . . . . 1737 Xionglan Chen, Guanghe Chen, and Zhanfeng Zhao

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Review of ML Method, LVD and PCFCRD and Future Research for Noisy Multicomponent LFM Signals Analysis . . . . . . . . . . . . . . . . 1744 Jibin Zheng, Kangle Zhu, Hongwei Liu, and Yang Yang Research on Vision-Based RSSI Path Loss Compensation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1751 Guangchao Xu, Danyang Qin, Ping Ji, Min Zhao, Ruolin Guo, and Pan Feng Efﬁcient Energy Power Allocation for Forecasted Channel Based on Transfer Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1758 Zhangliang Chen and Qilian Liang A Modular Indoor Air Quality Monitoring System Based on Internet of Thing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 Liang Zhao, Guangwen Wang, Liangdong Ma, and Jili Zhang Performance Analysis for Beamspace MIMO-NOMA System . . . . . . . 1773 Qiuyue Zhu, Wenbin Zhang, Lingzhi Liu, Bowen Zhong, and Shaochuan Wu A Novel Low-Complexity Joint Range-Azimuth Estimator for Short-Range FMCW Radar System . . . . . . . . . . . . . . . . . . . . . . . . 1782 Yong Wang, Yanchun Li, Xiaolong Yang, Mu Zhou, and Zengshan Tian Comparative Simulation for Nonlinear Effect of Hybrid Optical Fiber-Links in High-Speed WDM Systems . . . . . . . . . . . . . . . . . . . . . . 1787 Zhan-Heng Dai, Wei-Feng Chen, Li-Min Li, Ruo-Fei Ma, Bo Li, and Gongliang Liu POI Recommendation Based on Heterogeneous Network . . . . . . . . . . 1795 Yan Wen, Jiansong Zhang, Geng Chen, Xin Chen, and Ming Chen A Survey on Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . 1803 Yan Wen, Cong Fan, Geng Chen, Xin Chen, and Ming Chen A Hybrid TWDM-RoF Transmission System Based on a Sub-Central Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1811 Anliang Liu, Haichao Wei, Zhenyu Na, and Hongxi Yin Optimal Subcarrier Allocation for Maximizing Energy Efﬁciency in AF Relay Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1819 Weidang Lu, Shanzhen Fang, Yiyang Qiang, Bo Li, and Yi Gong A Study on D2D Communication Based on NOMA Technology . . . . . 1826 Xiumei Wang, Kai Mao, Huiru Wang, and Yin Lu Research on Deception Jamming Methods of Radar Netting . . . . . . . . 1835 Xiaoqian Lu, Hu Shen, Wenwen Gao, and Xiaoyu Zhong

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Cluster Feed Beam Synthesis Network Calibration . . . . . . . . . . . . . . . 1844 Zhonghua Wang, Yaqi Wang, Chaoqiong Fan, Bin Li, and Chenglin Zhao Design and Optimization of Cluster Feed Reﬂector Antenna . . . . . . . . 1855 Zhonghua Wang, Yaqi Wang, Chaoqiong Fan, Bin Li, and Chenglin Zhao Cognitive Simultaneous Wireless Information and Power Transfer Based on Decode-and-Forward Relay . . . . . . . . . . . . . . . . . . . . . . . . . 1864 Xiaoyan Li, Yiyang Qiang, Weidang Lu, Hong Peng, and Bo Li A Deep-Learning-Based Distributed Compressive Sensing in UWB Soil Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1872 Chenkai Zhao, Jing Liang, and Qin Tang An Improved McEliece Cryptosystem Based on QC-LDPC Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1880 Fan Bu, Zhiping Shi, Lanjun Li, Shujun Zhang, and Dandi Yang Research on Multi-carrier System Based on Index Modulation . . . . . . 1887 Dong Wang, Jie Yang, and Bing Zhao A GEO Satellite Positioning Testbed Based on Labview and STK . . . . 1892 Yunfeng Liu, Qi Zhang, Shuai Han, and Deyue Zou SVR Based Nonlinear PA Equalization in MIMO System with Rayleigh Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1900 Bowen Zhong, Wenbin Zhang, Shaochuan Wu, and Qiuyue Zhu LoS-MIMO Channel Capacity of Distributed GEO Satellites Communication System Over the Sea . . . . . . . . . . . . . . . . . . . . . . . . . 1908 Chi Zhang, Hui Li, Xuan An Song, Jie Cheng, and Li Jie Wang Design and Implementation of Flight Data Processing Software for Global Flight Tracking System Based on Stored Procedure . . . . . . 1916 Peng Wang, Wanwei Wang, Zhe Zhang, Min Chen, and Jun Yang Face Recognition Method Based on Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1925 Yunhao Liu and Jie Yang An On-Line ASAP Scheduling Method for Time-Triggered Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1930 Guevara Ania, Qiao Li, and Ruowen Yan Soil pH Value Prediction Using UWB Radar Echoes Based on XGBoost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1941 Tiantian Wang, Chenghao Yang, and Jing Liang

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A Novel Joint Resource Allocation Algorithm in 5G Heterogeneous Integrated Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1948 Qingtian Zeng, Qiong Wu, and Geng Chen A Vehicle Positioning Algorithm Based on Single Base Station in the Vehicle Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1958 Geng Chen, Xueying Liu, Qingtian Zeng, and Yan Wen Heterogeneous Wireless Network Resource Allocation Based on Stackelberg Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1969 Shouming Wei, Shuai Wei, Bin Wang, and Sheng Yu On the Performance of Multiuser Dual-Hop Satellite Relaying . . . . . . 1977 Huaicong Kong, Min Lin, Xiaoyu Liu, Jian Ouyang, and Xin Liu Architectures and Key Technical Challenges for Space-Terrestrial Heterogeneous Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1986 Yang Zhang, Chao Mu, Zhou Lu, Fangmin Xu, and Ye Xiao Design and Implementation of the Coarse and Fine Data Fusion Based on Round Inductosyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1993 Li Jing, Cui Chenpeng, and Zhao Xin Lossless Flow Control for Space Networks . . . . . . . . . . . . . . . . . . . . . . 2002 Zhigang Yu, Xu Feng, Yang Zhang, and Zhou Lu Heterogeneous Network Selection Algorithm Based on Deep Q Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2011 Sheng Yu, Chen-Guang He, Wei-Xiao Meng, Shuai Wei, and Shou-Ming Wei Vertical Handover Algorithm Based on KL-TOPSIS in Heterogeneous Private Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2020 Chen-Guang He, Qiang Yang, Shou-Ming Wei, and Jing-Qi Yang A Deep Deformable Convolutional Method for Age-Invariant Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2029 Hui Zhan, Shenghong Li, and Haonan Guo Weight Determination Method Based on TFN and RST in Vertical Handover of Heterogeneous Networks . . . . . . . . . . . . . . . . . . . . . . . . . 2038 Chen-Guang He, Jing-Qi Yang, Shou-Ming Wei, and Qiang Yang Deep Learning-Based Device-Free Localization Using ZigBee . . . . . . . 2046 Yongliang Sun, Xiaocheng Wang, and Xuzhao Zhang A Modiﬁed Genetic Fuzzy Tree Based Moving Strategy for Nodes with Different Sensing Range in Heterogeneous WSN . . . . . . . . . . . . . 2050 Xiaofeng Yu, Bingjie Zhang, Hanqin Qin, Tian Le, Hao Yang, and Jing Liang

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Wireless Indoor Positioning Algorithm Based on RSS and CSI Feature Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2057 Shi-Xue Zhang, Xin-Yue Fan, and Xiao-Yong Luo Design and Veriﬁcation of On-Board Computer Based on S698PM and Time-Triggered Ethernet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2068 Cuitao Zhang, Xiongwen He, Panpan Zhan, Zheng Qi, Ming Gu, and Dong Yan An Optimal Deployment Strategy for Radars and Infrared Sensors in Target Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2075 Lanjun Li and Jing Liang Integrity Design of Spaceborne TTEthernet with Cut-Through Switching Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2085 Ji Li, Huagang Xiong, Dong Yan, and Qiao Li Image Mosaic Algorithm Based on SURF . . . . . . . . . . . . . . . . . . . . . . 2093 Qingfeng Sun, Hao Yang, Liang Wang, and Qingqing Zhang Research on Dynamic Performance of DVR Based on Dual Loop Vector Decoupling Control Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 2099 Hao Yang and Liang Wang Facial Micro-expression Recognition with Adaptive Video Motion Magniﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2107 Zhilin Lei and Shenghong Li Computation Task Ofﬂoading for Minimizing Energy Consumption with Mobile Edge Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2117 Guangying Wang, Qiyishu Li, and Xiangbin Yu Soil pH and Humidity Classiﬁcation Based on GRU-RNN Via UWB Radar Echoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2124 Chenghao Yang, Tiantian Wang, and Jing Liang Bit Error Rate Analysis of Space-to-Ground Optical Link Under the Inﬂuence of Atmospheric Turbulence . . . . . . . . . . . . . . . . . 2132 Xiao-Fan Xu, Ni-Wei Wang, and Zhou Lu Performance Analysis of Amplify-and-Forward Satellite Relaying System with Rain Attenuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2140 Qingquan Huang, Guoqiang Cheng, Lin Yang, Ruiyang Xing, and Jian Ouyang Threat-Based Sensor Management For Multi-target Tracking . . . . . . . 2147 Yuqi Lan and Jing Liang

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Research on Measurement Matrix Based on Compressed Sensing Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2155 Zhihong Wang, Hai Wang, Guiling Sun, and Yi Xu PID Control of Electron Beam Evaporation System Based on Improved Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2163 Wenwu Zhu Doppler Weather Radar Network Joint Observation and Reﬂectivity Data Mosaic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2169 Qutie Jiela, Haijiang Wang, Jiaoyang He, and Debin Su Numerical Calculation of Combustion Characteristics in Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2173 Xudong Wang, Chunhua Xiong, Feng Wang, Gaojun An, and Dongkai Ma A NOMA Power Allocation Strategy Based on Genetic Algorithm . . . . Lu Yin, Wang Chenggong, Mao Kai, Bao Kuanxin, and Bian Haowei

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AUG-BERT: An Efﬁcient Data Augmentation Algorithm for Text Classiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2191 Linqing Shi, Danyang Liu, Gongshen Liu, and Kui Meng Coverage Performance Analysis for Visible Light Communication Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2199 Juan Li and Xu Bao An Intelligent Garbage Bin Based on NB-IoT . . . . . . . . . . . . . . . . . . . 2208 Yazhou Guo, Ming Li, Kai Mao, Zhuoan Ma, and Yin Lu Research on X-Ray Digital Image Defect Detection of Wire Crimp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2217 Yanwei Wang and Jiaping Chen Architecture and Key Technology Challenges of Future Space-Based Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2223 Ni-Wei Wang, Xiao-Fan Xu, Ying-Yuan Gao, Yue Cui, Fei Xiao, and Zhou Lu Filter Bank Design for Subband Adaptive Microphone Arrays . . . . . . 2230 Hongli Jia Communication System Based on DFT Spread Spectrum Technology to Reduce the Peak Average Power Ratio of CO-OFDM System . . . . . 2241 Yaqi Wang and Yupeng Li

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Low-Complexity Channel Estimation Method Based on ISSOR-PCG for Massive MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2251 Cheng Zhou, Zhengquan Li, Qiong Wu, Yang Liu, Baolong Li, Guilu Wu, and Xiaoqing Zhao Ship Classiﬁcation Methods for Sentinel-1 SAR Images . . . . . . . . . . . . 2259 Jia Duan, Yifeng Wu, and Jingsheng Luo Wheat Growth Assessment for Satellite Remote Sensing Enabled Precision Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2270 Yuxi Fang, He Sun, Yijun Yan, Jinchang Ren, Daming Dong, Zhongxin Chen, Hong Yue, and Tariq Durrani An Improved ToA Ranging Scheme for Localization in Underwater Acoustic Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2278 Jinwang Yi, Zhipeng Lin, Fei Yuan, Xianling Wang, and Jiangnan Yuan Performance Analysis of Three-Layered Satellite Network Based on Stochastic Network Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2285 Ying Zhou, Xiaoqiang Di, Ligang Cong, Weiwu Ren, Weiyou Liu, Yuming Jiang, and Huilin Jiang Robust Sensor Geometry Design in Sky-Wave Time-Difference-ofArrival Localization Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2295 He Ma, Xing-peng Mao, and Tie-nan Zhang A NOMA Power Allocation Method Based on Greedy Algorithm . . . . 2304 Yin Lu, Shuai Chen, Kai Mao, and Haowei Bian Multi-sensor Data Fusion Using Adaptive Kalman Filter . . . . . . . . . . . 2314 Yinjing Guo, Manlin Zhang, Fong Kang, Wenjian Yang, and Yujie Zhou Feasibility Study of Optical Synthetic Aperture System Based on Small Satellite Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2321 Ni-Wei Wang, Xiao-Fan Xu, and Zhou Lu A New Coded-Modulated Pulse Train for Continuous Active Sonar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2328 Chengyu Guan, Zemin Zhou, Di Wu, and Xinwu Zeng Region Based Hierarchical Modelling for Effective Shadow Removal in Natural Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2336 Ping Ma, Jinchang Ren, Genyun Sun, Paul Murray, and Tariq Durrani Collaborative Attention Network for Natural Language Inference . . . . 2343 Shiyi Zhang, Yinghua Ma, Shenghong Li, and Weikai Sun Three-Dimensional Imaging Method of Vortex Electromagnetic Wave Using MIMO Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2350 Jia Liang, Yan Li, Ping-fang Zhang, Xiang-wei Jiang, and Bin Cai

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Energy Storage Techniques Applied in Smart Grid . . . . . . . . . . . . . . . 2357 Youjie Zhou, Xudong Wang, Xiangjing Mu, Zhizhou Long, Changbo Lu, and Lijie Zhou A Robust Hough Transform-Based Track Initiation Method for Multiple Target Tracking in Dense Clutter . . . . . . . . . . . . . . . . . . 2364 Qian Zhu, Panhe Hu, Jiameng Pan, and Qinglong Bao Structure Design and Analysis of Space Omni-Directional Plasma Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2373 Junfeng Wang, Tao Li, Hua Zhao, Qiongying Ren, Yi Zong, and Zhenyu Tang Design and Implementation of GEO Battery Autonomous Management System for Lithium Battery with Balanced Control Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2380 Lijun Yang, Bohan Chen, Yan Du, Liang Qiao, and Jiaxiang Niu Target Direction Finding in HFSWR Sea Clutter Based on FRFT . . . . Shuai Shao, Changjun Yu, Aijun Liu, Yulin Hu, and Bo Li

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Adaptive Non-uniform Clustering Routing Protocol Design in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2398 Qingtian Zeng, Tianyi Zhang, Geng Chen, and Ge Song Comparative Analysis of Reﬂectivity from an Updated SC Dual Polarization Radar and a SA System in CINRAD Network . . . . . . . . . 2410 Yue Liu, Debin Su, Xue Tan, and Haijiang Wang Subcarrier Allocation-Based SWIPT for OFDM Cooperative Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2418 Xueying Liu, Xin Liu, Bo Li, and Weidang Lu Power Control for Underlay Full-Duplex D2D Communications Based on D. C. Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2427 Zanyang Liang and Liang Han Power Control for Underlay Full-Duplex D2D Communications Based on Max-Min Weighted Criterion . . . . . . . . . . . . . . . . . . . . . . . . 2435 Yingwei Zhang and Liang Han Analysis on the Change of Dynamic Output Degree Distributions in the BP Decoding Process of LT Codes . . . . . . . . . . . . . . . . . . . . . . . 2443 Shuang Wu A Stable and Reliable Self-tuning Pointer Type Meter Reading Recognition Based on Gamma Correction . . . . . . . . . . . . . . . . . . . . . . 2448 Yucui Liu, Kunfeng Shi, Zhiqiang Zhang, Zhihong Hu, and Anliang Liu

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Spectral Efﬁciency for Multi-pair Massive MIMO Two-Way Relay Networks with Hybrid Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2459 Hongyan Wang, Zhengquan Li, Xiaomei Xue, Baolong Li, Yang Liu, Guilu Wu, and Qiong Wu An Improved Frost Filtering Algorithm Based on the Four Rectangular Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2467 Xinpeng Zhang, Xiaofei Shi, Min Zhang, and Li Li Waterline Extraction Based on Superpixels and Region Merging for SAR Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2474 Xige Liu, Xiaofei Shi, Zhigang Wang, and Li Li Coastline Detection with Active Contour Model Based on Inverse Gaussian Distribution in SAR Images . . . . . . . . . . . . . . . . . . . . . . . . . 2479 Kuiyuan Ni, Xiaofei Shi, Yuelong Zhang, and Li Li Facial Expression Recognition Based on Subregion Weighted Fusion and LDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2485 Hui Lin, Yan Wang, Zhenzhen Wang, Mengli Sun, Shiqiang Zhang, Xiaofei Shi, and Xiaokai Liu Extended Target Tracking Using Non-linear Observations . . . . . . . . . 2490 Qifeng Sun, Wangfei Quan, Lei Hou, and Tingting Zhang A Coordinated Multi-point Handover Scheme for 5G C/U-Plane Split Network in High-Speed Railway . . . . . . . . . . . . . . . . . . . . . . . . . 2498 Xuanbing Zeng, Gang Chuai, and Weidong Gao A Hierarchical FDIR Architecture Supporting Online Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2506 Cangzhou Yuan, Ran Peng, Panpan Zhan, and Fayou Yuan College Students Learning Behavior Analysis Based on SVM and Fisher-Score Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 2514 Qiumin Luo, Hongzhi Wang, Gang Li, and Zunyi Shang Network Trafﬁc Text Classiﬁcation Based on Multi-instance Learning and Principal Component Analysis . . . . . . . . . . . . . . . . . . . . 2519 Hongzhi Wang, Qiumin Luo, Zunyi Shang, Gang Li, and Xiaofei Shi Calculation and Simulation of Inductive Overvoltage of Transmission Line Based on Taylor’s Formula Expansion Double Exponential Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2525 Yucheng Qiu, Donghui Li, and Xiaofei Shi Deep Learning Based Exploring Channel Reciprocity Method in FDD Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2533 Jie Wang, Guan Gui, Rong Wang, Yue Yin, Hao Huang, and Yu Wang

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Steering Machine Learning Mechanism Based on Big Data Integrated Cooperative Collision Avoidance for MASS . . . . . . . . . . . . 2542 Chengzhuo Han, Tingting Yang, Siwen Wei, Hailong Feng, Jiupeng Wang, and Genglin Zhang A Weighted Fusion Method for UAV Hyperspectral Image Splicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2550 Yulei Wang, Yao Shi, Qingyu Zhu, Di Wu, Chunyan Yu, Meiping Song, and Anliang Liu Hyperspectral Target Detection Based on Spectral Weighting . . . . . . . 2555 Di Wu, Yulei Wang, Yao Shi, Qingyu Zhu, and Anliang Liu A Framework for Analysis of Non-functional Properties of AADL Model Based on PNML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2562 Cangzhou Yuan, Hangyu He, Panpan Zhan, and Tao Chen A Golden Section Method for Univariate One-Dimensional Maximum Likelihood Parameter Estimation . . . . . . . . . . . . . . . . . . . . 2571 Ruitao Liu and Qiang Wang Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2581 Chen Lu, Dong Liang, Dongxu Wang, and Yilin Zhao SFSSD: Shallow Feature Fusion Single Shot Multibox Detector . . . . . 2590 Dafeng Wang, Bo Zhang, Yang Cao, and Mingyu Lu Beamforming Based on Energy State Feedback for Simultaneous Wireless Information and Power Transmission . . . . . . . . . . . . . . . . . . 2599 Chunfeng Wang and Naijin Liu Research on Cross-Chain Technology Architecture System Based on Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609 Jianbiao Zhang, Yanhui Liu, and Zhaoqian Zhang Research on Data Protection Architecture Based on Block Chain . . . . 2618 Jianbiao Zhang, Yanhui Liu, and Zhaoqian Zhang Research on Active Dynamic Trusted Migration Scheme for VM-vTPCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2625 Xiao Wang, Jianbiao Zhang, Ai Zhang, Xingwei Feng, and Zhiqiang Zeng Multiple Hybrid Strategies Filtrate Localization Based on FM for Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2634 Wei-Cheng Xue, Yu Hua, and Jun Ju Localization Algorithm Based on FM for Mobile Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2642 Wei-cheng Xue, Yu Hua, and Jun Ju

Contents

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Coherent State Based Quantum Optical Communication with Mature Classical Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . 2647 Ming Li and Li Li Design of Codebook for High Overload SCMA . . . . . . . . . . . . . . . . . . 2654 Min Jia, Shiyao Meng, Qing Guo, and Xuemai Gu A Spectrum Allocation Scheme Based on Power Control in Cognitive Satellite Communication . . . . . . . . . . . . . . . . . . . . . . . . . 2663 Xiaoye Jing, Xiaofeng Liu, Min Jia, Qing Guo, and Xuemai Gu Fruit Classiﬁcation Through Deep Learning: A Convolutional Neural Network Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2671 Tahir Arshad, Min Jia, Qing Guo, Xuemai Gu, and Xiaofeng Liu Correction to: Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos . . . . . . . . . . . . . . . . . . . . . Zhiyuan Li, Aiping Jiang, and Yuying Mu

C1

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2679

Flame Detection Method Based on Feature Recognition Ti Han1, Changyun Ge1, Shanshan Li2(&), and Xinqiang Zhang1 1

Intelligent Science and Technology Department, Dalian Neusoft University of Information, 116023 Dalian, China {hanti,gechangyun,zhangxinqiang}@neusoft.edu.cn 2 College of Electrical and Information Engineering, Huaihua University, 418000 Huaihua, China [email protected]

Abstract. This paper introduced technique of current flame detecting system based on the CCD camera from which the color images are transferred into a computer, then the image processing algorithm is used to determine whether there is ﬁre in the image sequence, the monitoring method is the most important in the whole system. The initiation of flame is a slowly process in which the image characteristics are very clearly, As the shape, area, and intensity of the flame in different time, each one varies every time. The image information of flame is analyzed in this paper, the regularity is summarized in color feature and dynamic characteristics, which is the main basis for the design of the identiﬁcation algorithm. The color model is established based on the analysis of the characteristics of flame color, and the dynamic characteristics of the flame are identiﬁed according to the irregularity, the similarity and the stability of the flame, so as to provide the accurate basis for the flame detection. Keywords: Flame recognition

Colour character Dynamic character

1 Introduction Along with the development of computer science and image processing technology, it was found that when the fuel is burning, the fuel will emit light from ultraviolet to infrared. In the visible band, the flame image has a unique feature, such as chromatography, texture, etc., so that it is obviously different from the background in the image [1–3]. Using these features, image processing method is used to identify the ﬁre. Because the visual information is the medium of light, the image detection can be more quickly than the traditional detection method. The image information is rich and intuitive, and it has laid a foundation for the identiﬁcation and judgment of early identiﬁcation of ﬁre, any other ﬁre detection technology can not provide such a rich and intuitive information, can be used in large space, large area of the environment. As the combustion process is a typical unstable process, affected by fuel, environment and climate. In natural environment, the process of combustion is more complicated than that of general power plant, there are variety of characterization parameters. At the same time, there are various kinds of interference factors in the ﬁeld. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 1–8, 2020 https://doi.org/10.1007/978-981-13-9409-6_1

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Such as sunlight, lighting, etc. will affect the results of the identiﬁcation. It is difﬁcult to obtain higher accuracy and wide applicability if the early ﬁre detection is carried out by a single parameter measurement [4, 5]. The characteristics are mainly discussed in the paper about the area of the light spot in the flame video image, and the large background region. By using suspected area of the extraction of the segmentation, a number of characteristics of the flame are identiﬁed. It can further reduce the false alarm rate of image type flame detection system.

2 The Process of Flame Identiﬁcation The image signal collected by the camera is processed by the digital image signal. In order to make the system application and update flexible, all the processing of image data is realized by software. The whole software flow chart of ﬁre identiﬁcation system is shown in Fig. 1. The overall idea of the design is to use a common CCD camera to take a picture of the scene. Then put the image into the real-time background subtraction to determine whether the abnormal situation, if there are unusual circumstances, the flame identiﬁcation procedure is carried out. Image with abnormal condition after flame segmentation and then color feature recognition and dynamic feature recognition begins. If these two characteristics are consistent with the existence of flame, ﬁre alarm warning is issued.

3 Color Feature Recognition of Flame After the color image segmentation collected by camera, then begin to focus on the analysis of its objectives to determine whether it is ﬁre flame. Method is to put the target into different color spaces to see if they meet certain rules, thus can derive an effective judgment method [6–8]. This paper is about empirical analysis of color space based on the flame image segmentation. Because the image of input system is a RGB image, the image is converted from RGB space to YCbCr space according to the Formula 1, then get the images of the Cr Cb distribution. 2

3 2 3 2 Y 0 0:2990 0:5870 4 Cb 5 ¼ 4 128 5 þ 4 0:1687 0:3313 Cr 128 0:5000 0:4187

32 3 0:1140 R 0:5000 54 G 5 0:0813 B

ð1Þ

The distribution of flame image in (Cr, Cb) color space is close to the normal distribution, but other interference objects with flame color do not have. Flame image Cr and Cb have their own normal distribution characteristics relative to Y, the flame image point in two-dimensional space (Cr, Cb) approximately obey the normal distribution. In order to make the image easy to understand, Fig. 2 is Cr alone compared with Y.

Flame Detection Method Based on Feature Recognition

Begin CCD real time image acquisition N Abnormal condition detection Y Flame image segmentation

Judge for non flame N

Color feature recognition of flame Y

N

Dynamic feature recognition of flame Y Judge for the flame

Issue fire early warning

Fig. 1. Flow chart of flame detection

(a) Flame image

(b) Other image

Fig. 2. Cr-Y space distribution

3

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Using normal distribution function to carry out quantitative analysis, the expression of normal distribution function is as below. ( " 2 #) ðx lx Þ y ly y ly 1 1 ðx lx Þ2 exp þ2 þ Fðx; yÞ ¼ 2prx ry 2 r2x r2y rx ry

ð2Þ

Among them, lx andly are respectively the mean of x and y, rx and ry are respectively the sample standard deviation of x and y. Thus can calculate the mean (mu_x) and variance (sigma_x) of Cr and Cb of flame image. The F function distribution by Formula 3 is shown as Fig. 3.

(a) Flame image

(b) Other image Fig. 3. F function distribution

f ðiÞ ¼

1 2p sigma x sigma y ( " #) 1 ðxðiÞ mu xÞ2 ðxðiÞ mu xÞðyðiÞ mu yÞ ðyðiÞ mu yÞ2 þ exp þ2 sigma x2 sigma y2 2 sigma x sigma y

ð3Þ When Cr and Cb in an image meet the above-mentioned distribution in the mean and standard deviation interval, In other words, F (x, y) of Cr and Cb meet the above similar distribution, the image can be considered flame. Turning Cr and Cb matrix into one-dimensional vector, the general statistical characteristic value of Cb and Cr can be get, shown as Table 1(take 0.05 conﬁdence interval).

Flame Detection Method Based on Feature Recognition

5

Table 1. General statistical characteristic value of Cb and Cr

Cr parameter Cb parameter

Mean point estimation 154.293

Standard deviation point estimation 22.264

Mean interval estimation [154.214,154.372]

Standard deviation interval estimation [22.209, 22.320]

154.879

27.104

[154.783,154.975]

[22.209, 22.320]

And so on, you can get the feature vector of color statistics of every frame image. When the image of the following frame is segmented, if the target is in accordance with the statistical characteristic of the above, that is to identify the occurrence of ﬁre. Through the ﬁre scene of each scene to make a pattern of training, get a characteristic parameter of the flame, then can get a relatively reliable threshold value to determine the data obtained. In this experiment, the threshold of statistical vector is selected as: Cr mean interval (150, 160), standard deviation interval (20, 23), Cb mean interval (150, 160), standard deviation interval (25, 28). On this basis, for a given threshold value T, if the Cr, Cb values of a point meet f(i) > T, then the image is considered to have a flame color. Here set T to 0.005, the image points of the flame color region taken out by the above section are counted, to make f(i) > T image points more than 60, thus the flame points of CrCb space ﬁt the normal distribution.

4 Dynamic Feature Recognition of Flame The shape of an object is an important feature of resolution and recognition for the human visual system. After many times of search, analysis and comparison, the dynamic characteristics of the system is based on the identiﬁcation of the scene of the image gray, shape, changes in the recognition target. According to the theory of computer graphics, these features are quantiﬁed and normalized as a criterion for identiﬁcation [9, 10]. After the analysis, the system adopts the following flame image dynamic feature identiﬁcation criterion. 4.1

Irregularity

According to the irregular shape of the flame but most of the interference sources (such as electric torch, incandescent lamp, etc.) are characterized by a high degree of shape regularity, circular degree can be used as one of the flame criterion. The circular degree represents the complexity of the shape of the object, the formula is shown as Formula 4. Circularity ¼ L2=ð4p SÞ

L perimeter; S area

ð4Þ

The circular degree to the circular object takes the minimum value 1, the more complex the shape of an object is, the greater its value is. First of all, extract the edge chain code to the results of color detection, calculated L. Secondly, calculate the area of

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the flame area in the image, that is, the total number of pixels that have been set to black in the segmented image, get S. Finally, the circle degree is calculated, and the average value of continuous N frame image is obtained. Experiments have demonstrated that the circularity of flame is greater than 7.5. 4.2

Similarity

In the recognition of flame, we can consider the change law of the similarity of flame shape in the early ﬁre. This change law is in fact the shape change non regularity of the ﬁre flame with respect to other common interference phenomenon, but this kind of non regularity has certain similarity from the shape change, the space change, the space distribution, In particular, the flame shape characteristic of each successive frame image is similar to that of a short interval. Therefore, it can be described by the structural similarity of continuous images, this is taken into account that although the ﬁre flame presents a trend of constant development and change,The method of calculating frame difference similarity can be used to describe the characteristic. Let bi(x, y) be the target image binary image sequence, mark the pixels with the value of 1 in bi(x, y), get the possible flame area Xi in each frame of the image sequence. After the discovery of suspicious flame regions, use the method of similarity calculation of consecutive frames image to classify the flame and interference pattern. The similarity of continuous frame image is deﬁned as Formula 5. P bi ðx; yÞ \ bi þ 1 ðx; yÞ ni ðx; yÞ ¼

ðx;yÞ2X

P

ðx;yÞ2X

bi ðx; yÞ [ bi þ 1 ðx; yÞ

i ¼ 1; 2; . . .; N

ð5Þ

After obtained a number of similarity, the mean similarity n for several consecutive frames is used as the criterion. In general, when the n is less than the threshold, that is a fast moving object highlights. And when it is greater than a certain threshold, there is ﬁxed light emitting region. When it is between the two threshold, the area can be considered as the flame region. Experiments have demonstrated that the threshold range of flame similarity is between 0.3 and 0.7. 4.3

Stability

From the point of view of flame recognition, this paper uses the centroid characteristic of the flame image, the stability is expressed by the centroid [11, 12]. For a flame image, ﬁrst calculate the centroid, the expression is deﬁned as Formula 6. ðx; yÞ ¼ ðM10 =M00 ; M01 =M00 Þ

ð6Þ

M is the matrix moment, for a binary image f(i, j), the deﬁnition of its pq order moment is deﬁned as Formula 7.

Flame Detection Method Based on Feature Recognition

Mpq ¼

XX

f ði; jÞip jq

7

ð7Þ

Among them, i, j are the horizontal and vertical coordinates of the image. The aim of computing image stability is to take into account the unique nature of the constant change of flame shape, reflected in the image of the digital feature that is the performance of its center of mass is also the transformation of the disorder. Correspondingly, if the change in the same time as the same time increases, it is shown that there is a high brightness object move to the camera direction, this can eliminate interference phenomenon. Experiments have demonstrated that the range of centroids is within 20 pixels.

5 Conclusion To sum up, for the flame image sequences, through the flame segmentation, color feature recognition and dynamic feature recognition, in accordance with the characteristics of the flame, the flame identiﬁcation result is the existence of flame, and ﬁnally issued a ﬁre warning. Flame has more characteristics, it needs to ﬁnd a method suitable for ﬁre identiﬁcation and processing speed. Therefore, a further study need to be made in theory and technology, and for in-depth research and testing. As a ﬁre identiﬁcation system, all aspects of the factors should be considered, in order to achieve technical, performance indicators in line with the actual needs. Acknowledgements. Project supported by Natural Science Foundation Project of Liaoning Province, No. 20180520011.

References 1. Zhou F, Li X, Zhang X (2015) PCNN based Otsu multi-threshold segmentation algorithm for noised images. J Comput Inf Syst 21(8):7791–7798 2. Chena J, Heb Y, Wanga J (2010) Multi-feature fusion based fast video flame detection. Build Environ 45(5):1113–1122 3. Cheong P, Chang KF, Lai YH (2011) A ZigBee-based wireless sensor network node for ultraviolet detection of flame. IEEE Trans Ind Electron 58(11):5271–5277 4. Marques JS, Jorge PM (2000) Visual inspection of a combustion process in a thermoelectric plant. Sig Process 80(8):1577–1589 5. Xu X, Guan YD, Xu XY (2008) Application of image compression technology in forest ﬁre prevention and control system. Sci Technol Rev 26(6):34–37 6. Chen TH, Kao CL, Chang S (2003) An intelligent real-time ﬁre-detection method based on video. In: Proceedings of IEEE 37th annual 2003 international Carnahan conference on security technology. Taipei, pp 104–111 7. K.R.Castleman. Digital Image Processing. Electronic Industry Press, 2011, pp. 389 * 391 8. Lin KY, Wu JH, Xu Lh (2005) A survey on color image segmentation techniques. Image Graph 10(1):1–10

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9. Horng WB, Peng JW, Chen CY (2005) A new image-based real-time flame detection method using color analysis. In: Proceedings of 2005 IEEE international conference on networking, sensing and control. Tucson, Arizona, USA, pp 100–105 10. Chen TH, Yin YH, Huang SF, Ye YT (2006) The smoke detection for early ﬁre-alarming system base on video. In: Proceedings of the 2006 international conference on intelligent information hiding and multimedia signal processing (IIH-MSP’06) processing, pp 427–430 11. Dedeoglu Y, Toreyin BU, Gudukbay U et al (2005) Real-time ﬁre and flame detection in video. In: Proceedings of IEEE ICASSP’05, pp 669–672 12. Mueller M, Karasev P, Kolesov I, Tannenbaum A (2013) Optical flow estimation for flame detection in videos. IEEE Trans Image Process 22(7):2786–2797

Small Cell Deployment Based on Energy Efﬁciency in Heterogeneous Networks Yinghui Zhang1, Shuang Ning1, Haiming Wang1, Jing Gao2, and Yang Liu1(&) 1

College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China [email protected] 2 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China

Abstract. The deployment of the next generation mobile networks increasingly relies on the deployments of small cell. In this paper, we propose and evaluate an optimal energy efﬁciency (EE) small cell deployment scheme to solve the problem of small cell deployment in Massive MIMO system, considering the impact of base station (BS) location, number of antennas and BS density on the EE of the system in different scenarios. Single-cell model and multi-cell model are considered. In the single-cell model, the system allocates the location of the small cell by minimizing the system power consumption when the scheme satisﬁes the target transmission rate constraint of the user. In the multi-cell model, the optimal BS density is obtained by deriving the EE expressions of different optimization parameters. The simulation results show that the scheme can achieve high EE and validate the effectiveness of the proposed scheme. Keywords: Massive MIMO efﬁciency

Heterogeneous network Small cell Energy

1 Introduction In recent years, massive MIMO is widely considered as an effective means to improve system capacity in the Fifth Generation Mobile Networks (5G) [1]. The large number of degrees of freedom obtained by the base station (BS) through the deployment of large-scale antenna arrays can be used to improve the system performance [2]. However, the traditional macro cell network cannot meet the development of future communication, because the harsh requirements of future communication systems for high speed, low time delay and high energy efﬁciency (EE) are need. Therefore, massive MIMO [3] and small cell [4] is considered as an effective means to improve the capacity, high data rate, lower delay and higher spectral efﬁciency (SE) and EE required by 5G wireless communication systems. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61761033, and Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2016MS0604. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 9–19, 2020 https://doi.org/10.1007/978-981-13-9409-6_2

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Although massive MIMO heterogeneous system is an effective means to improve wireless network capacity and realize high data rate, it still faces many challenges. On the one hand, the deployment of BSs is relatively random in macro cell and small cell heterogeneous networks. A series of problems caused by deployment can be reduced obviously if the BSs can be deployed according to certain rules. On the other hand, the increase in the number of antennas also brings about the problem of high circuit power consumption for massive MIMO systems. Although deploying a large number of antennas can correspondingly reduce the transmission power consumption of the system [5], it will also correspondingly increase the power consumption of the radio frequency link circuit. In order to solve the related problems, this paper studies the deployment of small cell in massive MIMO heterogeneous networks. In a single cell system, the power consumption of the system is controlled and the method of uniform deployment of small cell is adopted to maximize EE. In a multi-cell system, a nonuniform deployment of small cell is adopted, which is more consistent with the actual situation, and the setting of deployment parameters under the condition of optimal EE is obtained. The rest of the paper is organized as follows. In Sect. 2, we illustrate the system model. Section 3 optimal small cell deployment scheme is proposed. Meanwhile, the properties of the proposed scheme are studied. Section 4 provides simulation results and, ﬁnally, conclusions are drawn in Sect. 5.

2 System Model This paper involves two deployment schemes. The ﬁrst is a single cell uniform deployment, and small cell is uniformly deployed within the coverage area of macro cell, which is relatively easy and has good coverage effect. The second is a multi-cell non-uniform deployment scheme, in which small cell are distributed in areas outside the radius of macro cell in a two-dimensional Poisson process. This section considers the downlink of single cell and multiple users, including one macro cell, m small cells and k service users. Macro cell is equipped with N transmitting antennas and the maximum number of antennas is four [6]. The received signal of the kth user is expressed as yk ¼ h H k;0 x0 þ

Xm i¼1

hH k;i xi þ zk ;

ð1Þ

NMBS 1 NSBS 1 where hH and hH represent the channel fading coefﬁcient vectors k;0 2 C k;i 2 C between macro cell, small cell and user k respectively. x0 and xi are the transmission signals of macro cell and small cell respectively. zk represents additive Gaussian noise with zero mean value and a variance of r2 . The deployment model of cell is shown in Fig. 1.

Small Cell Deployment Based on Energy

(a) Single cell heterogeneous networks

11

(b) Multi-cell heterogeneous networks

Fig. 1. Heterogeneous networks

As shown in Fig. 1a, macro cell is deployed in the center of the whole cell, and small cell is uniformly deployed in different positions of macro cell coverage radius in a certain proportion, and users are randomly distributed. The multi-cell heterogeneous network deployment model which uses Voronoi topology is shown in Fig. 1b. We consider multi-cell heterogeneous networks serving multiple users. Users are evenly distributed in the area with density kU . Macro cell is distributed as a two-dimensional Poisson process with a density of k1 . Small cell is distributed in the area outside the radius of macro cell [7] with Poisson process of density k2 (the inner area is within the radius of macro cell and the outer area is outside the radius. The large circle in the Fig. 1b is the coverage area of macro cell, the small circle in blue is the small cell, and the small cell is deployed in the area outside the coverage area of macro cell [8]. The inner region is denoted as Ainside ¼ [ x2u1 Bðx; DÞ which is the union of positions whose distance to the nearest macro cell is not greater than d, while the outer region, denoted Aoutside ¼ > >

@ PK > > : i¼1 i6¼k

1 Pm C PK Tr ðHi;j Wk;j Þ : j¼0 P C A ck k¼1 trðQ0;1 Wk;j Þ qj;1 8k;j m 2 2 þr Tr ðHi;j Wk;j Þ k j¼0

ð9Þ

Set rank Wk;j ¼ 0. Using semi-deﬁne relaxation [12], we can obtain min

m X K X

trðWk;j Þ þ Pstatic

j¼0 k¼1

8P PK m H 1 2 > h 1 þ W W > k;j k;j hk;j rk i¼1 #k < j¼0 k;j PK : s:t: > k¼1 tr Q0;1 Wk;j qj;1 8k;j > : Wk;j 0

ð10Þ

Given the target user SINR and antenna transmission maximum power constraints, the optimization parameters can be solved by Eqs. (8)–(10). 3.2

Multiple Cells

In this section, the downlink of multi-user massive MIMO system is considered to maximize EE while ensuring quality of service (QoS) [13]. Under the condition of maximizing EE, how to select the number of transmitting antennas and the number of active users of the BS is discussed.

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The power consumption (PC) of the system should not only consider the radiated power, but also consider the loss of analog hardware, backhaul signaling and other overhead costs (such as cooling and power loss). PC is deﬁned as PC ¼

XS XK

qx i¼1

Cða2 þ 1Þ a

j¼1 ðpk2 Þ2 |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

þ P0 þ PBS M þ PUE K þ MKPCE ; |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}

ð11Þ

Pstatic

Pdynamic

where K is the number of users, M is the antenna of macro cell, qx

Cða2 þ 1Þ a

ðpk2 Þ2

is the

average transmit power of user K in cell j, P0 is constant and the power consumption of hardware circuits caused by node cooling system, control signals, loop infrastructure, etc. ðPBS M þ PUE K Þ is the power consumption of base transceiver stations. Moreover, MKPCE is the power consumed by the user and the signal processing at the BS. Solving the optimal parameter group h ¼ ðk2 ; K; M Þ is as follows max EEðhÞ ¼ P

B log2 1 þ S j¼1

PK

i¼1 qx

Cða2 þ 1Þ a ðpk2 Þ2

q2 KsðMKÞ 1 þ qK

þ P0 þ PBS M þ PUE K þ MKPCE

:

ð12Þ

s:t: SINR c Then the optimal deployment scheme for EE optimization will be obtained by setting different parameters. In massive MIMO system, the circuit power consumption of the BS cannot be ignored [14]. Considering that the actual number of antennas requires corresponding RF transmission link support, deploying a large number of antennas can reduce the transmission power consumption of the system, but it will also increase the power consumption of the RF link circuit accordingly [15]. Therefore, this paper will coordinate the circuit power consumption caused by deploying a large number of antennas and select the number of BS antennas with the best EE. In order to ﬁnd the optimal value of M, EE maximization problem is described as EE1 ¼

B log2 1 þ

q2 KsðMKÞ 1 þ qK

P0 þ PBS M þ PUE K þ MKPCE

;

ð13Þ

where s is the pilot sequence of user k, q is the ratio coefﬁcient of received signal to SINR. The ratio of M to K is deﬁned as c ¼ M K , which is the number of antennas per user. For given K, c, the maximum value of EE is

EEM ¼

B log2 1 þ

q2 Ksð11cÞ 1 þ qK

P0 þ ðKPBS þ K 2 PUE Þc

;

ð14Þ

According to Eq. (11) and Eq. (12), replace the PC in Eq. (12) with Eq. (11), we can obtain the Eq. (15).

Small Cell Deployment Based on Energy

15

In the heterogeneous networks, the deployment density of small cell affects the EE of the whole system [16]. Therefore, the optimized density of small cell is taken as a research parameter. In this case, the EE maximization expression is

EEk2 ¼ P P S K j¼1

i¼1 qx

B log2 1 þ Cða2 þ 1Þ a ðpk2 Þ2

q2 KsðMKÞ 1 þ qK

þ P0 þ PBS M þ PUE K þ MKPCE

:

ð15Þ

EEk2 is a monotonic increasing function of k2 , and k2 ! 1 is the case when EE is maximized. However, an inﬁnitely high-density of BSs is not feasible in practice. Therefore, we can get the parameters ðM; k2 Þ settings under the condition of optimal EE, which will be veriﬁed through simulating in the following part.

4 Simulation Results 4.1

Single Cell

In Fig. 2, the selection of the optimal location of the BSs under different QoS is simulated. We can see there will be different power consumption when the small cells are placed in the radius of different distances of the macro cell, It can be seen that with

Fig. 2. Power consumption of different BS location under different QoS

the increase of QoS value, when the small cell is 200 and 450 m away from the macro cell, the power consumption is relatively large, and the power required for cell deployment near the position of 350 m is minimum. Therefore, it is optimal to deploy the small cell at a position near 350 m away from the Macro cell in this case. Figure 3 shows that the power consumption of the system is greatly reduced with the increase of the number of macro cell antennas. It shows that the power consumption

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can be reduced by deploying multi-antenna BSs, but the power consumption of the system also increases with the increase of the number of antennas, because the increase

Fig. 3. Power consumption under different macro cell antennas

of static part Pstatic in the circuit obviously exceeds the decrease of dynamic part Pdynamic . Therefore, the number of macro cell antennas cannot be increased indeﬁnitely and the power consumption of static circuits cannot be ignored. Meanwhile, the power consumption is the least when the small cell distance from macro cell is around 350 m.

Fig. 4. Comparison of Gaussian distribution and uniform deployment

Small Cell Deployment Based on Energy

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This paper also compares Gaussian distribution of small cell with uniform deployment. As can be seen from Fig. 4, under different QoS constraints, the power consumption required to deploy small cell in Gaussian distribution mode in a single cell is smaller. It provides meaningful reference for the deployment of small cell and can be further studied.

Fig. 5. User signal-to-interference-noise ratio diagram under different scenarios

Considering the multi-cell scenario, Fig. 5 is the SINR cumulative distribution function (CDF) under different deployment scenarios. Comparing the multi-cell system model with the traditional cellular network and the system model without dividing

Fig. 6. Optimal inner region radius map

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Macro Cell and Small Cell areas, it can be seen that the multi-cell system model in this paper is better than others. It can be seen from Fig. 6 that the system EE increases with the increase of the number of BS antennas, and the system EE is close to optimal when the number of

Fig. 7. Optimal small cell density

antennas reaches 120. At the same time, when the radius of the internal area is 500 m, the system has the optimal EE. We know that unlimited deployment of small cell is neither feasible nor practical. Therefore, the density of small cell is simulated. As shown in Fig. 7, the optimal EE are obtained when the density is k ¼ 7 BS=km2 and the macro cell antenna number is M ¼ 120.

5 Conclusion In this paper, we have proposed and investigated the deployment of small cell combined with massive MIMO dual-layer heterogeneous network. Research and simulations show that the combination of massive MIMO and small cell deployment can effectively improve the EE of the heterogeneous network. In a single-cell uniform deployment scenario, the power consumption of the system is minimal when the small cell is deployed at an optimal distance from the macro cell. At the same time, we consider the power consumption of static circuits, which affects the system EE and cannot be ignored. Considering the multi-cell non-uniform deployment, the system EE can be greatly improved by choosing the optimal density of small cell, which shows that small cell is a promising solution for maximum EE deployment.

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References 1. Andrews JG, Buzzi S, Choi W et al (2014) What will 5G be. IEEE J Sel Areas Commun 32(6):1065–1082 2. Bjornson E, Jorswieck EA, Debbah M et al (2014) Multi-objective signal processing optimization: the way to balance conflicting metrics in 5G systems. IEEE Signal Process Mag 31(6):14–23 3. Larsson EG, Edfors O, Tufvesson F et al (2014) Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2):186–195 4. Hoydis J, Kobayashi M, Debbah M (2011) Green small-cell networks. IEEE Veh Technol Mag 6(1):37–43 5. Li C, Zhang J, Letaief KB (2014) Throughput and energy efﬁciency analysis of Small Cell networks with multi-antenna base stations. IEEE Trans Wireless Commun 13(5):2505–2517 6. Björnson E, Kountouris M, Debbah M (2013) Massive MIMO and small cells: improving energy efﬁciency by optimal soft-cell coordination. In: International conference on telecommunications (ICT). Casablanca, May 2013 7. Ng DWK, Lo ES, Schober R (2012) Energy-efﬁcient resource allocation in OFDMA systems with large numbers of base station antennas. IEEE Trans Wireless Commun 11(9): 3292–3304 8. Wang H, Zhou X, Reed MC (2014) Coverage and throughput analysis with a non-uniform small cell deployment. IEEE Trans Wireless Commun 13(4):2047–2059 9. Cui S, Goldsmith AJ, Bahai A (2005) Energy-constrained modulation optimization. IEEE Trans Wireless Commun 4(5):2349–2360 10. Andrews JG, Baccelli F, Ganti RK (2011) A tractable approach to coverage and rate in cellular networks. IEEE Trans Commun 59(11):3122–3134 11. Bjornson E, Sanguinett L, Hoydis J et al (2015) Optimal design of energy-efﬁcient multiuser MIMO systems: is massive MIMO the answer. IEEE Trans Wireless Commun 14(6): 3059–3075 12. Huang Y, Palomar DP (2010) Rank-constrained separable semi-deﬁnite programming with applications to optimal beamforming. IEEE Trans Signal Processing. 58(2):664–678 13. Bjornson E, Matthaiou M, Debbah M (2015) Massive MIMO with non-ideal arbitrary arrays: hardware scaling laws and circuit-aware design. IEEE Trans Wireless Commun 14(8):4353–4368 14. Huh H, Caire G, Papadopoulos HC et al (2012) Achieving ‘massive MIMO’ spectral efﬁciency with a not-so-large number of antennas. IEEE Trans Wireless Commun 11(9): 3226–3239 15. Kim H, Chae CB, Veciana G et al (2009) Across-layer approach to energy efﬁciency for adaptive MIMO systems exploiting spare capacity. IEEE Trans Wireless Commun 8(8): 4264–4275 16. Peng J, Hong P, Xue K (2015) Energy-aware cellular deployment strategy under coverage performance constraints. IEEE Trans Wireless Commun 14(1):69–80

Research on Knowledge Mining Algorithm of Spacecraft Fault Diagnosis System Lianbing Huang(&), Wenshuo Cai, Guoliang Tian, Liling Li, and Guisong Yin Institute of Manned Space System Engineering, Beijing 100094, China [email protected]

Abstract. The change of telemetry data of spacecraft is usually caused by telecommand or fault, which conforms to the causality model of remote-control input and telemetry output under different conditions of spacecraft. Traditional expert system relies on static knowledge of experts to diagnose telemetry parameters. In order to solve the problem of rule-based expert system knowledge acquisition and less manual intervention, considering the characteristics of spacecraft telemetry, this paper proposes an expert knowledge acquisition algorithm based on successful data envelope line and conditional probability from two dimensions of analog and digital quantities respectively. Through data mining of historical telemetry, this algorithm achieves the threshold of analogue quantities and automatic extraction of causal rules at different stages of product life cycle. The experimental results show that the algorithm is effective and the simulation value is more accurate than the product design index and the redundancy of causal rules is less. After knowledge mapping, the algorithm can be applied in the spacecraft fault diagnosis expert system. Keywords: Data mining

Knowledge acquisition Causal rule

1 Introduction In recent years, with the characteristics of many parallel missions, short development cycle, high launch density and long on-orbit operation time, the downlink telemetry data volume will increase exponentially compared with the previous spacecraft, both during the comprehensive test period and in-orbit operation phase. How to use data mining technology to mine useful information from mass downlink telemetry data and apply it to practical projects to improve the quality and development efﬁciency of spacecraft products is an urgent problem to be considered and solved. At present, most of the current fault diagnosis modes of spacecraft in China rely on expert systems [1]. The telemetry data is sent down to the expert system, and the inference engine infers, analyses and judges the telemetry according to the knowledge and rules in the knowledge base, and outputs the diagnosis results to the users through the man-machine interface. The knowledge of expert system is the basis for expert system to draw conclusions. The level of knowledge determines the success or failure of system diagnosis [2]. At this stage, expert knowledge acquisition still depends on expert manual editing rules in advance. This semi-automatic diagnosis mode is not © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 20–27, 2020 https://doi.org/10.1007/978-981-13-9409-6_3

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intelligent enough, and has the problems of low efﬁciency and poor flexibility. Therefore, this paper aims to solve the problem of automatic knowledge acquisition and engineering application in spacecraft expert system based on data mining technology.

2 Background Using data mining technology to analyze and intelligently learn historical telemetry data, ﬁnd fault signs in time and take effective preventive measures, it is possible to avoid serious failures or accidents of spacecraft. NASA has developed some data driver applications for spacecraft fault detection and diagnosis, such as Orca system for space shuttle, IMS system for International Space Station, and so on. At present, NASA has developed some data driver applications for spacecraft fault detection and diagnosis. The research in this ﬁeld is still in its infancy.

3 Introduction of Expert System At present, there are many mature expert system products and tools that can be used in the rapid development of spacecraft fault diagnosis expert system, such as CLIPS, EXSYS, G2, etc. [3]. The United States, Russia, Japan and other countries have developed a number of expert systems for spacecraft fault diagnosis. Typical expert system structure, as shown in Fig. 1, mainly includes inference engine, knowledge base, interpreter, man-machine interface and other modules. Reasoning engine is responsible for translating knowledge into internal executable computer language according to certain rules of knowledge base knowledge, interpreter is responsible for translating knowledge into internal executable computer language according to speciﬁc grammar rules, domain experts are responsible for editing diagnostic knowledge and submitting it to the database, and users are responsible for reviewing the diagnostic results of daily telemetry data and processing the diagnostic results in time. Obviously, the knowledge base is the mapping of experts’ knowledge in the computer, and the inference engine is the mapping of the ability of using knowledge to reasoning in the compute [4].

Fig. 1. Composition of typical expert system

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4 Requirement Analysis Spacecraft telemetry information can be roughly divided into analog and digital quantities. Analog quantity refers to the quantity that changes continuously in a certain range, such as current, voltage, power, etc. Physical quantities which are discrete in time and quantity are called digital quantities, such as valve switch state, bus communication state, etc. At present, in spacecraft fault diagnosis expert system, the diagnosis of analog signals mainly depends on threshold values, such as maximum and minimum currents; for digital diagnosis, it mainly depends on event-driven, such as judging the change of digital signals when sending remote control instructions. The current model has the following shortcomings: (1) The design index range of analog threshold is large. If it is directly converted into knowledge, there is a risk of missing fault information. (2) It is difﬁcult to identify the identity of experts. There are some gaps in the knowledge compiled by different technicians, which affects the diagnostic ability of the system. (3) The internal correlation of telemetry data is easy to neglect, and the deep-seated potential faults are difﬁcult to ﬁnd; the change of digital quantity caused by a single remote control command is easy to ﬁnd, and the causal relationship between remote control command chain and telemetry is difﬁcult to excavate. (4) The mode of manual editing knowledge is inefﬁcient, and it is difﬁcult to meet the needs of mass telemetry data downlink and long-term on-orbit operation of subsequent spacecraft.

5 Overall Design The core idea of fault diagnosis system based on data mining is to acquire the operation status and knowledge of spacecraft subsystem equipment by analyzing historical data, so as to solve the problem of knowledge acquisition. In the design of data mining algorithm, the characteristics of spacecraft telemetry and rule-based expert system are fully considered, and the knowledge that can be translated directly is mined to realize engineering application. This paper divides the database telemetry data into two parts: analog and digital. Time and remote control instructions are used as auxiliary variables to mine the data from two dimensions. The general framework is as follows (Fig. 2). 5.1

Analog Telemetry Information Mining

The core idea of analog data mining is to extract speciﬁc thresholds by using successful historical data and corresponding data mining methods. Data envelope analysis, such as longitudinal test and test, is carried out for similar or similar products with successful flight experience. Successful envelope is constructed and threshold is extracted. Data Envelopment Line (DEL) analysis steps: Firstly, the analysis object is determined, and the normality of telemetry parameters is tested by Epps-Price method. Then, outliers in successful data are screened and eliminated by Grubbs test method.

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Fig. 2. Overall framework of expert knowledge mining

Finally, the envelope of data is obtained by using the principle of single-valued control chart. The envelope range consists of the upper bound of envelope UE and the lower bound of envelope LB. The calculation formulas are shown in 1 and 2. UB ¼ minfx þ 3 r; maxfxðiÞ ; i ¼ 1; 2; mgg

ð1Þ

LB ¼ minfx 3 r; minfxðiÞ ; i ¼ 1; 2; mgg

ð2Þ

Including: x ¼ m1 5.2

m P i¼1

sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ m P xðiÞ ; r ¼

ðxðiÞ xÞ

i¼1

m1

.

Digital Telemetry Information Mining

Telemetry commands and digital changes have a time-dependent relationship, and there is a causal relationship between them. It can be understood that if the premise of event B is to send remote control command event A, then event A must occur before event B, and this logical relationship conforms to the thought of conditional probability. The knowledge obtained from mining can not be directly applied to practical projects. This paper focuses on the use of conditional probability statistics to discover the relationship between remote control commands and digital changes, and to form causal knowledge. Deﬁnition 1 Remote control command sequence (CS). A set of remote control instructions arranged in chronological order is called the sequence of remote control instructions, which is recorded as CS, CS = , satisfying ei.timestamp

0.3 the curves of BER increases at a faster rate when ρt increases.

Performance Analysis of SSK in AF Relay

33

Fig. 2. BER performance with diﬀerent correlation coeﬃcient versus SNR.

Fig. 3. The BER performance versus correlation coeﬃcients.

5

Conclusion

In this paper, we have investigated the error performance of the transmit correlated dual-hop AF-SSK system. A closed form average BER expression is obtained. Computer simulation validates the accuracy of the theoretical analysis.

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From the simulations we observe that the theoretical BER curves are consistent with the simulation curves at high SNR. Besides, the transmit coeﬃcient ρt has a great impact on the BER performance of the system especially when ρt > 0.3.

References 1. Nosratinia A, Hunter TE, Hedayat A (2004) Cooperative communication in wireless networks. IEEE Commun Mag 42(10):74–80 2. Yang P, Di Renzo M, Xiao Y, Li S, Hanz L (2015) Design guidelines for spatial modulation. IEEE Commun Surveys Tuts 17(1):6–26 3. Som P, Chockalingam A (April 2013) End-to-end BER analysis of space shift keying in decode-and-forward cooperative relaying. In: Proceedings of the IEEE wireless communications network conference (WCNC), Shanghai, China 4. Altin G, Aygolu U, Basar E, Celebi ME (2017) Multiple-input-multiple output cooperative spatial modulation systems. IET Commun 11(15):2289–2296 5. Mesleh R, Ikki SS (2013) Performance analysis of spatial modulation with multiple decode and forward relays. IEEE Wireless Commun Lett 2(4):423–426 6. Mesleh R, Ikki SS, Alwakeel M (2011) Performance analysis of space shift keying with amplify and forward relaying. IEEE Commun Lett 15(12):1350–1352 7. Mesleh R, Ikki SS (2015) Space shift keying with amplify-and-forward MIMO relaying. Trans Emerg Telecommun Technol 26(4):520–531 8. Koca M, Sari H (September 2012) Performance analysis of spatial modulation over correlated fading channels. In: Proceedings of the IEEE VTC-Fall, (2012) Quebec City, Canada, pp 1–5 9. Gradshteyn IS, Ryzhik IM (March 2007) Table of integrals, series, and products, 7th ed. In: Jeﬀrey A, Zwillinger D (eds) Academic Press 10. Abramovitz M, Stegun IA (1974) Handbook of mathematical function with formulas, graphs, and mathematical tables. Dover publications, New York 11. Proakis JG (2007) Digital communications, 5th edn. McGraw-Hill, New York

The JSCC Algorithm Based on Unequal Error Protection for H.264 Jiarui Han1, Jiamei Chen1(&), Yao Wang2, Ying Liu1, Yang Zhang1, and Liang Qiao1 1

College of Electrical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China [email protected], [email protected] 2 Communication Department, Shenyang Artillery Academy Company, No. 31 Dongdaying Avenue, Dongling Area, Shenyang 110161, China

Abstract. Joint Source and Channel Coding (JSCC) considers the source coding and channel coding of communication system to optimize the design. Firstly, an Unequal Error Protection (UEP) scheme based on the Rate Compatible Punctured Turbo(RCPT) code is proposed. The simulation results show that the UEP scheme is superior to the Equal Error Protection (EEP) scheme when the channel rate is ﬁxed. Both the objective data and subjective video show that the UEP scheme is superior to the EEP scheme without increasing channel redundancy and achieves channel coding design based on source characteristics. Secondly, using UEP schemes with different bit rates, a channel adaptive JSCC system is designed. The system can adjust the bit rate allocation between sources and channels adaptively according to channel conditions, and realize the joint source and channel coding. Experiments show that the video restoration quality of this scheme is better than that of the single UEP scheme. Keywords: JSCC

UEP RCPT H.264

1 Introduction In the wireless and mobile network environment, the demand of multimedia applications is growing. The technology of wireless video transmission has been more widely used. The coding efﬁciency of video signals is also increasing. Accompanied by many the introduction of new access technologies, wireless channel has had higher throughput, which makes the transmission of video signals in the radio channel is possible. However, wireless networks have time-varying, limited bandwidth, relatively higher bit error rate characteristics, and the channel change not only varies greatly with the base station and terminal location and direction, but also causes serious and sudden decline of the error. Therefore, video coding methods for wireless communication should not only have a high compression capability but should also have a good anti-error performance. The objective of the source coding is under the target bitrate premise, making the coding distortion minimum [1]. And the objective of the channel coding is under the permissible channel capacity conditions, transmitting data as reliably as possible. In the bandwidth limited multimedia communications system, these two objectives are © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 35–41, 2020 https://doi.org/10.1007/978-981-13-9409-6_5

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contradictory. Therefore, if the source encoder and channel encoder are designed separately, the purpose of efﬁcient and reliable transmission of the information will not be achieved. The solution of this problem is to jointly consider the source coding and channel coding [2–4], that is, the joint source and channel coding (JSCC) [5]. JSCC can cascade up the source encoder with the channel encoder while maintaining the independence between them, and jointly optimize their encoded parameters. By reasonably allocating the transmission bandwidth between the multimedia data and the protected data, the source-channel coding system with joint optimization of parameters can be realized. JSCC on the one hand can reduce system complexity, and on the other hand can achieve the purpose of the joint coding by assigning rate between the source and channel [6]. Based on the H.264 [7] video standard and the principle of Rate Compatible Punctured Turbo (RCPT), this paper proposes a unequal error JSCC protection method for the H.264 video information. Firstly, H.264 video information is compressed according to different important levels, and then different levels of video information are protected by different bit rates of RCPT.

2 H.264 Data Segmentation In this section, the data segmentation mode of H.264 is proposed to classify the three data types A, B and C into two categories. Namely, type A as the ﬁrst important class and type B and C as the second important class. In addition, as long as the bandwidth is allowed, video data transmission does not need to constrain the code rate and quality. However, in the actual channel environment, when the channel quality is poor, the current channel state cannot meet the transmission rate required by the video stream. Under these circumstances, the encoding and sending rate of video stream must be controlled. The rate control algorithm is to dynamically adjust the parameters of the encoder and obtain the target bit number. In H.264, after quantization is located in DCT, the quantized DCT coefﬁcients will appear more 0 values, which will help VLC to achieve higher compression ratio. The greater the value of 0, the less bits of bits required after VLC, the lower the code rate. H.264 USES a non-vector quantizer. Deﬁnitions and implementations are complex and need to be avoided involving floating-point operations. The basic forward quantizer operation is as follows. Zij ¼ roundðYij =Qstep Þ

ð1Þ

where, Yij is a transformation coefﬁcient described above, Qstep is the size of quantization step, Zij is a quantization coefﬁcient. Changing the quantiﬁcation factor can control the change of code rate in a large range. Quantitative change factors can achieve the goal of adaptive adjustment rate, but it also has certain side effects, quantitative factors besides of rate effect, is have a signiﬁcant impact on video quality, quantitative parameters and bit rate are contradictory.

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3 Design and Implementation of Unequal Error Protection Scheme As shown in Fig. 1, the basic process of unequal error protection schemes is based on data segmentation, and channel encoders selectively implement UEP mechanisms for different classes of data. Under the EEP mechanism, a uniﬁed rate error control channel coding is applied to the data.

Original video file *. yuv

Video file after process *.yuv

JM10.2 video coding

JM10.2 video decoding

File reading

File output

Bit stream segmentation

Bit stream merging

Important part

Low bit rate RCPT coding

Unimportant part

High bit rate RCPT coding

Low bit rate RCPT decoding

Bit merging

MQAM modulation

Unimportant part

Important part

High bit rate RCPT decoding

MQAM demodulation

Channel

MQAM demodulation

Fig. 1. Flow chart of unequal error protection scheme

In order to compare the performance of UEP and EEP, the channel code rate is ﬁxed to 3 Mbps, and the Rate control function in the JM10.2 software coding parameter is opened. The RateControlEnable is set to 1 and the bit rate is set to 1.5 Mbps so that the total bit rate of the channel is ﬁxed and the total bit rate of the Turbo code should be ﬁxed when the channel is uploaded and transported. The total bit rate is determined by the next type Pc ki roverall ¼ Pc i¼1 i¼1 ðki =ri Þ

ð2Þ

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Among them, roverall is the total rate of the RTCP code, ki is the length of each data classiﬁcation frame, and ri is the rate of each data classiﬁcation.

4 Performance Evaluation 4.1

Campare the Function Between UEP and EEP

According to the formula, we design the following rate matching, as the Table 1 shows. Among the four conﬁgurations, although each data classiﬁes the sub-bitrate into different numbers, it can be calculated that the total bitrate is always 1/2, thus, the formula of channel code rate is 1.5 Mbps * 2 = 3 Mbps. Table 1. Distribution of same bit rate UEP and EEP Encoding method Match1 Match2 Match3 Match4

EEP UEP1 UEP2 UEP3

Channel rate Partition A 1/2 4/9 2/5 1/3

Partition B 1/2 10/19 5/9 5/8

Partition C 1/2 10/19 5/9 5/8

In the H2.64 frame, the maximum movement search scope is 16 pixels, the maximum number of reference frames is stated as the ﬁrst 5 frames. Entropy coding type adopts CAVLC. The size of the image is 176 * 144. The frame rate is set to 30 fps. The encoding frame is I-P-P-P, and the B frame is not inserted. The value of the quantization parameter is uniformly set to 35. And the channel is a Gauss channel. According to the different channel conditions, UEP and EEP will eventually reconstruct the PSNR-Y value of the video, as shown in Fig. 2. From the graph, we can see that the 3 UEP schemes improve the PSNR-Y value of the image brightness component without additional channel redundancy. The reason that UEP3 is superior than UEP2, and UEP2 is superior than UEP1 is that the rate of class A data classiﬁcation in the schemes is 1/3 < 2/5 < 4/9. That is, the protection for the important data is reduced in turn. Finally, because the channel redundancy is the same, and the code source rate is certain, when the signal to noise ratio increases to a certain extent, the PSNR-Y value will no longer rise. This value is determined by the source compression ratio. Figure 3 shows the restoration quality of video information under different channel protection methods. The video is taken at a construction site. As the Fig. 3 shows, when the EEP method is used to restore the image, the image and the scene can not be seen clearly because of the poor channel condition. However, the quality of the video

The JSCC Algorithm Based on Unequal Error Protection for H.264

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40

PSNRY (dB)

35

30

25

20

15 0.9

EEP UEP3 UEP2 UEP1

1

1.1

1.2

1.3

1.4

1.5

1.6

Eb/No(dB)

Fig. 2. UEP and EEP reconstruction video PSNR-Y values

recovery from UEP1 to UEP3 is higher in turn. And the recovery image of UEP3 is only a small number of mosaic in the characters’ chin and the scene. From the previous PSNR curve, it is found that the PSNR-Y in the UEP3 mode is 6.66 dB more than the EEP, and the PSNR-Y in the UEP1 mode is also improved 3.58 dB than the EEP. So from the visual sense of restoring the image and the objective data from PSNR-Y data statistics, the effect of using UEP to restore the quality of the image is consistent. Both show that the quality improvement of the image recovery using the UEP method is very obvious.

(a) The second frame of original video

(b) The second frame of EEP

(c) The second frame of UEP1, UEP2 and UEP3

Fig. 3. UEP and EEP video recovery map

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Performance Analysis of UEP with Different Bit Rate

From the previous section, it is concluded that the UEP method can effectively improve the quality of the restored video without increasing the channel redundancy. In order to further study the relationship between source and channel coding, we study the performance of various UEP schemes with different bit rates and their relationship with the source code parameter QP. In the DP mode, the H.264 video coding standard uses three different types of data partitions. According to different channel states, the source code and channel coding can be matched effectively. The rate of data classiﬁcation is as shown in Table 2.

Table 2. UEP allocation scheme with different bit rate Encoding method Match1 Match2 Match3

UEP1 UEP2 UEP3

Channel rate Partition A 1/3 2/5 4/9

Partition B 1/2 1/2 1/2

Partition C 1/2 1/2 1/2

From Table 2 we can see that the channel protection for partition A decreases in turn, and same for partition B and C. The simulation results shows that, at the low signal to noise ratio, the highest bit rate is obtained. With the improvement of channel condition, the bit error rate decreases. The three cause of the reverse arrangement is that the UEP1 has the lowest bit rate and the channel redundancy is the most. When the channel rate is ﬁxed to 3 Mbps, the data information is reduced and the PSNR-Y value is reduced. Therefore, the higher the channel redundancy, the better the video quality can be restored. In the case of limited channel bandwidth, the more the channel redundancy, the greater the compression ratio of the source, and the effect of the coding parameters on the bit rate. When the source compression is too large, it is not possible to restore the video quality no matter how well the channel protection is done (Fig. 4). 40

PSNR Y (dB)

35

30

25

20 UEP1 UEP2 UEP3

15 0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

Eb/No(dB)

Fig. 4. Comparison of PSNR_Y values for different bit rate UEP schemes

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5 Conclusion This paper proposes a JSCC protection method for H.264 video information based on the idea of unequal error protection. Firstly, UEP and EEP are set at the same total bit rate. In the Gauss white noise channel, a lot of simulation veriﬁcation is done by using MATLAB software. Without increasing redundancy, important information is effectively protected. Thus, the system can enhance the channel protection ability in the case of high bit error rate. Compared with EEP protection mode, the quality of video restoration is signiﬁcantly improved in decoding. Then, the protection of information by different bit rate protection measures is studied. The simulation results show that the JSCC technology can ensure the reliable transmission of video information and effectively utilize the limited bandwidth resources of the channel. Using limited redundancy to achieve effective error protection, a better trade-off between redundancy consumption and error protection is obtained. Acknowledgements. This research was supported by National Natural Science Foundation of China (Grant No. 61501306), Doctoral Scientiﬁc Research Foundation of Liaoning Province (Grant No. 20170520228).

References 1. Chen YM, Wu FT, Li CP, Varshney PK (2019) An efﬁcient construction strategy for nearoptimal variable-length error-correcting codes. IEEE Commun Lett 23(3):398–401 2. Köken E, Tuncel E (2017) Joint source-channel coding for broadcasting correlated sources. IEEE Trans Commun 65(7):3012–3022 3. Deka S, Sarma KK (2017) Joint source channel coding with MC-CDMA in capacity approach. In: 4th international conference on signal processing and integrated networks (SPIN), pp 489–493, Feb 2017 4. Mamatha AS, Sagar K, Sumanth J, Tharun Thejus JP, Varun R, Singh V (2018) Joint source channel coding for hyperspectral imagery. In: IEEE India council international conference (INDICON), pp 16, Oct 2018 5. Chen C, Wang L, Liu S (2018) The design of protograph LDPC codes as source codes in a JSCC system. IEEE Commun Lett 22(4):672–675 6. He J, Li Y, Wu G, Qian S, Xue Q, Matsumoto T (2017) Performance improvement of joint source channel coding with unequal power allocation. IEEE Wireless Commun Lett 6 (5):582–585 7. Zhu X, Chen CW (2016) A joint source-channel adaptive scheme for wireless H.264/AVC video authentication. IEEE Trans Inf Forensics Secur 11(1):141–153

Mean-Field Power Allocation for UDN Yanwen Wang1, Jiamei Chen1(&), Yao Wang2(&), Qianyu Liu1, and Yuying Zhao1 1 College of Electrical and Information Engineering, Shenyang Aerospace University, No. 37 Daoyi South Avenue, Shenbei New Area, Shenyang, China [email protected] 2 Department of Air Defense Forces, Noncommissioned Ofﬁcer Academy, Institute of Army Artillery and Air Defense Forces, No. 31 Dongdaying Avenue, Shenhe Area, Shenyang, China [email protected]

Abstract. Ultra Dense Network (UDN) is an effective solution to the explosive growth of trafﬁc in the future 5G networks. In this paper, a mean-ﬁeld power allocation algorithm is proposed for UDN. It imbeds the power allocation decision problem into a Dynamic Stochastic Game (DSG) model. And then it ﬁnds the optimal decision by deriving the model into a mean-ﬁeld game model. The simulation results show that compared with the other methods, the proposed method can achieve better performance in terms of the CDF and the Utility EE, and can also guarantee the Quality of Service (QoS). Keywords: Mean-ﬁeld theory Dynamic stochastic game

Ultra dense network Power control

1 Introduction UDN greatly improves the system capacity and the flexibility of services sharing among various access technologies and coverage levels [1, 2]. However, it still faces a number of challenges. A lot of micro base stations in UDN are more likely to be deployed by users themselves without planning, which making the network topologies and features extremely complex [3]. The unplanned and dense deployment of MiBSs in UDN will bring a lot of energy consumption to the network. This problem make the power control more challenging than the traditional sparse network deployment. Therefore, it is necessary to ﬁnd an efﬁcient power allocation scheme [4]. For the power allocation problems in the traditional networks, the convex optimization theory can provide the best solution. However, it needs the global network information and centralized control, thus producing signiﬁcant signaling overhead and computing complexity [5, 6]. In order to avert this limitation of optimization, the game theory has attracted wide attention. It can describe the rational behaviors, analyze the dynamic equilibrium, and design the distributed control algorithms [7]. But, the huge number of the access points in UDNs will lead to the well-known curse of

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 42–47, 2020 https://doi.org/10.1007/978-981-13-9409-6_6

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dimensionality in Game Theory. Because of such large participation of the equipments, the methods used in the small scale networks are insufﬁcient to explore the power optimization of UDNs [8, 9]. In this paper, a joint resource allocation method for energy efﬁcient power control in UDNs networks is proposed. The strategy transforms the difﬁcult dynamic stochastic game problem into a relatively low complexity mean ﬁeld equilibrium problem. The ultimate goal is to optimize the power control strategy, and ultimately improve energy efﬁciency.

2 Problem Description of Dynamic Stochastic Game In the UDNs, suppose B BSs share the spectrum with bandwidth x, and B ¼ f1; 2; . . .; b; . . .Bg is the set of the BSs. These BSs serve a total of M users, which is expressed as set M and M ¼ M1 [ . . . [ MB , where Mb is a user set served by the BS b 2 B. The channel gain between the user m 2 Mb and the BS b is deﬁned as hbm ðtÞ. With the hypothesis of an additive Gauss white noise with zero mean and variance r2 , the instantaneous data rate of user m is: rbm ðtÞ ¼ x log2

pb ðtÞjhbm ðtÞj2 1þ Ibm ðtÞ þ r2

! ð1Þ

where pb ðtÞ 2 0; pmax is the transmission power of BS b, and Ibm ðtÞ ¼ b P 2 8b0 2Bnfbg pb0 ðtÞjhb0 m ðtÞj is the interference to BS b from the other BSs. Suppose Eb ðtÞ

is the energy available for BS b at time t. Thus, we can deﬁne Xb ðtÞ ¼ ½Eb ðtÞ; hb ðtÞT as the system state in time t, and its state space is X ¼ ðX1 ðtÞ [ [ XB ðtÞÞ. Where, hb ðtÞ ¼ ½hbm ðtÞm2M . With the above basic assumptions, the utility of the BSs at a time t can be deﬁned as ub ðpb ðtÞÞ ¼ rb ðt; Xb ðtÞÞ=ðpb ðtÞ þ p0 Þ

ð2Þ

where, ub ðÞ represents the packet success rate and also the energy efﬁciency. And our goal is to determine the control strategy for each base station to maximize the utility function ub ðÞ while guaranteeing the QoS of users. The limiting time average Pt1 _ pðsÞ. expectation of the control variables pb ðtÞ can be expressed as p ¼ limt!1 1t s¼0 And then, the problem of maximum utility of BS b can be written as follows: max ub ð^pb ; ^pb Þ ^pb

s:t:8m 2 Mb ð1Þ; ð2Þ yb ðtÞ 2 yb ðt; xÞ 8t

ð3Þ

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In this way, the power control problem can be expressed as a dynamic stochastic game: g ¼ B; fPb gb2B ; fXb gb2B ; fCb gb2B

ð4Þ

where Cb is the average utility of participant b 2 B, and depends on the state xðT Þ ! xðT 0 Þ. When jBj [ 2, solving the coupled equations of jBj is very complex.

3 Mean-Field Solution to the Problem The SDG becomes more and more difﬁcult to analyze when jBj increases. Fortunately, our problem has a special structure which can simpliﬁes the problem when jBj is large. Indeed, from a SBS standpoint, what matters in terms of utility is a weighted sum of the actions. The relevant quantity which affects the utility of SBS b is the impact of other SBS on the given SBS b 2 B appears as a form of interference: X p 0 ðt; xðtÞÞjhb0 m ðtÞj2 ð5Þ Ibm ðt; xðtÞÞ ¼ 8b0 2Bnfbg b It can be proven that if Ibm ðt; xðtÞÞ converges, h so SDG converges ito a mean-ﬁeld ^ ^ game. As a result, in the MF, the solution C t; x ðtÞ ; q t; x ðtÞ of mean-ﬁeld ^ equilibrium is the solution of dynamic stochastic game of jBj SBSs. Where, C t; x ðtÞ ^ is obtained by solving the HJB equation, and q t; x ðtÞ is educed by solving the FPK equation. Therefore, the optimal transmission power can be given from the following: @ h ^ i C t; x ðtÞ þ f ðtÞ @x pðtÞ 1 @ 2 h ^ i þ trðX2z 2 Þ C t; x ðtÞ @x 2

p ðtÞ ¼ arg max½Xt

ð6Þ

4 Simulation Results The simulation evaluates the relative performance of MF-Game algorithm proposed in this paper by comparing it with an existing Stackelberg-Game algorithm in literature [10] for high and low loads k. Here the load k = MB represents the number of UEs served by an SBS. We use low load for k = 3 UEs per SBS and high load for k = 8 UEs per SBS. The SBS density ks of the system refers to the number of base stations per square kilometer. From Fig. 1, it can be seen that, for a dense network, our proposed method improves the utility EE of about 4.2% compared to the existing baseline model with k = 3. However, as the user load increases to k = 8, this EE improvement will reach up

Mean-Field Power Allocation for UDN

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Fig. 1. Utility EE for high and low loads k under different density of SBSs

to 20.1%. In dense scenarios, our proposed method has more advantages in EE because it can better adapt to the dynamic characteristics of the network. The common point of these two methods is that they both try to optimize the transmission power to maximize EE. Nevertheless, as the number of the users is huge, the conventional game theory Stackelberg-Game algorithm must face the well-known curse of dimensionality. Fortunately, MF-Game algorithm, as an advanced game-theoretic method, is very expert at analyzing the control policy. Figure 2 shows that the total throughput of the system increases with the increase of the number of available base stations. This is because when the total number of base stations increases, the number of base stations available to users increases, that is, the

Fig. 2. Total throughputs of users for high and low loads k under different density of SBSs

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constraints of the original optimization problem become larger, so the total throughput of the system will increase to a certain extent. The density of SBS is large and the interference is large, and the growth rate of throughput is slow. With the increase of user density, the system throughput is increasing, but the rate of increase will be more and more gentle, and eventually will be stable, which is due to the limited total capacity of the system.

5 Conclusion A power control method based on mean ﬁeld is proposed in this paper. In order to improve the energy efﬁciency of the network, the original DSG problem is transformed into an average ﬁeld problem which is easy to solve, and the optimal power control method is sought. The simulation results show that the mean ﬁeld method proposed in this paper is superior to the classical game theory method in terms of energy efﬁciency and network throughput. Acknowledgements. This research was supported by National Natural Science Foundation of China (Grant No. 61501306), Doctoral Scientiﬁc Research Foundation of Liaoning Province (Grant No. 20170520228), College Students’ innovation and entrepreneurship training program (Grant No. 110418092).

References 1. de Mari M, Calvanese Strinati E, Debbah M, Quek TQS (2017) Joint stochastic geometry and mean ﬁeld game optimization for energy-efﬁcient proactive scheduling in ultra dense networks. IEEE Trans Commun Netw 3(4):766–781; Voronkov A (2004) EasyChair conference system. Retrieved from easychair.org 2. Aziz M, Caines PE (2017) A mean ﬁeld game computational methodology for decentralized cellular network optimization. IEEE Trans Control Syst Technol 25(2):563–576 3. Samarakoon S, Bennis M, Saad W, Debbah M, Latva-aho M (2015) Energy-efﬁcient resource management in ultra dense small cell networks: a mean-ﬁeld approach. In: 2015 IEEE global communications conference (GLOBECOM), San Diego, CA, pp 1–6 4. Yang C, Li J, Sheng M, Anpalagan A, Xiao J (2018) Mean ﬁeld game-theoretic framework for interference and energy-aware control in 5G ultra-dense networks. IEEE Wirel Commun 25(1):114–121 5. Yang C, Li J, Guizani M (2016) Cooperation for spectral and energy efﬁciency in ultra-dense small cell networks. IEEE Wireless Commun. 23:64–71 6. Al-Zahrani AY, Yu FR, Huang M (2016) A joint cross-layer and colayer interference management scheme in hyperdense heterogeneous networks using mean-ﬁeld game theory. IEEE Trans Veh Technol 65(3):1522–1535 7. Park J, Jung SY, Kim SL, Bennis M, Debbah M (2016) User-centric mobility management in ultra-dense cellular networks under spatio-temporal dynamics. In: Proceedings of IEEE global communication conference (GLOBECOM), pp 1–6 8. Xiao Y, Niyato D, Han Z, DaSilva LA (2015) Dynamic energy trading for energy harvesting communication networks: a stochastic energy trading game. IEEE J Sel Areas Commun 33 (12):2718–2734

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9. de Mari M, Calvanese Strinati E, Debbah M, Quek TQS (2017) Joint stochastic geometry and mean ﬁeld game optimization for energy-efﬁcient proactive scheduling in ultra dense networks. IEEE Trans Cognitive Commun Netw 3(4):766–781 10. Shaﬁgh AS, Mertikopoulos P, Glisic S (2016) A novel dynamic network architecture model based on stochastic geometry and game theory. In: 2016 IEEE international conference on communications (ICC), Kuala Lumpur, pp 1–7

Design of Gas Turbine State Data Acquisition Instrument Based on EEMD Zhonglin Wei(&), Pengyuan Liu, Feng Wang, and Tianhui Wang Shijiazhuang Campus, Army Engineering University, No. 97, Heping West Road, Shijiazhuang 050003, Hebei, China [email protected]

Abstract. In order to carry out the condition monitoring tasks in working process of gas turbine, a multi-channels data acquisition instrument was designed based on high-speed AD and FPGA, which can collect temperature, rotational speed and vibration signals in real time. The data is transmitted to PC through USB interface, then PC uses EEMD to analysis the vibration data and LabVIEW software to process and display data. At the same time the instrument has both on-line data processing module and storage module, and it can analyze data offline in special working environment. The instrument is characterized by good communication ability with host computer and strong anti-interference ability, so it can provide reliable state data for fault detection and analysis of gas turbine, and it is feasible and practical to carry out data acquisition and condition monitoring in a complex environment. Keywords: Data acquisition monitoring

Empirical mode decomposition Condition

1 Introduction In order to monitor the condition of gas turbine, a high-precision real-time acquisition instrument was designed, which is mainly composed of sensors, signal acquisition and modulation circuits, with LabVIEW as the software platform. Considering the necessary conditions and special working environment of gas turbine fault detection, the acquisition instrument can collect high-speed and highprecision data from two vibration signals, two temperature signals, one speed signal and two switching signals, then transfers data to the host computer through USB interface, and real-time analyses the data.

2 Hardware Design The hardware of the gas turbine state data acquisition instrument consists of signal acquisition and conditioning, data processing, data transmission and power processing circuits, its hardware composition is shown in Fig. 1. External sensor signal is converted to A/D after conditioning circuit, and dual SDRAM is used for data cache in FPGA. Cached data is sent to PC through USB © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 48–55, 2020 https://doi.org/10.1007/978-981-13-9409-6_7

Design of Gas Turbine State Data Acquisition

49

CAN bus

Start signal

Optocoupler level conversion

Vibration signals

Operational amplifier

High-speed AD

Speed signal

Operational amplifier

Comparator

Temperature signals

DSP

SATA interface

FLASH FPGA SDRAM

Operational amplifier

SDRAM

USB interface

Fig. 1. Hardware block diagram

interface, and processed, displayed and stored by PC. The advantage of this method is that it has strong data processing ability, can conduct in-depth state analysis and fault detection of gas turbine, and display intuitively, and has a large amount of data storage. When the working environment of gas turbine is not suitable for the above way, the cached data can be transmitted to the DSP module through the FPGA, and on-line data processed by the DSP, then stored in the FLASH chip. The data in FLASH chip can be transmitted over a long distance through CAN bus, or can be taken out offline through USB interface for offline analysis. 2.1

Temperature Signal Measuring Circuit

The instrument uses K-thermocouple temperature measuring circuit which supports cold junction compensation. The AD sampling circuit of thermocouple adopts MAX31855 output converter, which can convert K-thermocouple signal into digital quantity, and output 14-bit signed data in read-only format through SPI compatible interface. The thermocouple measuring circuit is shown in Fig. 2. 2.2

Speed Signal Measuring Circuit

Speed measuring motor is used to measure speed signal. When the gas turbine works normally, the speed measuring motor outputs AC voltage signal. The frequency of the signal is proportional to the speed, so it is only necessary to collect the frequency information. In order to ensure that the signal amplitude meets the input requirements of the later circuit, a peak-cutting circuit is designed in the input stage of the speed signal measuring circuit. The input stage circuit is shown in Fig. 3.

Z. Wei et al.

R72

50

U15 1 GND 2 T3 T+ 4 VCC

L1 T1C115

T1_OUT T1_CS T1_CLK

MAX31855

L5

T1+

8 N.C 7 SO 6 /CS 5 SCK

VC33

C131

Fig. 2. Thermocouple measuring circuit

+15V

R100 C123

D16 D6 C111

R92 J6 BNC Input

AGND

AGND C116 D12

SIP-2P

R89 R101

AGND

AGND

Limited Output

-15V

Fig. 3. Input stage of speed signal measuring circuit

D2

C124

Design of Gas Turbine State Data Acquisition

51

In order to ensure the effective and undistorted transmission of the signal, the gain adjustable operational ampliﬁer circuit is used to amplify and shape the signal. Because the signal has been processed by op-amp and its DC component has been ﬁltered, a zero-crossing comparator circuit is used in the comparator stage to obtain the standard TTL square wave signal. The output signal is converted to level, then the frequency signal is sent to the FPGA. The transmission stage of speed signal measuring circuit is shown in Fig. 4.

1 2 3 4 5 6 7

U24 14 13 12 11 10 9 8

+

+ -

R96

4 5

ANALOG_+15V

C106

AGND

U21-A +

2

TTL

R97

D10

AGND

AGND

ANALOG_-15V

R94

1

AGND

3

R95

Clipping signal

2 WR1

Fig. 4. Transmission stage of speed signal measuring circuit

The R96 is the matching resistance, which can effectively eliminate ringing by choosing the appropriate resistance value. The D10 is a germanium diode, which can limit the reverse input signal of the comparator to −300 mV and protect the chip from damage. 2.3

Vibration Sensor Protection Circuit

R92

+24V

The built-in IC piezoelectric accelerometer is used to measure vibration signal. In order to ensure the normal operation of the sensor and protect the sensor at the same time, the vibration sensor protection circuit is designed, which has the protection function of open circuit and short circuit. The sensor protection circuit is shown in Fig. 5.

R90

R91

SIGALE

+24V

1 2 3 4

U20

8 7 6 5

Fig. 5. Sensor protection circuit

R93 LED

+24V

52

2.4

Z. Wei et al.

Data Transmission Circuit

The USB interface adopts CY7C68013, which supports the USB2.0 protocol. All USB interface operations are coordinated by FPGA. The CAN bus interface uses CTM1050T as the transceiver, and the controller uses SJA1000. The FPGA is directly connected to SJA1000 for modiﬁcation and debugging.

3 Software Design The acquisition software was developed based on LabVIEW programming, which is used to process the uploaded signals. In the working process of the acquisition instrument, the signals are collected by sensors, processed by FPGA and sent to the PC for data analysis and judgment. The software user interface is shown in Fig. 6.

Fig. 6. Software user interface diagram

Temperature and speed data are processed to display the speciﬁc values intuitively. Vibration is an important symbol of gear fault diagnosis, so it is necessary to analyze it in time domain, amplitude and frequency domain during data processing. 3.1

EEMD Algorithm

When gears fail, their vibration signals will show strong non-stationary characteristics due to AM and FM. Aiming at this feature and avoiding mode mixing, the core algorithm uses Ensemble Empirical Mode Decomposition (EEMD). EEMD is an efﬁcient and adaptive signal decomposition method, which is suitable for processing

Design of Gas Turbine State Data Acquisition

53

non-linear and non-stationary signals [1, 5]. It makes use of the statistic characteristic of uniform frequency distribution of Gauss white noise to make the signal with Gauss white noise have continuity on different scales, thus effectively solving the problem of mode confusion, and at the same time improving the resolution. The decomposition process of EEMD is as follows [2–4]: Step 1 Gauss white noise sequence is added to the target data. Step 2 Decomposing the Sequence: The sequence with Gauss white noise is decomposed into a family of intrinsic mode functions (IMFs) according to EMD algorithm. Step 3 Repeating the process: Adding different Gauss white noise sequences with the same amplitude each time, and repeating steps 1 and 2. Step 4 Take the mean of each decomposed IMF as the ﬁnal result, i.e. Cj ðtÞ ¼

N 1X Cij ðtÞ N i¼1

ð1Þ

In (1), Cj(t) denotes the jth IMF component obtained by EEMD decomposition of the original signal; N is the number of times of adding white noise. Assuming that the given time signal is X(t), the signal is transformed into Hibert transform: _

x ðtÞ ¼

1 p

Z

þ1 1

xðsÞ 1 ds ¼ xðtÞ ts pt

ð2Þ

_

Then the analytic signal of X(t) is zðtÞ ¼ xðtÞ þ jx ðtÞ. According to the properties of the analytic signal, the spectrum Z(jX) of Z(t) is equal to the frequency spectrum obtained by multiplying the negative frequency of (jX) to zero and the positive frequency to 2. 3.2

Vibration Analysis

The accelerometer selected by the acquisition instrument has a sensitivity of 15 mV/g, a sampling frequency of 100 kHz and a sampling point of 10,240. The original time domain signal measured in the actual working process is shown in Fig. 7. The time domain signal is decomposed by EEMD. The added white noise amplitude is 0.2 times the standard deviation of the original signal amplitude. The set aggregate number is 200. The extracted IMF is shown in Fig. 8. It can be seen that the clear spectrum can be obtained by EEMD and Hibert transform, and the phenomenon of mode aliasing can be effectively solved.

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Fig. 7. Time-domain diagram of the original signal

Fig. 8. IMF diagram

4 Conclusions The acquisition instrument achieves high-speed and accurate acquisition of temperature, rotational speed and vibration acceleration signals in actual working environment, and provides reliable data for fault detection and analysis of gas turbine. At the same time, the acquisition instrument has a good communication function with the host computer. It can be used as a fault detection instrument for equipment in special working environment, and it has good application flexibility and practicability.

References 1. Lin JS (2010) Gearbox fault diagnosis based on EEMD and Hilbert transform. J Mech Transm 34(5):62–64 2. Wu ZH, Huang NE (2008) Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv Adapt Data Anal 1:1–41

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3. Zhang J (2008) Analysis and improvement of modal aliasing in EMD algorithms. In: University Of Science and Technology of China Master Thesis, Hefei 4. Lei YG, He ZJ, Zi YY (2009) Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 23(4):1327–1338 5. Huang NE, Shen Z, Long SR (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Ser A 454:903– 995

Cramér–Rao Bound Analysis for Joint Estimation of Target Position and Velocity in Hybrid Active and Passive Radar Networks Chenguang Shi1,2, Wei Qiu2, Fei Wang2(&), and Jianjiang Zhou2 1

Science and Technology on Electro-Optic Control Laboratory, Luoyang 471009, China 2 Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing University of Aeronautics and Astronautics), Ministry of Education, Nanjing 210016, China [email protected]

Abstract. This paper examines the joint moving target parameter estimation in hybrid active and passive radar networks with sensors placed on moving platforms, which are composed of one dedicated linear frequency modulated (LFM)-based active radar transmitter, multiple frequency modulated (FM)-based illuminators of opportunity, and multichannel radar receivers. Firstly, target returns contributed from the active radar transmitter and multiple illuminators of opportunity are adopted to fulﬁll the radar purpose, resulting in a hybrid active and passive radar networks. Then, the CRLB for joint target parameter estimation is derived as the performance metric for the underlying system. Finally, the numerical results show that, the achievable CRLB can be decreased by exploiting the signals scattered off the target due to illuminators of opportunity transmissions. Keywords: Cramér–Rao lower bound (CRLB) Fisher information matrix (FIM) Hybrid radar network systems Linear frequency modulated (LFM) signals Frequency modulated (FM) signals

1 Introduction During recent years, the distributed radar network system has attracted signiﬁcant attention from researchers due to its obvious advantages against other radar systems [1, 2], which is composed of several widely deployed radar nodes and can simultaneously emit multiple independent waveforms via different transmitting antennas. Research on target parameter estimation is becoming more and more popular [3–7]. It has been demonstrated that the signals scattered off the target due to illuminators of opportunity transmissions can be utilized to enhance the target detection performance and parameter estimation accuracy of the active radar system. In this paper, we consider a hybrid active and passive radar networks with sensors placed on moving platforms, which are composed of one dedicated linear frequency modulation (LFM)-based active

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 56–65, 2020 https://doi.org/10.1007/978-981-13-9409-6_8

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radar transmitter, multiple frequency modulation (FM)-based illuminators of opportunity, and multichannel radar receivers. For the sake of simplicity, the antenna locations are known as prior knowledge. On the other hand, the signals transmitted from the illuminators of opportunity can be decoded and reconstructed at the multichannel radar receivers. Thus, target returns received at the radar receivers due to active radar transmission and illuminators of opportunity transmissions can be employed for joint target parameter estimation, forming a hybrid active and passive radar networks. However, to the best of our knowledge, there are still no published literatures that address the problem of joint moving target parameter estimation performance in hybrid active and passive radar networks. This gap motivates this work. This paper aims to investigate the Cramér–Rao lower bound (CRLB) for joint target estimation in hybrid active and passive radar networks with sensors placed on moving platforms, which are composed of one dedicated LFM-based active radar transmitter, multiple FM-based illuminators of opportunity, and multichannel radar receivers. We compute the joint CRLB for the target estimation of location and velocity in hybrid radar network systems, in which the non-coherent processing mode is considered. Finally, numerical simulations are provided to verify the accuracy of the theoretical derivations. This rest of this paper is organized as follows. Section 2 describes the signal model for hybrid radar networks. In Sect. 3, the joint CRLB is computed for the non-coherent processing scenario by deriving closed-form expressions of the FIM. The numerical simulations are provided in Sect. 4. Finally, conclusion remarks are drawn in Sect. 5.

2 Signal Model Consider a hybrid radar network architecture comprising of one dedicated LFM-based active radar transmitter, Nt FM illuminators of opportunity, and Nr multichannel receivers. Let the active radar transmitter and the ith, i ¼ 1; . . .; Nt FM-based illuminator be located at pt ¼ ½xt ; yt and pti ¼ ½xti ; yti respectively, in a 2-dimensional Cartesian coordinate system for simplicity. Similarly, the jth, j ¼ 1; ; Nr radar receiver is located at prj ¼ xrj ; yrj . The target position and velocity are supposed to be deterministic unknown and denoted by p ¼ ½x; y and v ¼ vx ; vy . We deﬁne the unknown target state vector: y h ¼ x; y; vx ; vy :

2.1

ð1Þ

LFM Signal Model in Active Radar Networks

It is assumed that the dedicated active radar transmitter transmits a sequence of LFM pulses or chirps. The complex envelope of the signal transmitted from the transmitter is pﬃﬃﬃﬃﬃ Pt sðtÞ, where Pt is the transmitted power. The complex envelope of the transmitted unitary power signal is then given by [6]:

58

C. Shi et al. N 1 1 X sðtÞ ¼ pﬃﬃﬃﬃ s1 ðt nTR Þ; N n¼0

ð2Þ

where s1 ð t Þ ¼

2 p1ﬃﬃ ejpkt ; s

0;

jtj 2s ; elsewhere

ð3Þ

N is the number of subpulses for each transmitted burst, TR is the pulse repetition interval (PRI) and s is the duration of each pulse, such that s\TR =2. Moreover, ks2 ¼ Bs is the effective time-bandwidth product of the signal and B is the total frequency derivation. Let stj denote the time delay corresponding to the jth path: stj ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 2 x xrj þ y yrj ðx xt Þ2 þ ðy yt Þ2 þ c

kp pt k þ p prj ; ¼ c ð4Þ

where c is the speed of light. The Doppler shift of the moving target corresponding to the jth path is the time rate of change of the total jth path length:

2 3 xxrj yyrj yyt xxt þ vy kpp k þ pp þ v 7 t k rj k fc 6 x kppt k kpprj k

7; ftj ¼ 6 5 c4 xx yy yyt xxt þ vtx kpp þ vty kpp þ vxj pprj þ vyj pprj tk tk k rj k k rj k

ð5Þ

where fc denotes the carrier frequency of the radar transmitter. 2.2

FM Signal Model in FM-Based Passive Radar Networks

The complex envelope of the signal transmitted from the ith FM illuminator of pﬃﬃﬃﬃﬃ opportunity is Pti si ðtÞ, where Pti is the transmitted power of the ith FM transmitter. The transmitted signal si ðtÞ is the unitary power pulse, that is [3]: si ðt Þ ¼

p1ﬃﬃﬃ ejbi sinð2pfi t þ ui Þ ; Ti

0;

jtj T2t ; elsewhere

ð6Þ

where Ti is the observation time, bi is the modulation index, fi is the instantaneous frequency, and ui is the signal phase [3]. It is worth pointing out that the signals from different illuminators of opportunity can be estimated perfectly at each radar receiver from the direct path reception and separated in some domain. Let sij and fij denote the different bistatic delays and Doppler shifts corresponding to the ij th path associated with the target located at h:

Cramér–Rao Bound Analysis for Joint Estimation

sij ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2 2 ðx xti Þ2 þ ðy yti Þ2 þ x xrj þ y yrj c

2 xxrj yyti xxti þ v þ v x kpp k y kpp k pprj k ti ti fci 6 k

fij ¼ 6 c4 xxrj yyrj þ vxj pp þ vyj pp k rj k k rj k

59

kp pti k þ p prj ; ¼ c ð7Þ

3 yy þ pprj k rj k 7 7; ð8Þ 5

where fci represents the carrier frequency of the ith FM transmitter.

3 Joint Cramér–Rao Lower Bound 3.1

Non-coherent FIM for LFM-Based Active Radar Networks

Under the non-coherent processing mode, the target is assumed to be made up of several individual isotropic scatterers [3, 5]. The attenuation coefﬁcient corresponding to the jth path is modeled as a zero-mean complex Gaussian random variable ftj CN ð0; r2 Þ, which is constant over the observation interval [5] and varies with the angle of view. The received signal at the jth receiver due to the signal transmitted from the dedicated radar transmitter is given by: rtj ðtÞ ¼

pﬃﬃﬃﬃﬃ Pt atj ftj s t stj ej2pftj t þ ntj ðtÞ;

ð9Þ

where ntj ðtÞ is the additive noise corresponding to the jth path, which is a temporally white, zero-mean complex Gaussian random process with variance r2n . The term atj ¼ 1 kppt kkpprj k

represents the variation in the signal strength due to path loss effects.

Note that the signals from the active radar transmitter are supposed to be received and processed at the multichannel radar receivers. The joint log-likelihood ratio across all the transmitter-receiver pairs is: Lðh; rðtÞÞ ¼

Nr X j¼1

Z 1

2

r2 a2 Pt þ r2n tj

rtj ðtÞs t stj ej2pftj t dt

þ C; 1 r2n r2 a2tj Pt þ r2n

ð10Þ

where rðtÞ ¼ ½rt1 ðtÞ; rt2 ðtÞ; . . .; rtNr ðtÞy denotes the received signals from the entire set of the receivers, and C is a constant that is not dependent on h. The derivations for non-coherent MIMO radar in [6] express the MIMO FIM as a combination of the constituent bistatic FIMs. After lengthy algebraic manipulations, we can write the non-coherent FIM for active radar networks as follows:

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2 Nr 8p2 r2 a2tj Pt X JA JA non ðhÞ ¼ ij ðhÞ; 2 2 2 2 j¼1 rn r atj Pt þ rn

ð11Þ

where the elements of the bistatic FIM JA ij ðhÞ for the non-coherent processing mode corresponding to the jth transmitter-receiver pair are identical to the results in [6]. 3.2

Non-coherent FIM for FM-Based Passive Radar Networks

Since it is assumed that the different transmitted FM signals can be separated at the radar receivers, the received signal at the jth receiver due to the signal transmitted from the ith FM illuminator of opportunity can be expressed in a similar way: rij ðtÞ ¼

pﬃﬃﬃﬃﬃ Pti aij fij si t sij ej2pfij t þ nij ðtÞ;

ð12Þ

where nij ðtÞ represents the additive clutter-plus-noise corresponding to the ijth path, which is a temporally white, zero-mean complex Gaussian random process with 0 variance rn2 . The term aij ¼ kpp k1 pp denotes the ijth path loss. Further, the target ti k rj k 0 attenuations fij are zero-mean Gaussian distributed with variance r 2 . It should be noted that rij ðtÞ are mutually independent for different transmitterreceiver pairs, which is due to the fact that the illuminators of opportunity and radar receivers are widely separated. The joint log-likelihood ratio across all the receivers for a given transmitted waveform can be written as:

Nt X Nr X L h; r ðtÞ ¼ 0

i¼1 j¼1

0

r 2 a2ij Pti

r0n2 r0 2 a2ij Pti þ r0n2

Z

1 1

rij ðtÞsi

t sij e

j2pfij t

2

0 dt

þ C : ð13Þ

0 where r ðtÞ ¼ ½r11 ðtÞ; r12 ðtÞ; ; rNt Nr ðtÞy denotes the received signals from the entire 0 set of the receivers, and C is a constant that is not dependent on h. Similarly, the non-coherent FIM with respect to h in FM-based passive radar networks can be calculated as follows:

0 2 Nt X Nr 8p2 r 2 a2ij Pti X JPij ðhÞ; JPnon ðhÞ ¼ 02 02 2 02 i¼1 j¼1 rn r aij Pti þ rn

ð14Þ

where the elements of the bistatic FIM JPij ðhÞ for the non-coherent processing mode corresponding to the ijth transmitter-receiver pair are identical to the results in [3].

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Non-coherent CRLB for Hybrid Radar Networks

Now, we will compute the CRLB for the non-coherent processing scenario in hybrid radar networks by deriving the hybrid FIM expression. Using the derivations above, the FIM for hybrid LFM-based active and FM-based passive radar networks obtained from (11) to (14) is given by: P Jnon ðhÞ ¼ JA non ðhÞ þ Jnon ðhÞ 2 0 2 Nt X Nr Nr 8p2 r2 a2tj Pt 8p2 r 2 a2ij Pti X X JA JPij ðhÞ: ¼ ij ðhÞ þ 02 02 2 02 2 2 2 2 j¼1 rn r atj Pt þ rn i¼1 j¼1 rn r aij Pti þ rn

ð15Þ

Furthermore, the non-coherent CRLB matrix for joint estimation of target locations and velocities is derived as: CRLBnon ðhÞ ¼ J1 non ðhÞ:

ð16Þ

The CRLBs for the estimates of the unknown target locations and velocities are determined by the four diagonal elements of the CRLB matrix: 1 ) y CRLBxnon ðhÞ ¼ J1 non ðhÞ 1;1 ; CRLBnon ðhÞ ¼ Jnon ðhÞ 2;2 : 1 vy x CRLBvnon ðhÞ ¼ J1 non ðhÞ 3;3 ; CRLBnon ðhÞ ¼ Jnon ðhÞ 4;4

ð17Þ

Remark 1 We can clearly notice from the expressions of the entries of the hybrid FIM in (15) that the target parameter estimation performance can be remarkably enhanced with the information obtained from FM-based passive radar networks, which is because that the use of passive radar networks results in increase of the signal-to-noise ratio (SNR) values, implying the decrease of the obtained CRLB. These expressions for CRLB can be utilized as an important performance metric to optimize the hybrid radar networks for a predetermined accuracy requirement with the minimum system cost.

4 Simulation Results and Analysis In the sequel, numerical simulation results are dedicated to compute the joint CRLB for hybrid active and passive radar network systems as well as verify the accuracy of the theoretical derivations. Here, we consider a hybrid radar network with one dedicated active radar transmitter, four FM-based illuminators of opportunity, and four multichannel radar receivers, as depicted in Fig. 1. We set the signal parameters as follows: N ¼ 256, B ¼ 50 MHz, TR ¼ 104 s, fc ¼ 10 GHz, s ¼ 106 s, r2 ¼ 1, r2n ¼ 1013 W, c ¼ 3 108 m=s, Ti ¼ 0:1 s, bi ¼ 10, ui ¼ p=2, fi ¼ 25 KHz, fci ¼ 100 MHz, 0 0 r 2 ¼ 1, rn2 ¼ 1013 W, Pti ¼ 10 KW.

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Deﬁne the SNR as: Nr X

SNR ¼ 10l g

j¼1

, ! r2 a2tj Pt

r2n :

ð18Þ

Figure 2 illustrates the square root of CRLB (RCRLB) in the x-position and yposition dimensions versus SNR for the non-coherent processing mode. Similarly, Fig. 3 shows the velocity RCRLB as a function of the SNR. The curves show that the RCRLB decreases with the increase of SNR. The RCRLB is lower in the y-dimension for both the position and velocity. Moreover, it should be pointed out that the RCRLB of hybrid radar networks is much lower than that of active radar networks, which shows that the target parameter estimation performance can be signiﬁcantly improved with the use of information obtained from FM-based passive radar networks, although the resolution of the FM-based passive radar networks is much worse when compared with the resolution of active radar networks. This is due to the fact that the use of passive radar networks leads to increase of the SNR values, which leads to the decrease of the RCRLB.

7000

5000

FM Transmitters Radar Transmitter Radar Receiver Target

4000

(80, 20)m/s

Y position[m]

6000

(30, 50)m/s

3000

(10, 70)m/s

2000 1000

(50, 50)m/s

(30, 50)m/s (60, 50)m/s

0 -1000 -1000

0

1000

2000

3000

4000

5000

6000

7000

X position[m]

Fig. 1. Target and the hybrid radar network conﬁguration used in the numerical simulations.

Cramér–Rao Bound Analysis for Joint Estimation 2

px (Hybrid radar network) py (Hybrid radar network) px (Active radar network) py (Active radar network)

1.8

RCRLB[m]

1.6 1.4 1.2 1 0.8 0.6 0.4 -1 10

0

10

SNR[dB]

Fig. 2. Non-coherent RCRLB in the target position dimensions versus SNR.

2.2 2 1.8

RCRLB[m/s]

1.6 1.4 1.2 1

vx (Hybrid radar network) vy (Hybrid radar network) vx (Active radar network) vy (Active radar network)

0.8 0.6 0.4 0.2 -1 10

0

10

SNR[dB]

Fig. 3. Non-coherent RCRLB in the target velocity dimensions versus SNR.

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px (Hybrid radar network) py (Hybrid radar network) px (Active radar network) py (Active radar network)

2.5

RCRLB[m]

2

1.5

1

0.5

0 -1 10

0

10

SNR[dB]

Fig. 4. Non-coherent RCRLB in the target position dimensions versus SNR when ½px ; py ¼ ½8500; 4000 m:

Furthermore, to investigate the dependence of the non-coherent CRLB on the geometry, we change the target position as illustrated in Fig. 4 for which we are computing the hybrid RCRLB to [8500, 4000] m. It is apparent that the RCRLBs are different from the earlier case, which is because that the geometry between the target and the hybrid radar network systems impacts the derivatives of the delay-Doppler terms with respect to the Cartesian coordinates signiﬁcantly [3, 6]. For brevity, the noncoherent RCRLB curves in the target velocity dimensions versus SNR when ½px ; py ¼ ½8500; 4000 m are omitted here.

5 Conclusion In this paper, new signal model has been built for the hybrid active and passive radar networks, which accounts for the target reflected signals contributed from both the active radar and illuminators of opportunity transmissions. The moving target parameter estimation and its CRLB have been discussed and derived subsequently. Finally, simulation results were provided to illustrate that the joint target parameter estimation accuracy can obviously be improved by employing the signals obtained from the passive radar networks. In future work, we will concentrate on the problem of optimization of the hybrid radar networks for a predetermined accuracy requirement with the minimum system cost. Acknowledgements. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61801212), in part by the Natural Science Foundation of Jiangsu

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Province (Grant No. BK20180423), in part by China Postdoctoral Science Foundation (Grant No. 2019M650113), in part by the Fundamental Research Funds for the Central Universities (Grant No. NT2019010), in part by the National Aerospace Science Foundation of China (Grant No. 20172752019, No. 2017ZC52036), in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA), in part by the Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, and in part by the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Industry and Information Technology, Nanjing 210016, China.

References 1. Li J, Stoica P (2009) MIMO radar signal processing. Wiley, Hoboken, NJ, pp 1–20 2. Hack DE, Patton LK, Himed B et al (2014) Detection in passive MIMO radar networks. IEEE Trans Signal Process 62:2999–3012 3. Shi CG, Wang F, Zhou JJ (2016) Cramér-Rao bound analysis for joint target location and velocity estimation in frequency modulation based passive radar networks. IET Signal Process 10:780–790 4. Gogineni S, Rangaswamy M, Rigling BD et al (2014) Cramér-Rao bounds for UMTS-based passive multistatic radar. IEEE Trans Signal Process 62:95–106 5. He Q, Blum RS, Haimovich AM (2010) Noncoherent MIMO radar for location and velocity estimation: More antennas means better performance. IEEE Trans Signal Process 58:3661– 3680 6. Shi CG, Salous S, Wang F et al (2016) Cramér-Rao lower bound evaluation for linear frequency modulation based active radar networks operating in a rice fading environment. Sensors 16:1–17 7. Shi CG, Wang F, Sellathurai M et al (2016) Transmitter subset selection in FM-based passive radar networks for joint target parameter estimation. IEEE Sens J 16:6043–6052

A Hinged Fiber Grating Sensor for Hull Roll and Pitch Motion Measurement Wei Wang(&), Libo Qiao, Yuliang Li, Jingping Yang, and Chuanqi Liu Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, 300387 Tianjin, China [email protected]

Abstract. This paper introduces a novel ﬁber Bragg grating (FBG) sensor for hull roll and pitch motion measurement. The sensor is mainly composed of three parts: differential hinge structure, ﬁber grating and mass block. When the hull produces a dip angle affected by external forces, the ﬁber gratings ﬁxed on the left and right sides are deformated due to the tensile and the pressure force. The relationship between the deformation of the ﬁber gating and its wavelength is subjected to the proportional function. By using compensation algorithm of the demodulator, we can get the ﬁber wavelength and the inclination angle of the ship. Keywords: Fiber Bragg gratings Dynamic monitoring angle hinge structure Mechanical temperature compensation

Differential

1 Introduction Among all the transportation methods, ship transportation has the advantages of large load and low cost [1]. However, due to the influence of wind, waves and current, the hull on the sea often undergoes periodic rolling, pitching and swaying [2]. As a result of these movements, a series of negative consequences would be produced, such as ship stalling at the same power, serious damage to hull structure and crew seasickness [3]. Therefore, if these movements can be found in time, it can inevitably prolong the ship service life and reduce the discomfort ableness of crews caused by the hull fluctuations. Fiber Bragg grating is an important part of the ﬁber sensor. It has excellent antielectromagnetic interference capability and electrical insulation. Multiple gratings of different wavelengths can also be connected in series to the same ﬁber [4]. It has the characteristics of corrosion resistance, physical stability and small volume but plasticity. Therefore, ﬁber optic sensors are suitable for marine vessels. Nowadays, FBG sensors have been extensively used to measure common parameters such as temperature, stress and strain, vibration, displacement and acceleration. It has also made much progress in measuring angles. Ferdinand [5] used the optic ﬁber and pendulum structure to measure the angle of dip. However, the ﬁber grating is in a dangling state for a long time. At the same time, it is not well protected by the package and easy to be brittle. Xie [6] measured the angular change by utilizing the change of © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 66–74, 2020 https://doi.org/10.1007/978-981-13-9409-6_9

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buoyancy in the liquid. While after long-term use, the metal in the liquid is easily corroded, and the liquid is dangerous to some extent, which brings difﬁculties to the experimental process.

2 Theory 2.1

FBG Basic Sensing Principle

The sensing process of the FBG sensor is to change the wavelength of the FBG by changing the physical quantity of the object (such as temperature, displacement, pressure, etc.). Through the demodulator is modulated and demodulated, the change in physical quantity is measured. The principle is shown in Fig. 1 [7].

Fig. 1. Sensing principle of FBG

According to the ﬁber coupling mode, when the ﬁber Bragg grating is affected by external factors, the center wavelength of the ﬁber grating will drift. The variation of ﬁber wavelength can be expressed by kB ¼ 2neff K

ð1Þ

where: kB is the FBG wavelength, neff is the effective refractive index of the core of the ﬁber, Ʌ is the period of the FBG. Since temperature and strain can have a direct effect on neff and Ʌ. DkB ¼ 2Dneff K þ 2neff DK

2.2

ð2Þ

Temperature Compensation Method of FBG

The effect of temperature on hull roll and pitch motion measurements is eliminated using mechanical compensation. Two identical ﬁber gratings are pasted on the two opposite sides respectively where strain changes occur. Two ﬁber gratings are

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subjected to tensile strain and compressive strain respectively. The central wavelengths of two ﬁber gratings are k1 and k2, and the wavelength changes are expressed as follows [8]: Dk1 ¼ ae De1 þ aT DT1

ð3Þ

Dk2 ¼ ae De2 þ aT DT2

ð4Þ

where ae is the sensitivity coefﬁcient of the ﬁber grating with respect to strain, and aT is the temperature sensitivity coefﬁcient of the ﬁber grating. When the hull is angled by rolling or pitching, it will inevitably lead to strain change of the ﬁber grating. One of the ﬁber gratings is subjected to tensile strain while the other is subjected to compressive strain. And the values of the two strains are equal in magnitude and opposite in direction. it can be inferred that: DT1 ¼ DT2

ð5Þ

Dk1 Dk2 ¼ 2ae De1

ð6Þ

Therefore, once the sensor is packaged, the wavelength change in the center of the ﬁber grating will be unaffected by temperature. It is only related to the strain wavelength shift caused by the hull roll and pitch. Thereby, a temperature self-compensating ﬁber grating sensor is achieved. 2.3

Arc Hinge Flexibility Theory

The flexible hinge is the most important part of the design structure, and Fig. 2 is a schematic diagram of the elliptical flexible hinge. This section mathematically models the flexible arc hinge and gives a method to solve its flexibility [9].

Fig. 2. Arc flexible hinge schematic

In solving the hinge flexibility, the deformation of the flexible hinge is decomposed into the accumulation of bending deformations of a number of micro-element segments, where each segment is treated as an equal-section rectangular beam of length

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dx. R, a, w, t0 and hm are the notch radius, half notch length, hinge width, minimum thickness and maximum central angle of the hinge respectively. According to the geometric relationship, it can be known that: tðxÞ ¼ 2R þ t0 2

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ R2 ðx aÞ2

hm ¼ arcsin

a R

ð7Þ ð8Þ

According to the basic formula of material mechanics, the angular deformation az which is generated by micro-element around z-axis under the action of the moment Mz is computed as: Z az ¼ 0

1

Mz dx ¼ EIz ðxÞ

Z

1

0

Mz dx Ewt3 ðxÞ

ð9Þ

where E is the elastic modulus of the selected material. and Iz (x) is the moment of inertia of the micro-element cross section to the z-axis. It should be noticed that dx ¼ R cos h dh, tðhÞ ¼ 2R þ t0 2R cos h. The expression of flexibility under the action of the moment Mz is: az 12R N1 ¼ Mz Ew Z N1 ¼

hm

cosh dh 3 hm t ðhÞ

ð10Þ ð11Þ

3 Sensor Structure 3.1

Structure Description

The sensor structure is illustrated in Fig. 3. The sensor is composed of three parts: hinge structure, FBG and mass block. The hinge structure is a differential symmetric hinge structure, which can greatly enhance the sensitivity and improve the measurement accuracy of the sensor. The hinge structure and the mass are integrally formed to avoid the loss and maintenance problems caused by long-term use. The main body uses beryllium bronze material, which has the advantages of high strength, high elastic limit, corrosion resistance and fatigue resistance. Hence, it is suitable for hull sensors. Combining with the hinge structure and the mass block, FBG sensitive component can dynamically measure the angular change caused by the ship motion. Influenced by the gravity, the force of the mass block exerts on the hinge structure changes when the tilting angle of the hull is produced. The ﬁber gratings ﬁxed on the left and right sides are deformed by the tensile force and the pressure, respectively, resulting in the change of the center wavelength of the ﬁber gratings. When the tilt

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angle changes continuously, it basically exhibits a linear function relationship with the change of the center wavelength of the ﬁber grating. By means of the compensation algorithm of the demodulation instrument, the relationship between the center wavelength of the ﬁber and the change of the tilt angle can be ultimately obtained.

Fig. 3. Sensor structure

3.2

Ansys Analysis

The structural model of the FBG sensor designed in this paper can be directly modeled and analyzed in Ansys Workbench15.0 software. After applying the ﬁxed and meshing, the direction of the gravity of the sensor is sequentially changed to indicate the change of the tilt angle during the hull movement. Through the static analysis of the sensor displacement change in x-axis, it can be clearly seen that the measurement range of the sensor is from −90° to 90°. Along with the increasing of the tilt angle, the displacement of the stress point in the x-axis direction increases. The strain at the point of force can be obtained by the strain formula of the sensor. Figure 4 shows the static analysis of Ansys software at different angles. This subsection analyzes the structure of the sensor from two aspects, i.e., modal analysis and harmonious response analysis. Modal analysis is primarily used to determine the resonant frequency and mode shape of the sensor structure. The resonance phenomenon of the designed structure can be avoided by analyzing the frequency of the ﬁrst resonance. The purpose of the harmonic response analysis is to calculate the response at several frequencies and to further observe the stress which is corresponding to the peak frequency. According to the above-mentioned dynamic analysis, the natural frequency of the FBG angle sensor is 105.07 Hz (Fig. 5). Moreover, the difference between the ﬁrst order modal frequencies and the second to ﬁfth order modal frequencies is rather large, which indicates that the cross-coupling of the structure is very small. Figure 6 is a harmonic response curve of the sensor from 0 to 150 Hz. It can be seen from the

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71

(b) θ = 90°

(c) θ = 0° Fig. 4. Ansys static analysis

diagram that there is an obvious resonance phenomenon at the 105 Hz. In reality, the frequency of the wind wave is between 0.01 and 3 Hz. Thus, the sensor can meet the practical requirements of measurement accuracy.

(a) First-order mode shape

(b) Frequency diagram under different modes

Fig. 5. Modal analysis

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Fig. 6. Harmonic response curve

4 Sensor Test Experiment During the experiment, the ﬁber grating angle sensor is placed on the standard angle block. The measurement range of the FBG sensor, i.e., −90° to 90° can be achieved by placing different angle blocks. The demodulation software can dynamically monitor the central wavelength of the ﬁber grating by using the compensation algorithm. The data of the center wavelength is recorded at every 5° from −90° to 90° at room temperature. When the wavelength data are stabilized, the wavelength values of each angle are recorded by the demodulator software. After collecting 2000 times per second in, a total of 60 s of data, the average value of the data is calculated. Meanwhile the curve between the sine value of the angle and the center wavelength of the ﬁber grating is plotted, as shown in Fig. 7. It can be seen from the ﬁgure, the measuring range of the sensor can reach −90° to 90°. By further calculating, the linear ﬁtting between the angle sine value and the left ﬁber grating center wavelength value curve can achieve 99.64%, and the right curve linear ﬁtting degree is up to 99.61%. After the differential between the left and right ﬁber gratings, the linear ﬁtting degree of the curve is obviously improved, and the calculated value can reach 99.89%. The slope of the ﬁtted line, K ¼ Dk=Dh, is also the sensitivity of the FBG angle sensor which is an important index to measure the performance of the sensor. According to the slope of the ﬁtting line, the sensitivity of the left ﬁber is 14.26 pm/1°,

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Fig. 7. The linearity curve of sensor

the sensitivity of the right ﬁber is 13.77 pm/1°, and the sensitivity of the ﬁber after the differential is 28.03 pm/1°. It is shown that the design of the differential structure improves the sensitivity to a great extent, which proves the validity and feasibility of this structure.

5 Conclusions The FBG sensor designed in this paper can dynamically monitor the change of the sloping angle of the hull. The measurement range can reach −90° to 90°, and sensitivity coefﬁcient is superior. The outer sealing shell of the sensor is processed by an entire aluminum alloy, which improves the water-tightness and anti-destructive function of the system. The side of the sensor uses a ﬁber optic waterproof plug connector to improve the anticorrosion capability of the sensor. Once the sensor is packaged, the change of the center wavelength of the ﬁber grating will not be affected by temperature, which will provide a stable and reliable device for long-term detection and health monitoring of the hull structure in the ocean-going ship ﬁeld.

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Acknowledgements. This paper is supported by Natural Youth Science Foundation of China (61501326, 61401310). It also supported by Tianjin Research Program of Application Foundation and Advanced Technology (16JCYBJC16500).

References 1. lianhui JIA (2011) Research and development of the real-time monitoring system of ship’s motions and stresses. Harbin Engineering University 2. Li X (2013) Research on marine parallel stabilized platform. Jiangsu University of Science and Technology 3. Liu N (2018) Research on the vertical motions’ control system of a fast multi-body ship based on longitude damping devices. Harbin Engineering University 4. Moody V (2004) Fiber theory and formation. Elsevier Inc., 2004-06-15 5. Ferdinand P, Rougeault S (2000) Optical ﬁber Bragg grating inclinometry for smart civil engineering and public works. Proc SPIE Int Soc Opt Eng 4185:13–16 6. Xie T, Wang X, Li C, Tian S, Zhao Z, Li Y (2017) Fiber bragg grating differential tilt sensor based on mercury column piston structure. Acta Optica Sinica 37(03):170–176 7. Lee B (2003) Review of the present status of ﬁber sensors. Opt Fiber Technol: 3–4 8. Zhang Y (2016) Research of distributed inclination technology based on ﬁber Bragg grating and it’s application. Wuhan University 9. Li Y, Wu H, Yang X, Kang S, Cheng S (2018) Optimization design of circular flexure hinges. Opt Precis Eng 26(06):1370–1379

Natural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER Yunxue Shao(&) and Hongyu Suo College of Computer Science, Inner Mongolia University, Inner Mongolia, People’s Republic of China [email protected], [email protected]

Abstract. Maximum Stable Extreme Region (MSER) is the most influential algorithm in text detection. However, due to the complex and varied background of Mongolian text in natural scene images, it is difﬁcult to distinguish between text and non-text connected regions, thus reducing the robustness of the MSER algorithm. Therefore, this paper proposes to extract the connected regions in the natural scene pictures by applying MSER, and then uses the convolutional neural network (CNN) to train a high-performance text classiﬁer to classify the extracted connected regions, and ﬁnally obtaining the ﬁnal detection results. This paper evaluates the proposed method on the CSIMU-MTR dataset established by the School of Computer Science, Inner Mongolia University. The recall rate is 0.75, the accuracy rate is 0.83, and the F-score is 0.79, which is signiﬁcantly higher than the previous method. It shows the effectiveness of the proposed Mongolian text detection method for natural scenes. Keywords: Natural scene mongolian text detection Maximum stable extreme region (MSER) Convolutional neural network (CNN)

1 Introduction As the most direct representation of human high-level semantic information, text, especially in natural scene pictures, plays an indispensable role in image understanding, and has a wide range of practical applications with broad application prospects. In recent years, although plenty of Latin, Arabic or Chinese texts have been extracted from complex natural scene images, in the printing of Mongolian document images [1, 2], it is still in its infancy using complex the detection of Mongolian text in natural scene images. The Mongolian detection in the natural scene can play a key role in promoting the information management and control of the Mongolian network information platform and improving the quality of the content. It has broad application prospects in the development of information technology in Inner Mongolia. Natural scene text detection methods can be divided into two categories: sliding window based methods and connected body based methods [3]. The sliding window based method [4] uses a multi-scale sliding window search to traverse all image regions, and then distinguishes between textual and non-textual information through a

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trained classiﬁer, typically using a manually designed Low-level features [5, 6] to train classiﬁers, extraction from a sliding window. Such as local binary mode (LBP), scaleinvariant feature transform descriptor (SIFT), and gradient direction histogram (HOG) [7]. Although the sliding window based method is simple and intuitive, in order to obtain effective detection results, a large number of sliding windows are usually required, and the image regions in each window need to be classiﬁed, resulting in high computational complexity and slow speed. The natural scene text detection method based on the connected body [8–10] ﬁrst generates a large number of connected body regions by the similarity between the interpixel attributes (color, texture, stroke width, etc.) in the aggregated image, and then extracts from the connected body region. Dividing into text areas and non-text areas, and ﬁnally getting the test results. Stroke width transform (SWT) [11] and maximum stable extremum region (MSER) [12, 13] are the two most widely used extraction methods in the process of extracting connected candidate regions. At present, the MSER-based method has the high ability to detect most text components in an image. The method is robust to viewing angle, character size, illumination variation, and has a fast and stable feature. Therefore, this paper proposes a natural scene Mongolian text detection method based on MSER, which uses deep convolutional neural network to learn the deep features of candidate connected regions, at the same time, the better selection among the combination between CNN classiﬁer and non-maximum value suppression (NMS) [14], which improves text detection performance. The structure of this paper is organized as follows: In Sect. 2, we will discuss all the details of the proposed method. In Sect. 3, the experimental results are discussed. In the last section, the conclusion is given.

2 Related Work

Input image

pre-processing

MSER image

Detection results

CNN classification

Region filtering

Fig. 1. The flowchart of MSER-based Mongolian text detection method

The method flow in this paper is shown in Fig. 1. The proposed detection method consists of three main steps. First, a candidate connected region is generated by applying an MSER detection method on the input image; then the candidate connected region is divided into a text region and a non-text region by the trained CNN classiﬁer; ﬁnally, the text region is displayed on the input image. The details of the method are as follows.

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Generating Candidate Connected Areas

MSER is the most influential algorithm in text detection. Its principle is similar to a diffuse water method, which is usually a grayscale image. For grayscale images, the grayscale value of the pixel ranges from (0, 1, …, 255), the image is binarized with a certain threshold, and the grayscale value is less than the threshold in the binarization operation. The pixel will be set to black, while pixels with gray values greater than or equal to the threshold will be set to white. When the threshold gradually increases from 0 to 255, the generated binary image will change from all white to all black. At some point in the change, black dots or black areas will appear in the image. Which is regarded as the local minimum area of grayscale, when the threshold increases, these areas will become larger, and ﬁnally the entire image is a black area. In the process of change, some special local minimum value regions change in the large interval of a certain gray level without substantially following the threshold change. The local minimum value region that achieves this requirement is the maximum stable minimum value. region. Accordingly, when the threshold is gradually decreased from 255 to 0, a series of maximum stable maximum regions are obtained. Typically, the result of the MSER is the union of the region of maximum stable minimum and maximum stable maximum.

Fig. 2. MSER test results

For the Mongolian text detection system, our goal is to detect as many textual connectivity areas as possible in the process of generating candidate connected areas, as it is difﬁcult to recover lost textual connectivity areas in subsequent processes. Therefore, the threshold of the MSER is set to 2, which makes it possible to detect all the text-connected areas. As shown in the Fig. 2, although most of them are non-text connected areas, the real text connected areas are also correctly detected. At the same time, this method requires a powerful classiﬁer to distinguish between a large number of non-text connected areas and text connected areas. The following describes a high performance classiﬁer based on convolutional neural networks. 2.2

Training Text Classiﬁer

In recent years, deep learning has accomplished many challenging tasks in the ﬁeld of computer vision and made breakthroughs. Convolutional neural network (CNN) is a kind of feedforward neural network with convolutional computation and deep structure. It is one of the representative algorithms of deep learning. Traditional CNN networks

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have achieved great success in digital and handwritten character recognition [15, 16]. Natural scene text detection is an advanced visual task that is difﬁcult to solve with a set of low-level operations or manually designed features. Compared with the previous method of using heuristic features to classify text connected regions and non-text connected regions, this paper uses convolutional neural network to train text classiﬁers [17] (Fig. 3) to robustly classify generated candidate connected regions.

Fig. 3. CNN text classiﬁer

The structure of the CNN text classiﬁer is similar to that in the Deep Residual Network (ResNets) [18]. ResNets consist of many “residual units”. Each unit can be expressed as: yl ¼ hðxl Þ þ F ðxl ; wl Þ

ð1Þ

xl þ 1 ¼ f ð y l Þ

ð2Þ

where xl and xl þ 1 are the input and output of the Lth unit, wl is a set of weights (and biases) associated with the Lth unit, and F represents a residual function. hðxl Þ ¼ xl represents an identity map and f represents a ReLU activation function. If both hðxÞ and f ðyÞ are identity maps, i.e. hðxl Þ ¼ xl ; f ðyl Þ ¼ yl then in the forward and reverse propagation phases of the training, the signal can be passed directly from one unit to Another unit makes training easier. That is, the above formula can be expressed as: xl þ 1 ¼ xl þ F ðxl ; wl Þ

ð3Þ

By recursion, you can get the expression of any deep cell L feature: xL ¼ x l þ

L1 X

F ðxi ; wi Þ

ð4Þ

i¼1

The advantages of this expression are: (1) The feature xL for any deep cell L can be P expressed as the feature xL of the shallow cell L plus a residual of the form L1 i¼l F function, which indicates that there is a residual characteristic between any of the units

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P L and l. (2) For any deep unit L, its characteristic xL ¼ x0 þ L1 i¼0 F ðxi ; wi Þ is the sum of all previous residual function outputs. A trained CNN text classiﬁer is used to give a predicted value for each of the generated candidate connected regions to determine whether it is a text connected region. In our experiments, when generating candidate connected regions, many connected regions and other connected regions exist phenomenons of including and mostly intersect or mostly intersect. Therefore, we use NMS to select the highest scores in those connected regions and suppress those with low scores. Since the threshold of the MSER is set to 2, the generated candidate connected areas have different sizes and different shapes, with the screening process of the candidate connected areas is as shown in Fig. 4. The size of the candidate area with moderate area is adjusted to 32 * 32. Once inputted to the CNN text classiﬁer, the CNN text classiﬁer assigns a higher score to the text connected area, and assigns a lower score to the non-text connected area. The CNN text classiﬁer exhibits strong robustness and high discriminating power in distinguishing between text and non-text connected regions.

Fig. 4. Candidate connected area screening process. a Candidate connected areas having an area larger than 1300, b candidate connected areas having an area of less than 150, c candidate connected areas of a moderate area

3 Experimental Results and Analysis We evaluated the proposed method at the CSIMU-MTR dataset [19] established by the School of Computer Science, Inner Mongolia University. 3.1

Data Sets and Evaluation Criteria

The CSIMU-MTR data set includes 560 color images, of which 460 images are used for the training set and 100 images are used for the test set. Since this paper needs to train the CNN text classiﬁer, only 5679 character region positive samples and 8900 non-character region negative samples are extracted from the CSIMU-MTR training set. In that the training data is too small, the model will be over-ﬁtting, therefore, we use 244,350 background Mongolian character regions and 82,706 natural scene images with no characters to synthesize positive samples of Mongolian text in 100,000 natural scenes, and use 2716 natural scene images without Mongolian characters to generate

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190,000 Negative samples of non-character regions, which are adjusted are adjusted to 32 * 32 size, as shown in Fig. 5, it is used to train the CNN text classiﬁer.

Fig. 5. Training sample. a Positive sample, b negative sample

This paper adopts the database competition evaluation criteria proposed by Wolf [20] and others to evaluate the proposed method. The accuracy index of the evaluation index P and the recall index R and F are respectively expressed as: PN PjDi j j

i

Precision ¼

MD Dij ; Gi

PN i

PN PjDi j Recall ¼

j

i

MG Gij ; Di

PN i

F-score ¼ 2

ð5Þ

jDi j

ð6Þ

jGi j

Precision * Recall Precision þ Recall

ð7Þ

The accuracy rate refers to the ratio of the correctly detected text connected area to the total number of all connected areas. The recall rate refers to the ratio of the correctly detected text connected area to the real text connected area, and the comprehensive index is the average of the reconciliations between accuracy rate and recall rate. 3.2

Experimental Results and Analysis

As shown in Table 1, the experimental comparison of the Mongolian text detection method and other methods proposed in this paper on the CSIMU-MTR dataset shows that the proposed method achieves the best on the CSIMU-MTR dataset.

Table 1. Comparison of test results of different methods Method Our method Edge + SVM MSER + SVM

Recall 0.75 0.61 0.64

Precision 0.83 0.72 0.74

F-score 0.79 0.66 0.68

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The accuracy, recall, and F-score are improved by about 0.11, 0.09, and 0.11, respectively, compared to the best method, indicating that the proposed method can correctly detect more real text. Our approach beneﬁts from two aspects: on the one hand, MSER can extract most real text candidate regions; on the other hand, CNN-based text classiﬁer can robustly identify textual connectivity regions from a large number of candidate connected regions, improving classiﬁcation accuracy. Figure 6 shows the effect of the method in this paper. The characters in the ﬁgure are different in size, and the background is complex and changeable, with the ideal detection results the detection results are ideal. It shows that the proposed method is robust to Mongolian text detection in different natural scenes.

Fig. 6. Example of successful detection method in this paper

Although the method in this paper can successfully detect Mongolian text in most cases, in some cases the text cannot be successfully detected. When the text has some phenomenons, such as, uneven color, low resolution, uneven illumination, too low contrast, and the over exposure, the MSER is difﬁcult to detect the text area, as shown in Fig. 7.

Fig. 7. Example of failure of detection method in this article

4 Conclusion Aiming at the complex and varied images in natural scenes, this paper proposes a MSER-based natural scene Mongolian text detection method. Compared with the traditional method to classify text and non-text connected regions, this paper uses the high performance and high capacity of the deep learning model to improve the ﬁnal text and non-text classiﬁcation accuracy. The results on the standard dataset show that the natural scene Mongolian text detection method proposed in this paper has strong robustness and improves the ﬁnal accuracy, recall rate and F-score.

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Acknowledgements. This study was supported by the National Natural Science Foundation of China (NSFC) under Grant no. 61563039.

References 1. Gao G, Su X, Wei H et al (2011) Classical mongolian words recognition in historical document. In: International conference document analysis recognition, IEEE, pp 692–697 2. Wei H, Gao G (2014) A keyword retrieval system for historical mongolian document images. Int J Doc Anal Recognit (IJDAR) 17(1):33–45 3. Ye Q, Doermann D (2015) Text detection and recognition in imagery: a survey. IEEE Trans Pattern Anal Mach Intell 37(7):1480–1500 4. Jaderberg M, Vedaldi A, Zisserman A (2014) Deep features for text spotting. In: Computer vision—ECCV, pp 512–528 5. Chen X, Yuille AL (2004) Detecting and reading text in natural scenes. In: IEEE computer society conference on computer vision and pattern recognition, pp 366–373 6. Babenko B, Belongie S (2011) End-to-end scene text recognition. In: IEEE international conference on computer vision, pp 1457–1464 7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vision Pattern Recognit 1:886–893 8. Chen H, Tsai SS, Schroth G et al (2011) Robust text detection in natural images with edgeenhanced maximally stable extremal regions. In: 18th IEEE international conference on image processing, pp 2609–2612 9. Yin XC, Yin X, Huang K et al (2014) Robust text detection in natural scene images. IEEE Trans Pattern Anal Mach Intell 36(5):970–983 10. He T, Huang W, Qiao Y et al (2015) Text-attentional convolutional neural networks for scene text detection. IEEE Trans Image Process 25(6):2529–2541 11. Epshtein B, Ofek E, Wexler Y (2010) Detecting text in natural scenes with stroke width transform. In: IEEE computer society conference on computer vision and pattern recognition, pp 2963–2970 12. Nistér D, Stewénius H (2008) Linear time maximally stable extremal regions. In: European conference on computer vision-ECCV, pp 183–196 13. Huang W, Qiao Y, Tang X (2014) Robust scene text detection with convolution neural network induced MSER trees. In: Computer vision–ECCV, pp 497–511 14. Neubeck A, Gool L (2006) Efﬁcient non-maximum suppression. In: 18th ICPR 15. Shao Y, Wang C, Xiao B (2013) Fast self-generation voting for handwritten Chinese character recognition. Int J Doc Anal Recognit (IJDAR) 16(4):413–424 16. Shao Y, Wang C, Xiao B (2015) A character image restoration method for unconstrained handwritten Chinese character recognition. Int J Doc Anal Recognit (IJDAR) 18(1):73–86 17. Wang T, Wu DJ, Coates A, Ng AY (2012) End-to-end text recognition with convolutional neural network. In: IEEE international conference on pattern recognition, pp 3304–3308 18. He K, Zhang X, Ren S et al (2016) Identity mappings in deep residual networks 19. Shao Y, Gao G, Zhang L et al (2015) The ﬁrst robust mongolian text reading dataset CSIMU-MTR, pp 781–788 20. Wolf C, Jolion JM (2006) Object count/area graphs for the evaluation of object detection and segmentation algorithms. Int J Doc Anal Recognit 8(4):280–296

Coverage Probability Analysis of D2D Communication Based on Stochastic Geometry Model Xuan-An Song1,2, Hui Li1,2(&), Zhen Guo1, and Xian-Peng Wang1 1

2

College of Information Science and Technology, Hainan University, Haikou 570228, China [email protected] Engineering Research Center of Marine Communication and Network in Hainan Province, Haikou 570228, China

Abstract. Relaying is a common application of D2D communication, which optimizes system capacity and increases the coverage of mobile cellular networks on shared downlink resources. We established a network model of cellular base-stations and adopted the theory of stochastic geometry. Based on the model, the coverage probability analysis of the network is analyzed to select a speciﬁc user as the relay node, and the relay point uses the forwarding strategy of the decoding and forwarding. Subsequently, D2D communication can help the edge user to communicate with the base-station. The coverage probability expression of the downlink cellular network is deﬁned, then the coverage probability of the cellular link, the base-station to the relay link, and the relay to the edge user link are derived. Simulation results show that with the increasing of density of the macro base-stations, the coverage probability of the whole network will increase and the ﬁnal coverage probability will become saturated. Keywords: Stochastic geometry probability

Relay D2D communication Coverage

1 Introduction With the development of wireless networks, the challenges of future cellular networks and transmission reliability are enormous. The existing base-station deployment cannot meet the needs of users’ requirements. In general, the cellular network model uses the Wiener model to perform performance analysis on cellular links [1–3]. Wiener model has two disadvantages. Firstly, it is an overly idealized model. In this model, it is considered that the channel between the user and the base-station is an ideal channel, the interference within the base-station coverage is a constant, and the interference between the coverage of the base-station is negligible. However in reality, inter-base-station interference cannot be ignored due to an increase of signal interference between cells, and also due to an increase of mobile phone users in space. Moreover, as the density of users in the space changes, the interference inside the base-station also changes. So it is also ideal to assume the interference signal in the base-station as a constant. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 83–93, 2020 https://doi.org/10.1007/978-981-13-9409-6_11

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On the other hand, Wiener model assumes that all cells in space are strictly regular hexagons or circles. However, in the actual situation, the shape of the cell is random, so the traditional Wiener model is no longer suitable to be an analyzing modern, and it is no longer suitable for complex wireless communication systems [4, 5]. Due to these defects of the Wiener model, many researchers began to use random geometric models to describe the topology of modern communication networks. The stochastic geometric model considers that all base-stations and users in the space are randomly distributed, so that it can well meet the actual situation that contemporary users are randomly distributed throughout the space [6]. Literatures [7, 8] use a stochastic geometric model to analyze the signal-to-interference ratio of a randomly selected cell edge user in the downlink. They use a series of effective tools provided by modern stochastic geometry theory to obtain a simpler comparison than the Wiener model. In recent years, mobile communication technologies have developed rapidly, and business demands such as high speed, low energy consumption, low latency, and personalization have brought new challenges and promoted academic research on emerging communication technologies. Device-to-Device (D2D) communication technology is a promising technology in future mobile communication system. It is listed as the ﬁrst important technique in the Universal Mobile Telecommunications System (UMTS) project group. D2D communication has become a vital research topic of 5G mobile communications. The communication technology can directly establish a communication link between terminals that are close to each other, communicate by using the licensed spectrum, thereby effectively reduce the load of the base-station and improve the utilization efﬁciency of the spectrum. At the same time, D2D communication, as a short-range communication method with low transmission power and high transmission rate, is conducive to improving energy efﬁciency, extending terminal life time, and bringing convenience to users [9, 10]. Device-to-device communication under cellular networks is considered one of the most promising methods for dealing with spectrum resource shortages [11], which allows mobile terminals to communicate with each other directly in the cellular network [12] and signiﬁcantly mitigates the pressure on the base-station. D2D communication has recently attracted a lot of attention due to the advantages of improving spectrum utilization efﬁciency, increasing transmission rate, saving power and improving network. Based on the analysis of the coverage probability of the network and the random geometric model, the users are selected to be the relaying stations (RSs). The RS uses the forwarding strategy to transmit the D2D communication for the purpose of helping the edge user. The user equipment (UE) communicates with a base-station (BS). We deﬁne the coverage probability expression of the downlink relay cellular network, and then derive the coverage probability of the cellular link, the base-to-relay link, and the relay-to-edge user link. The conclusion of coverage probability under D2D communication based on stochastic geometric model is beneﬁcial to practical applications. The rest of this paper is organized as the following. In Sect. 2, we present the system model and methodology of the analysis. In Sect. 3, the downlink performance of proposed mechanism is analyzed, using tools from stochastic geometry. Simulation results are presented in Sect. 4 and ﬁnally, conclusion are drawn in Sect. 5.

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2 System Model Consider a single-layer downlink network that only contains cellular base-stations and users, and uses stochastic geometry to construct a network model of cellular networks and users. The location of the BS follows a homogeneous Poisson point process (PPP) UB with density kB, and D2D users also follows the distribution PPP UR with density kR [13]. D2D communication is a scenario in which the D2D link multiplexes the downlink resources of the cell, as shown in Fig. 1, where the red area represents the communication area of the cellular link. UE2 outside the red area is only allowed to connect to the BS through the relaying link. We know that users outside the gray area can communicate with the relay or base-station of the neighboring cell to treat other links as interference. Therefore, there are two types of links in the system: cellular links and D2D communication link includes relay links of BS-RS (Base-to-relay link) and RSUE (Relay-to-user link). The forwarding policy adopted by the relay user is a decoding and forwarding policy. The working mode is half-duplex, and the speciﬁc RS communicates with the base-station for the closest geometric distance, and the coverage of the relay user is a circle with a radius of R.

Fig. 1. System relay model, where red area represents the communication area of the cellular link and other area represents D2D communication area

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It is assumed that the distance between the users to in the base-station is r. Since the base-station deployment is PPP with a density parameter of kB, the probability density of r is fr ðrÞ ¼ 2pkB expðpkB r 2 Þ [8]. The D2D user also obeys the law of independent PPP. However, the special relay point and the edge user obey the independent stationary point process in the circular area of the relay point [9]. Therefore, the probability density distribution function of the distance d between the edge user and the relay point is fR ðRÞ ¼ 2d=R2 .

3 Performance Analysis of Downlink Assuming that all channels in the network have experienced Rayleigh fading, the side channel gain obeys the exponential distribution with a parameter value of 1, i.e. h exp. Considering a downlink cellular network, the transmitter x (base-station or relay user), and the receiver y (relay user or edge user) have a signal to interference plus noise ratio (SINR) [13] is given by SINRðx ! yÞ ¼ S=ðI þ NÞ

ð1Þ

where S is the power of the receiver y to receive the useful signal in the transmitter x, so S can be rewritten as S = Phr−a; where P is the transmitting power of x, h is the channel power gain caused by small-scale fading, a is the path loss factor, I is the interference from other transmitters in the same frequency band and N is the noise. We now assumes that the channel is ideal additive Gaussian white noise, so N = r2. Based on the above assumption, the probability of the coverage is deﬁned as the probability that the SINR of the receiver y is greater than or equal to the threshold value b, which is p ¼ PðSINR bÞ

3.1

ð2Þ

Coverage Probability of Cellular Links

It can be known from Formula (2) that when the base-station communicates with the user of UE1. According to the independent PPP distribution, it is assumed that the BS transmits to the cellular user UE1. For this cellular link, the coverage probability can be written as pcu ¼ PðSINRcu bcu Þ

ð3Þ

P a where SINRcu ¼ PIcucu hþo rr2 ; Icu ¼ i2UC =fx0 g Pcu hi ria ; Pcu is the power transmitted by the BS to the cellular user UE1 and ho is the channel gain. Icu denotes the aggregate interference.

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Theorem 1 The downlink coverage probability of a cellular network with BS-UE1 and is given by Z expðpkB r 2 ð1 þ qðbcu ; aÞÞÞ expðbcu r a r2 =Pcu Þrdr ð4Þ pcu ¼ 2pkB r[0

R1 1 dh; h is an identiﬁer through Laplace transform. where qðbcu ; aÞ ¼ b2=a 2=a cu bcu 1 þ ha=2 Proof refers to Appendix 1. 3.2

Coverage probability of D2D links

The analysis of the D2D link consists of two parts: one is the coverage probability analysis of BS-RS, and the other part is the coverage probability analysis composed of RS-UE2 [14]. (1) Coverage probability analysis of BS-RS links It is assumed that when the RS receives the BS signal, whose SINR is greater than or equal to bcR, the RS can decode, and the power transmitted from the BS to the RS is PcR. Then, the coverage probability of the BS-RS downlink can be deﬁned as pcR ¼ PðSINRcR bcR Þ a

cR go r where SINRcR ¼ PIcR þ r2 ; IcR ¼ the aggregate interference.

P i

ð5Þ

PcR gi ria , and go is the channel gain and IcR denotes

Theorem 2 The coverage probability PcR of the BS-RS link can be expressed as Z expðpkB r 2 ðkB þ kqðbcR ; aÞÞÞ expðbcR r a r2 =PcR Þr dr ð6Þ pcR ¼ 2pkB r[0 2=a

where k = min(kB ; kR Þ; qðbcR ; aÞ¼bcR

R1

2=a

bcR

1 dh. 1 þ ha=2

The proof and derivation pro-

cess of Theorem 2 is given in Appendix 2. (2) Coverage probability of RS-UE2 links The edge user UE2 receives the information of the base-station through the assistance of the RS, and distributes it in a smooth point process in a circular area, in which each of the relay users RS is centered and R is a radius. It is assumed that the PRU is the transmission power sent by the relay user to the edge user, b3 is the threshold value received by the UE2. And the IRU is the sum of the interferences received by the UE2 to the remaining relay users by ignoring the mutual interference between the base-station and the UE2 and each UE2. Then the coverage probability of the RS-UE link is given by pRU ¼ PðSINRRU bRU Þ

ð7Þ

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P mo ra a where SINRRU ¼ PIRU 2 ; IBR ¼ i PBR mi ri , and mo is the channel gain and IRU RU þ r denotes the aggregate interference. Theorem 3 The coverage probability D2D (BS-RS) link can be expressed as pRU ¼

2 R2

ZR

expðpkr 2 pcR qðbRU ; aÞÞ expðbRU r a r2 =ð1 þ bRU ÞPRU Þr dr

ð8Þ

0

where k ¼ minðkB ; kR Þ; qðbRU ; aÞ¼

bRU 1 þ bRU

2=a R 1 0 1 þ1ha=2 dh. Proof of Formula (8)

refers to Appendix 3.

4 Simulation Analysis The simulation analysis is based on the probability expression derived from the previous section. The relevant parameters used in the simulation are set in Table 1.

Table 1. System parameters Symbol kB kR Pcu/PcR/PRU R bcu/bcR/bRU r2

Description Density of BSs Density of RSs Transmit power Radius of Rs SINR threshold Nois power

Value 10−5 BS/m2 10−4 BS/m2 43 dBm/30 dBm/33 dBm 40 m −10 dB −60 dBm

Simulation analysis is performed under MATLAB according to parameter settings, since the cellular link is a special case of a special BS-RS link. Therefore, Fig. 2 shows the variation of the coverage probability of the BS-RS link PcR vs. density of BSs kB with different a. The curve can be divided into two parts according to the relationship between kB and kR. (1) If kB< kR, the value of k is kB. In this part, as the value of a increases, the degree of curvature of the curve increases continuously. When a = 3, the probability of pcR coverage does not change with the change of kB. When a = 4 or 5, the pcR coverage probability increases as kB increases. (2) If kB> kR, the value of k is kR, and the probability coverage increases slowly as kB increases. In addition, under the difference of the path loss factor a, the magnitude relationship of pcR is not determined, which means that the magnitude of the transmitted signal and interference received by the RS is uncertain for different base-station densities.

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BS-RS Link Coverage Probability (p cR )

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 =5 =4 =3

0.2 0.1

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Density of BS (10-4 m 2 )

Fig. 2. Relationship between density of base-stations and BS-RS link coverage probability under different path loss

Figure 3 shows the relationship of pRU and kB with different a and UE2 links. Similarly, the curve can be divided into two parts according to the magnitude relationship of kB and kR:

RS-UE2 Link Coverage Probability (pRU )

1 =5 =4 =3

0.98

0.96

0.94

0.92

0.9

0.88

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Density of BS (10-4 m 2 )

Fig. 3. Relationship between density of base-stations and RS-UE2 link coverage probability under different path loss

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(1) If kB< kR, the value of k is kB. In this part, as the value of a increases, the probability of pRU coverage decreases approximately linearly with the increase of kB. (2) If kB> kR, the value of k is kR. At this time, only pcU (in the pRU expression) changes with kB, and the curve decreases slowly as kB increases.

5 Conclusions Based on the stochastic geometry theory, we analyzed the downlink D2D communication networks and established a system relay model. According to the nature of the PPP, the relationship between the cellular BS and the user is established by using a mathematical random geometric model. Then, based on the model, the coverage probability analysis of the network is analyzed to select a speciﬁc user as the RS. The method of RS using decoding and forwarding is one of D2D communication technologies; we derive the coverage probability of cellular links, BS-RS links and RS-UE links. The simulation results show that as the density of the macro base-station increases, the coverage probability of the whole network will increase and the ﬁnal coverage probability will become saturated. In the future work, we will study the network performance of the cellular network in a random geometric model and the performance of direct communication between devices and devices without considering RS. Acknowledgements. This work was supported by High and New Technology Project of Hainan Province Key R. & D. Plan (ZDYF2018012) and the National Natural Science Foundation of China (No. 61661018). Hui Li is the corresponding author.

Appendix 1: Proof of Theorem 1 According to the deﬁnition of Eq. (3) and SINR, the coverage probability of the BSUE1 link can be expressed as Z P ho bcU r a ðIcU þ r2 =PcU jrÞfr ðrÞdr ð9Þ pcU ¼ Er PðSINRcU bcU jr Þ ¼ r[0

where fr(r) is BS probability density function (PDF) [8]. By ho * exp(1), we can rewrite Eq. (3) as Pðho bcU r a ðI cU þ r2 Þ=PcU jrÞ ¼ E P ho bcU r a ðIcU þ r2 Þ=PcU jr; I cU ¼ E exp bcU r a ðIcU þ r2 Þ=PcU ¼ expðbcU r a =PcU ÞLðbcU r a =PcU Þ

ð10Þ

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where L is Laplace transform of IcU. Deﬁned by the Laplace transform and ho * exp (1), it can be written as 2 0 13 X a Pcu hi ri A5 LIcU ðsÞ ¼ EðexpðsIcU Þ ¼ E4exp@s 2 ¼ EUC 4

3

Y

i2UC

i2UC =fx0 g

0

1 5 @ a ¼ exp 2pkB 1 þ sP cU ri =fx g

Z1

0

1 r

1

1 uduA 1 þ sP1 hi ria ð11Þ

The ﬁnal step of the above derivation is obtained from the properties of the Q probability generation function of the PPP, which satisﬁes E x2U gðxÞ ¼ R1 exp k R2 ð1 gðxÞÞdx [14]. S is changed by bcU r a =PcU , and the interference IcU can be further derived as 0 1 Z1 1 uduA 1 LIcU ðb1 r a =PcU Þ ¼ exp@2pkB 1 þ bcU r a ua r 0 1 Z1 ð12Þ b cU A ¼ exp@2pkB a udu bcU þ ðu=rÞ r

¼ expð2pkB qðbcU ; aÞÞ 2=a

where qðbcU ; aÞ¼bcU

R1

2=a

bcu

1 dh; h 1 þ ha=2

¼

u . 1=a2 rbcU

And Eq. (4) can be obtained by com-

bining Eqs. (9)–(12).

B: Proof of Theorem 2 The proof of Theorem 2 is similar to Theorem 1, except that the subscripts are different. Where k = min(kB ; kR Þ value depended on pcR.

A: Proof of Theorem 3 By the deﬁnition of Eq. (7), PRU can be converted into pRU

ZR PRU mo r a ¼P bRU ¼ Pðlo r a ðIRU þ r2 Þ=PRU jrÞfR ðrÞdr IRU þ r2

ð13Þ

0

In order to simplify the derivation, Ir ¼ IRU þ PRU mo r a is assumed to be signal transmitted from the RS, so PRU can be further rewritten as

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ZR ZR bRU ra ðIRU þ r2 Þ 2 b r a ðIRU þ r2 Þ rdr P mo jr fR ðrÞdr ¼ 2 E exp RU ð1 þ b3 ÞPRU R ð1 þ bRU ÞPRU 0

0

¼

2 R2

ZR 0

exp

bRU r a r2 bRU r a L rdr ð1 þ bRU ÞPRU ð1 þ bRU ÞPRU

ð14Þ The above derivation uses fR ðrÞ ¼ 2r=R2 and mo exp(1). LðÞ can be expressed as LðbRU r a =PRU Þ ¼ expðpr 2 kpcR qðbRU ; aÞÞ 2=a

where qðbRU ; aÞ¼bRU

R1

2=a

bRU

1 dh, 1 þ ha=2

ð15Þ

k ¼ minðkB ; kR Þ. Combining Eqs. (13)–(15), we

can get Eq. (8).

References 1. Wyner AD (1975) The wiretap channel. Bell Labs Tech J 54(8):1355–1387 2. Somekh O, Zaidel B, Shamai S (2007) Sum rate characterization of joint multiple cell-site processing. IEEE Trans Inf Theory 53(12):4473–4497 3. Jing S, Tse DNC, Soriaga JB et al (2008) Multicell downlink capacity with coordinated processing. EURASIP J Wireless Commun Netw: 586878 4. ElSawy H, Hossain E, Haenggi M (2013) Stochastic geometry for modeling, analysis and design of multi-tier and cognitive cellular wireless networks: a survey. IEEE Commun Surv Tutorials 15(3):996–1019 5. Haenggi M, Andrews J, Baccelli F et al (2009) Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE J Sel Areas Commun 27(7):1029–1046 6. Lee CH, Shih CY, Chen YS (2013) Stochastic geometry based models for modeling cellular networks in urban aeas. Wireless Netw 19(6):1063–1072 7. Ganti RK, Bacelli F, Andrews JG (2011) A new way of computing rate in cellular networks. In: IEEE international conference communications (ICC), June 2011 8. Andrews JG, Baccelli F, Ganti RK (2011) A tractable approach stochastic geometry for wireless networks coverage and rate in cellular networks. IEEE Trans Commun 59 (11):3122–3134 9. Universal Mobile Telecommunications System (UMTS), Selection procedures for the choice of radio transmission technologies of the UMTS, UMTS 30.03, version 3.2.0 10. Guidelines for evaluation of radio interface technologies for IMT-advanced, report ITU-R M.2135 11. Fodor G et al (2012) Design aspects of network assisted device-to-device communications. IEEE Commun Mag 50(3):170–177 12. Peng T, Lu Q, Wang H, Xu S, Wang W (2009) Interference avoidance mechanisms in the hybrid cellular and device-to-device systems. In: Proceedings of IEEE international symposium on personal indoor and mobile radio communications, pp 617–621 13. Al-Hourani A, Kandeepan S, Jammalipour A (2016) Stochastic geometry study on deviceto-device communication as a disaster relief solution. IEEE Trans Veh Technol 65(5):3005– 3017

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14. Yu H, Li Y, Xu X, Wang J (2014) Energy harvesting relay-assisted cellular networks based on stochastic geometry approach. In: 2014 international conference on intelligent green building and smart grid (IGBSG), Taipei, pp 1–6 15. Stoyan D, Kendall WS, Mecke J et al (1995) Stochastic geometry and its application, Chichester. Wiley Chichester, UK

Study of Fault Pattern Recognition for Spacecraft Based on DTW Algorithm Guoliang Tian(&), Lianbing Huang, and Guisong Yin Institute of Manned Space System Engineering, 100094 Beijing, China [email protected]

Abstract. A time series analysis method for spacecraft telemetry data is presented in this paper. For spacecraft testing and on-orbit flight, this method can monitor the changes of telemetry data automatically and identify the failure modes of spacecraft. Using dynamic time warping (DTW) algorithm, combining historical data samples as well as fault cases with this method analyzes the similarity of telemetry data transformed into time series. By comparing the results of analysis with the results of DTW distance calculation, the relative deviation of data is measured and the abnormal data in fault mode is identiﬁed. The results show that the telemetry data analysis method based on DTW algorithm can effectively detect data anomalies and realize fault identiﬁcation, which has a certain application prospect. Keywords: DTW

Spacecraft Data analysis Fault recognition

1 Introduction At present, Euclidean Distance is the main method to measure the similarity of time series,it directly calculates the distance between the corresponding points on the time axis [1]. This algorithm is simple and fast, but it can only be applied to time series of equal length, and it is sensitive to the migration and mutation of sequence on time axis. In the early stage, Euclidean distance or Euclidean-like distance (such as Lp distance) was widely used in time series similarity comparison [2]. Such distance has good mathematical properties and is easy to calculate. By calculating one-to-one correspondence between points, better results can be obtained when the amplitude of time series does not change much. However, when Euclidean distance is used to measure the difference between time series, the requirement of sequence is more stringent, and it is easy to produce large difference, which leads to the ﬁnal dissimilarity of the original similar sequence, thus making the result of similarity comparison inaccurate [3]. Dynamic Time Warping (DTW) distance is widely used in speech recognition. Based on the theory of dynamic programming, it is a non-linear programming technology that combines time planning with distance measurement. DTW distance is ﬁrst introduced into time series data mining by Berndt and Clifford to measure the similarity between two time series [4]. It does not require one-to-one matching between points in two time series, and allows sequence points to be self-replicated and then matched. DTW is a distance that allows time series to bend in the direction of time axis. It is not a point-to-point calculation, but can skip several points in the matching sequence within © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 94–102, 2020 https://doi.org/10.1007/978-981-13-9409-6_12

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local area, so that the two sequences can match in a more “co-ordinated” way. DTW not only maintains the matching between most point pairs, but also avoids the shortage of Euclidean-like distance.

2 Principle of DTW Algorithm The calculation of DTW distance can be done not only on the original sequence, but also on the reduced dimension sequence. It is assumed that the length of the two time series after dimensionality reduction is m, n: q½1 : m ¼ fq1 ; q2 ; . . .; qm g

ð1Þ

c½1 : n ¼ fc1 ; c2 ; . . .; cn g

ð2Þ

The DTW distance of q and c can be calculated recursively according to the original deﬁnition of DTW distance, but there will be many repeated calculations. Generally, a distance matrix of m n can be constructed, which is called a bending matrix, as shown in Fig. 1. The square (i, j) ð1 i m; 1 j nÞ in the graph corresponds to the matching between the data points qi and cj , its value is d qi ; cj , which is called base distance. 2 Consistent with reference [2], data point distance d qi ; cj ¼ qi cj is used as base distance. M

Similar path

q

c Fig. 1. Similar distance matrix

After constructing the bending matrix, the correspondence between q and c points is transformed into a bending path from the square (1, 1) to the square (m, n) in the bending matrix: W ¼ w1 ; w2 ; . . .wi ðmaxðm; nÞ l m þ nÞ

ð3Þ

Deﬁne a mapping function f : ðq; cÞ ! W that maps q, c point pairs to a square in a curved path, that is:

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wk ¼ fw qi ; cj ; 1 i m; 1 j n; 1 k l

ð4Þ

In practical calculation, bending paths generally have the following properties: (a) Path continuity The adjacent (including diagonal) squares in a curved path are continuous, i.e.

wk ¼ fw qi; cj ! i0 i þ 1; j0 j þ 1 w k þ 1 ¼ f w qi 0 ; c j 0

ð5Þ

(b) Monotonicity The curved path moves monotonously along the time axis, i.e.

wk ¼ fw qi; cj ! i i0 ; j j0 wk þ 1 ¼ fw qi0 ; cj0

ð6Þ

The DTW distance between q and c is transformed into solving the bending path with the minimum distance in the bending matrix. DTW ðq; cÞ ¼ arg min

c X

! wi

ð7Þ

i¼1

3 Test Data Analysis Method Based on DTW Algorithms In the traditional spacecraft test, the parameter interpretation method mainly extracts the current value of the telemetry data stream, and interprets it according to the upper and lower limits given by the telemetry data. In traditional spacecraft testing, the main method of parameter interpretation is to extract the current value of the telemetry data stream, and then interpret the extracted data according to the upper and lower limits given by the telemetry data. There are some shortcomings in this interpretation method. Firstly, if the design range of telemetry parameters is wide (such as current value, temperature value, etc.), it is not easy to ﬁnd abnormal changes in the parameter range. In addition, because the interpretation mechanism only aims at the correctness of single point telemetry, it is impossible to analyze the changes in overall trends of data flow. Thirdly, the information of the criterion given by the single point interpretation mechanism is insufﬁcient, and it is not easy to carry out further analysis of the abnormal phenomena. The interpretation method introduced in this paper is as follows. The method transforms the spacecraft telemetry data into time series, quantiﬁes the data changes by using the similarity analysis results of DTW distance calculation data and historical sample data, and then analyses whether the data migration changes exceed the design

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requirements, so as to realize data interpretation. This method improves the shortcomings of traditional interpretation methods in detecting data anomalies and trend changes. In spacecraft testing, such as the whole power-on start-up process and the subsystem power-on start-up process, there is a certain degree of repeatability. Therefore, sufﬁcient historical data can be used as a sample for similarity calculation. The results of calculation are based on the support of a large number of data, which can more accurately use the similarity calculation results to ﬁnd the trend of data change. 3.1

Data Analysis Workflow

The flow chart of interpretation application based on DTW is shown in Fig. 2. The speciﬁc flow chart is as follows: (1) Initialize the sample library and the related decision threshold database, in which the samples are iteratively revised according to the accumulation. (2) Sampling the telemetry needed for spacecraft analysis to form time series. (3) Find the corresponding telemetry samples from the sample library and calculate the DTW distance. (4) Comparing with the associated decision threshold, the interpretation result is generated. Judge threshold feedback correction, according to the statistical results of the data sample feedback correction.

Sample database

Modifing sample data

Decision threshold Library Threshold of DTW distance

Spacecraft testing or onorbit flight

Normal telemetry sampling

Calculating distance of DTW

Modified decision threshold sample

Judge result

Report and end

Fig. 2. DTW data interpretation process

The system is designed for automation. The remote sensing samples and the threshold setting are all self-learning. The system is revised by iteration in order to accurately reflect the calculation results. By using KNN classiﬁcation method to optimize, the optimal classiﬁcation point and its upper limit offset are found, and the threshold is automatically corrected. After processing, the decision threshold can reflect the calculated results more accurately and ﬁnd the telemetry deviation. Data sampling time should be set according to speciﬁc needs. Such as the analysis and comparison of device startup process in a short time after instruction is sent. It can also be used to analyze the trend of the whole life cycle of the same test project.

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Analysis of Test Results

In order to illustrate the application of DTW in test interpretation, the author selected the starting current of a certain equipment after power-up as the telemetry parameter and sampled ﬁve sets of 800 s data respectively to form a time series. In this way, time series can be formed. The DTW distance between the time series and the historical samples is calculated, and the change of the data is assessed by comparing the calculated distance with the decision threshold. The data sample curve is shown in Fig. 3.

Fig. 3. Telemetry samples and data curves

The calculation results are shown in Table 1. The DTW distances of data samples and telemetry samples 1, 2, 3 and 4 are similar, and the calculated results are between 3 and 4. The calculation results are quite different from those of telemetry sampling 5. It shows that sample 5 deviates greatly from the sample. Combining with the curve of Fig. 3, we can see that telemetry sampling 5 has obvious downward jump point. The experimental results show that using DTW algorithm to calculate similarity, we can ﬁnd the deviation of experimental data. If jump points occur, the distance of DTW increases signiﬁcantly. The algorithm is sensitive to jump variation constant data and can reflect the impact of data deviation.

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Table 1. DTW distance between telemetry sampling and reference sample Category Telemetry Telemetry Telemetry Telemetry Telemetry

3.3

sampling sampling sampling sampling sampling

DTW 3.584 3.403 3.313 3.420 7.879

1 2 3 4 5

Threshold Determination Method

Using DTW algorithm for similarity judgment needs to compare the calculated results with the set similarity judgment threshold. When the results of comparison are within the prescribed scope, the similarity is identical and the data deviation meets the requirements. Therefore, the validity of the set threshold has a great influence on the whole interpretation. The determination of threshold is mainly based on experience and data accumulation, and the determination threshold of each parameter needs to set an appropriate range. The threshold setting should be self-learning and corrected by certain data accumulation.

4 Fault Recognition Method Based on DTW Algorithms The author further uses DTW algorithm to add fault recognition function on the basis of interpretation system. Firstly, according to the theoretical characteristics of historical data samples or abnormal data, a set of fault mode database is established. In the fault mode database, there are many kinds of fault samples formed by the fault occurred and the affected telemetry data. During spacecraft testing or on-orbit flight, if the collected telemetry data is judged to be abnormal, the system will retrieve the fault mode library samples, and calculate the DTW distance between the sampled data and the fault mode samples, and then analyze the results to match the most similar fault mode (Fig. 4). Telemetry fault mode library Fault reference sample 1

. . .

Fault reference sample 2

History data and model

Fault reference sample n

Spacecraft testing or onorbit flight

Confirming fault data

Calculating distance of DTW

ordering possible fault

Fig. 4. DTW data interpretation process

Report and end

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Workflow of Fault Recognition System

The application flow of fault identiﬁcation based on DTW is as follows: (1) After being judged as abnormal data, the fault feature part of the data is extracted as the analysis object. (2) Extraction of fault mode samples associated with the parameter from the fault mode database. (3) Calculating DTW Distance Based on Fault Mode Samples. (4) Comparing with decision threshold, the matching result of fault mode is given. (5) If the result does not match, the fault is updated to the fault mode database as the fault pattern sample. 4.2

Analysis of Test Results

In order to make comparative analysis, the ﬁfth telemetry sampling of current telemetry parameters in the previous chapter is selected. The sampling data is real-time data with jump points in 300 s. See Fig. 5 for details.

Fig. 5. Telemetry sample and fault sample curve

The DTW distances between telemetry sampling and normal data samples, telemetry sampling and fault mode samples are calculated by using the algorithm program. The results are shown in Table 2. From the calculation results, the DTW distance between telemetry sampling and fault mode 2 is the smallest compared with other samples, which indicates that fault mode 2 is the most likely to occur. The results of the analysis are consistent with the state shown in Fig. 5. According to the characteristics of parameter changes caused by faults, similar samples can be obtained by similarity calculation, and the most possible fault modes can be found.

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Table 2. Fault telemetry sampling and fault mode sample calculation DTW Category Telemetry sample Failure mode 1 Failure mode 1 Failure mode 1 Failure mode 1

DTW 21.9258 6.785 32.331 26.160 21.972

Fault samples can be obtained from historical fault cases. According to the theoretical calculation, the parameters related changes caused by simulation faults, such as current sag, surge and overload jump, can be modeled and simulated theoretically. DTW algorithm for fault identiﬁcation can avoid the requirement of ﬁnding feature points in traditional fault identiﬁcation methods, and does not require the number of fault samples to be consistent with the number of telemetry samples. As long as the fault samples can reflect the fault characteristics, this method can accurately measure the similarity of the fault and achieve fault identiﬁcation.

5 System Performance Optimization DTW algorithm uses dynamic programming method to calculate the similarity between two time series. The complexity of the algorithm is O(N * M). When both time series are relatively long, the efﬁciency of DTW algorithm is relatively low. Therefore, in order to improve the recognition efﬁciency in DTW distance calculation, the lower bound function of DTW can be calculated. Preliminary screening is carried out to directly remove time series that do not meet the lower limit conditions, so as to narrow the scope of determination. The LB_Keogh lower bound function is used in the application of this paper. A sequence ½qL ; cU is constructed to compute the part of the target sequence that exceeds the boundaries of the constructed sequence. The results are taken as the lower bound of DTW distance, where qL and cU are sequences of minimum and maximum values of data in sliding window with width of 2 W + 1, respectively. Similar thresholds can be quickly obtained by LB_Keogh lower bound function. This method can reduce the amount of calculation and improve the system performance.

6 Conclusion During the whole development cycle of spacecraft, a large amount of operational data is generated, which has a good data application foundation. Aiming at these data, DTW distance algorithm is used as analysis tool. By calculating the deviation between historical data samples and data samples, the abnormal changes of data can be effectively found and the trend of data changes can be analyzed. At the same time, on the basis of

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data interpretation, fault pattern recognition and other applications are introduced to enhance the ability of fast fault location and processing. It provides automatic and intelligent monitoring means for spacecraft testing and on-orbit flight.

References 1. Li J, Wang Y (2007) EA_DTW: early abandon to accelerate exactly warping matching of time series. In: Proceedings of international conference on intelligent systems and knowledge engineering (ISKE) 2. Keogh E, Ratanamahatana C (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386 3. Eamonn J, Michael J (2001) Derivative dynamic time warping. In: The ﬁrst SIAM international conference on data mining, IEEE. Washington, pp 1–11 4. Berndt DJ, Clifford J (1996) Finding patterns in time series: a dynamic programming approach. In: Weld D, Clancey B (eds) Advances in knowledge discovery and data mining, AAAI/MIT, The MIT Press, Oregon, Portland, pp 229–248

A Joint TDOA/AOA Three-Dimensional Localization Algorithm for Spacecraft Internal Yin Long(&), Ke Zhu, and Cai Huang Institute of Manned Space System Engineering, China Academy of Space Technology, 100094 Beijing, China [email protected]

Abstract. Considering the lack of three-dimensional localization scheme for spacecraft internal, a joint TDOA/AOA three-dimensional localization algorithm based on Wireless Sensor Network (WSN) is proposed in this paper. WSN is deployed in the spacecraft which is composed of reference nodes and unknown nodes, and the reference nodes’ position are known which help to locate the unknown nodes. Only six reference nodes are enough for the proposed method to localize all the unknown nodes within the WSN in three-dimension theoretically, and the synchronization of the network is not necessary, satisfying the low complexity requirement of the WSN. TDOA (Time Difference of Arrival) is adopted to estimate AOA (Angle of Arrival), and the angle is estimated by the hierarchical deployment of the reference nodes by which the complicated antenna arrays for AOA are avoided. A three dimensional coordinate is established by setting the plane of the reference nodes as plane XOY and the z coordinate is computed according to the angle estimated by the AOA. Finally, the unknown node is projected on the plane XOY, and the x coordinate and y coordinate are computed by trilateration localization tragedy. Keywords: TDOA AOA ZigBee Spacecraft internal Three-dimensional localization

1 Introduction As the rapid development of spacecraft technique, the spacecraft get larger which could accept more astronauts in the future. The astronauts are working and resting in the spacecraft, and how to localize them is going to be studied. The GPS method can locate fast and precisely, however, it can only be employed outside as the GPS signal is badly decreased inside the spacecraft. At the present, the wireless sensor network (WSN) is applied for the inside localization widely, and the WSN can be based on ultrasonic, infrared, WiFi, Bluetooth, ZigBee and RFID. According to the theory of localization, the localization method can be divided by whether measuring the distance. The rangebased localization methods are more precise than the range-free ones, which concludes TOA [1] (Time of Arrival), TDOA [2] (Time difference of Arrival), AOA [3] (Angle of Arrival), RSSI [4] and ﬁngerprint. However, these methods are only used for the twodimensional localization which could not be applied in the three-dimensional localization directly. For the three-dimensional localization, the Landscape-3D [5] and the © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 103–109, 2020 https://doi.org/10.1007/978-981-13-9409-6_13

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SLBS [6] scheme are proposed, and the shortage of them is that they rely on the moving reference nodes which broadcast range measuring signal periodically. The APIT [7] method is proposed basing on the spherical coordinate computation which has high requirement for the calculating ability of the reference node. Therefore, a joint TDOA/AOA three-dimensional localization algorithm based on WSN is proposed, in which the moving reference nodes are not necessary and the complexity of the algorithm is acceptable.

2 Localization Algorithm The WSN is composed of reference nodes and unknown nodes, in which the position of the reference nodes are known while the unknown ones are not. On the assumption that all the reference nodes are deployed in the XOY plane such as A, B, C in Fig. 1, and the unknown node which is labeled as D is projected to the XOY as D′. Supposing the angle of AD and AD′ is h1, BD and BD′ is h2, and CD and CD′ is h3, setting the length of DD′ as h. A three dimensional coordinate is established and the plane which hold the reference node A/B/C is set as the plane xoy. Supposing the coordinate of D/D ′ is (x, y, z)/(x′, y′, 0), the length of AD/BD/CD is m1/m2/m3, and the length of AD′/BD ′/CD′ is r1/r2/r3, then comes the following formula:

Fig. 1. The localization 3D coordinate

2 3 2 03 x x 4 y 5 ¼ 4 y0 5 h z

ð1Þ

h ¼ m1 sin h1

ð2Þ

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h ¼ m2 sin h2

ð3Þ

h ¼ m3 sin h3

ð4Þ

r1 ¼ m1 cos h1

ð5Þ

r2 ¼ m2 cos h2

ð6Þ

r3 ¼ m3 cos h3

ð7Þ

Supposing the coordinate of reference node A/B/C in plane xoy is (x1, y1)/(x2, y2)/ (x3, y3), then comes the following formula:

x0 y0

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ r1 ¼ ðx1 x0 Þ2 þ ðy1 y0 Þ2

ð8Þ

r2 ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ðx2 x0 Þ2 þ ðy2 y0 Þ2

ð9Þ

r3 ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ðx3 x0 Þ2 þ ðy3 y0 Þ2

ð10Þ

2ðx1 x3 Þ 2ðy1 y3 Þ ¼ 2ðx2 x3 Þ 2ðy2 y3 Þ

1

x21 x23 þ y21 y23 þ r32 r12 x22 x23 þ y22 y23 þ r32 r22

#1 2 3 2" 3 2 2 2 2 2 2 x 2ðx1 x3 Þ 2ðy1 y3 Þ x x þ y y þ r r 1 3 1 3 3 1 7 4y5 ¼ 6 4 2ðx2 x3 Þ 2ðy2 y3 Þ x22 x23 þ y22 y23 þ r32 r22 5 z m sin h 1

ð11Þ

ð12Þ

1

The unknown node D is equipped with ultrasonic and RF ejector, and it periodically broadcast signal for distance measuring in both ultrasonic and RF. The reference node is equipped with ultrasonic and RF receiver, and the distance between reference node and the unknown one can be estimated by the time difference of the two kinds of signal received as below: d ¼ v tTDOA

ð13Þ

d denotes the distance, v denotes the speed of ultrasonic, tTDOA denotes the time difference of the ultrasonic signal and the RF received. Figure 2 shows the estimation method of the angle by TDOA/AOA. S denotes the position of reference node while P denotes the unknown node. Supposing the coordinate of S/P in the XOY plane are (xs, ys)/(xu, yu). S can both send and receive distance measuring signals while A can only receive. On the assumption that the length of AS is L, then PA equals PQ almost when d is much longer than L. On this condition, the length of SQ equals d − d1, then comes the formula:

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Fig. 2. The TDOA/AOA algorithm

d d1 ¼ L cos h h ¼ arccos

ð14Þ

d d1 L

ð15Þ

In this formula, d and d1 can be ﬁgured out by the TDOA method, and the L is known. The h may be blurred as it could be [0, p] as well as [−p, 0], and this problem can be solved by apriority.

3 Localization Scenario Typical wireless sensor networks consist of a large number of small and battery. Powered nodes with short range radios, low cost processors and speciﬁc sensing functions. WSN is widely applied in the area of military, industry and agriculture. The communication of WSN replies on the wireless network protocol, such as ZigBee, Bluetooth, WiFi, IrDA etc, and the comparison of these protocol is listed in Table 1.

Table 1. The comparison of the common WSN Load of system Battery life Nodes afford Maximum distance Data rate frequency

Bluetooth heavy short 7 10 m 1 Mbps 2.4 GHz

WiFi heaviest shortest 30 100 m 11 Mbps 2.4 GHz

IrDA light long 2 1m 16 Mbps 980 nm

ZigBee lightest longest 255 1–100 m 250 kbps 2.4 GHz

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We can see that ZigBee has the advantage of low power and large scale, and its data rate meets the requirement of localization. The spacecraft is composed of several cabin, inside each cabin the WSN is deployed separately. The WSN is deployed in the way that all the reference nodes are deployed on the same plane whose position are already known, and the ultrasonic and RF receiver are equipped in each reference node. The astronauts wear the unknown node which is equipped with the ultrasonic and RF ejector. The reference node compute the position of the astronaut by receiving the signal ejected by astronaut periodically, and the position information is transmitted to the gateway which is linked to the 1553B bus, then the ground station can acquaint the position of the astronaut (Fig. 3).

Fig. 3. The localization scenario in spacecraft

WSN is deployed in the way below to avoid using the antenna array. The inside of the spacecraft is divided into several plane, and the distance between near plane is set as L. All the reference nodes are deployed in each plane. The amount of reference nodes in each plane must not be less than 3 and all the 3 nodes must not be deployed in one line. Furthermore, the reference node could be mapped directly into the near plane which means it have the same coordinate in each plane (Fig. 4). Number the reference nodes of each plane as chart 2. The localization is divided into two steps. In the ﬁrst step, the astronaut received the information broadcasted by reference node which contains the ID of sender such as A. Then the astronaut choose the reference node B in the near plane which has the same coordinate with A. At last, the z coordinate of astronaut is ﬁgured out by TDOA/AOA method mentioned in Table 2. In the second step, the astronaut project itself onto the plane of reference node, and ﬁgured out the distance between itself and each reference node in the plane. Finally, choosing 3 reference nodes which has the same distance from the projection of the astronaut in the plane xoy, and the trilateration localization tragedy is used to estimate the x and y coordinate of astronaut.

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Fig. 4. The hierarchical deployment of the reference nodes Table 2. The allocation of the node ID Plane 1 1 1 2 2 2

Node A B C D E F

Node coordination x1, y1, z1 x2, y2, z1 x3, y3, z1 x1, y1, z2 x2, y2, z2 x3, y3, z2

Node ID 1 2 3 4 5 6

To improve the precision of the localization, the localization result should be invalid once the condition of L d is not satisﬁed. By judging the time difference of the ultrasonic signal and the RF received (tTDOA), we can tell the distance between the reference node and the unknown node. Once tTDOA < tmin, the localization should be invalid.

4 Conclusion In this paper, a joint TDOA/AOA three-dimensional localization algorithm based on Wireless Sensor Network (WSN) is introduced, which has the advantage of moving anchors free, low nodes density and low complexity. Furthermore, there are some problems should be solved in the future: The multi-range effect should be studied. The performance should be studied with nodes density and power consumption, and a precision model should be set by taking these factors into account.

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References 1. Qiangmao G, Fidan B (2009) Localization algorithms and strategies for wireless sensor networks. In: HerShey information science reference, New York 2. Lu Xiaofeng, Hui Pan, Towsley Don et al (2010) Anti-localization anonymous routing for delay tolerant network. Comput Netw 54(11):1899–1911 3. Niculescu D, Nalh B (2003) Ad hoc positioning system (APS) using AOA. In: Proceedings of the 22nd annual joint conference of the IEEE computer and communications societies (INFOCOM’03). IEEE, New York, pp 1734–l743 4. Patwari N, Hero AO, Perkins M et al (2003) Relative location estimation in wireless sensor networks. IEEE Trans Signal Process 51(8):2137–2148 5. Zhang LQ, Zhou XB, Cheng Q (2006) Landscape-3D: a robust localization scheme for sensor networks over complex 3D terrains. In: Proceedings of the 31st IEEE conference. IEEE, New York, pp 239–246 6. Dai Guilan, Zhao Chongchong, Qiu Yan (2008) A localization scheme based on sphere for wireless sensor network in 3D. Acta Electronica Sinica 36(7):1297–1303 (in Chinese) 7. Liangbin Lü, Yang Cao, Xun Gao et al (2006) Three dimensional localization schemes based on sphere intersections in wireless sensor network. J Beijing Univ Posts Telecommun 29 (z1):48–51 (in Chinese)

A Study on Lunar Surface Environment LongTerm Unmanned Monitoring System by Using Wireless Sensor Network Yin Long(&) and Zhao Cheng Institute of Manned Space System Engineering, China Academy of Space Technology, 100094 Beijing, China [email protected]

Abstract. An idea for lunar surface environment exploration system by using WSN (wireless sensor network) is proposed for long-term unmanned monitoring, and the large temperature difference between day and night, the loose soil structure of lunar surface and the space radiation intensity are considered. The system is composed of WSN, relay satellite of lunar, relay satellite of earth and earth station. An energy-balanced routing protocol is proposed to prolong the network lifetime. The communication protocol stack for lunar surface, lunar relay satellite, earth relay satellite and earth station is designed. The earth-moon communication technique based on relay satellite is proposed to guarantee realtime data transmission. Compared with the traditional technique, the idea proposed in this paper has advantages as: more detecting objects, larger detection range, longer detection time, higher reliability and lower costs. Keywords: Lunar surface environment long-term unmanned monitoring system Wireless sensor network Node Protocol stack

1 Introduction Lunar exploration has important strategic signiﬁcance, the current major international space power and organizations will be the moon exploration as a starting point for deep space exploration, have launched a series of lunar activities. Human beings on the moon more than 50 years of scientiﬁc exploration, detection methods from the previous flying around the moon, hard landing development for the subsequent soft landing, moon lunar rover and astronauts ﬁeld trips. Detection technology developed from the previous visible light, infrared development for the current full-month microwave detection [1, 2]. The detection covers the data processing and mapping of the lunar image map, the use of hyperspectral remote sensing, radar remote sensing means to detect lunar minerals, launch lunar rover to achieve a soft landing [3–5], access to live images and lunar sampling. The above means to obtain a certain degree of information on the moon, for the human understanding of the moon provides an important reference, but there are their own shortcomings. Through the visible light, infrared, microwave detection of the lunar environment, can not get through the actual © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 110–114, 2020 https://doi.org/10.1007/978-981-13-9409-6_14

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sample parameters at close range, the observation error. Through the lunar landing method, the detection range is limited, the terrain is limited, the detection parameters are very difﬁcult to return, and the risk of single point of failure is higher. Through the astronauts site visits, in the moon to adapt to the environment difﬁcult, detection time and space are limited, the cost and risk are high. This paper presents a lunar surface-based wireless sensor network [6–9], deployed by the lunar landing device, to achieve long-term unmanned monitoring of the moon. And collects the monitoring data of all the sensor nodes through the gateway node and carries on the data fusion, ﬁnally sends the fusion result to the lunar lander and returns to the earth.

2 Design of Lunar Surface Environment Detection System 2.1

The Impact of the Lunar Environment

The lunar surface of the lunar surface with outstanding adhesion, abrasive and permeability, may cause the sensor node buried, affecting the sensor node solar power and communication work. The day and night of the moon are 14 days and a half earth day, one moon day equals one day Earth day. Lunar surface temperature difference between day and night, requiring wireless sensor nodes to adapt to high and low temperature working environment. Lunar surface space radiation is serious, we must improve the wireless sensor node radiation resistance. The surface of the moon’s atmospheric pressure is very small, is 10–14 order of magnitude, is super-vacuum, will make the sensor node structure by 0.1 MPa additional internal pressure. Sensor nodes work on the ground in general use of battery-powered, through the replacement of the battery to achieve its continued work. As the node is difﬁcult to reach the lunar surface, we must design a new type of unattended power supply means to achieve long-term wireless sensor network monitoring. 2.2

System Design

This paper presents a wireless sensor network using a monthly environment detection system (Fig. 1), can be achieved on the lunar environment real-time long-term unmanned monitoring. A large number of wireless sensor nodes (including ordinary nodes and cluster head nodes, yellow nodes and green nodes in Fig. 1) are randomly distributed through the lunar lander on the lunar surface. The nodes are formed by selforganizing networks and are conﬁgured according to the node The sensor real-time monitors the lunar environment parameters and passes the monitoring results to the lunar lander (sink node) in multi-hop mode. Ordinary nodes are used for data acquisition and sending, and cluster head nodes are only used for data forwarding. The common node and cluster head node conﬁguration, cluster head node through the dynamic election, from the ordinary node. When the sensor nodes on the surface of the

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moon form a larger sensor network, the sensing data often need to be forwarded by multiple cluster head nodes to reach the lunar lander. The lunar lander data fusion of the received data and sends the processed data over the wireless link to the lunar relay satellite. The lunar relay satellite forwards the data to the Earth relay satellite, which relays the data to the ground receiving equipment of the earth for real-time environmental parameters of the lunar surface. Similarly, the ground sends control information to the lunar wireless sensor network over the reverse link. The lunar lander’s hardware and software resources are rich, data processing and communication ability, so it can be used as a gateway between wireless sensor network and lunar relay satellite. Lunar relay satellites and earth relay satellites have the advantages of high coverage, high communication link bandwidth, and therefore can be used as a means of communication. The lunar surface based wireless sensor network uses IEEE802.15.4 communication protocol, according to the integration of space and ground design ideas, lunar lander, lunar relay satellite, earth relay satellite and ground receiving equipment adopt IP over CCSDS protocols. Ground receiving equipment and terrestrial network between the use of TCP/IP protocol. To achieve this article on the monthly wireless sensor network, to solve three difﬁculties, including: (1) designing a node of a wireless sensor network adapted to the lunar surface environment; (2) design a kind of energy balance of low-power wireless sensor network networking method; (3) Design a

Fig. 1. Architecture of wireless sensor network

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protocol stack for a wireless sensor network that supports Lunar-to-Earth space communications. Figure 2, the general node of the protocol stack from top to bottom, including application framework layer, application support layer, network layer, data link layer and physical layer, which applies the framework layer, the application support layer And the network layer conforms to the deﬁnition of the ZigBee speciﬁcation, the data link layer and the physical layer conform to the IEEE802.15.4 standard. The protocol

Fig. 2. Protocol of wireless sensor network

stack of the cluster head node only includes the network layer, the data link layer and the physical layer, and conforms to the standard and the common node. 2.3

Energy Balance Routing Technology

As the energy of wireless sensor network nodes is limited, in order to ensure the longterm stability of the entire network, we need to adopt a kind of energy balance routing technology to enhance the entire network life cycle. When the wireless sensor network is large, the data collected by a node in the network must be forwarded through the remaining nodes to reach the sink node. The node that implements the forwarding function is called the cluster head node, and the energy consumption of the cluster head node is usually large. Therefore, the cluster head node must be rotated periodically to avoid the paralysis of a ﬁxed node. By using the periodic node residual energy assessment, the cluster nodes are dynamically selected to realize the energy balance of the whole network and improve the life cycle and robustness of the wireless sensor network.

3 Feasibility and Advantage Analysis Based on the wireless sensor network, the detection method of the lunar surface environment, from the detection distance, the detection object, the detection range, the detection time, the system reliability, the cost and so on, than the traditional hyperspectral remote sensing, radar remote sensing, lunar rover, Astronauts inspection and other means of detection has advantages, as shown in Table 1.

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Range Object Terrain Lifetime Reliability Cost

Radar remote sensing Long Single Unlimited Mean Single High

Lunar rover Close Single Limited Short Single High

Astronauts Close Single Limited Short Single High

WSN Close Various Unlimited Long Redundance Low

4 Concluding Remarks According to the analysis, this idea has the advantages of high detection distance, wide detection range, long detection time, high system reliability and low cost, compared with the traditional lunar environment detection technology. Activities for reference. This article only as a system design, put forward the possible system architecture and some key support technology program. In the future, we need to carry out detailed design and demonstration, especially in combination with the application of microelectromechanical technology and advanced radio frequency technology, to demonstrate the speciﬁc volume and power consumption, how to adapt to the requirements of delivery and spreading, possible wireless communication Ability to meet the requirements and so on, in order to complete the feasibility study feasibility study.

References 1. Wei Z, Yang L, Xin R et al (2012) Design and implementation of three-dimensional visualization of the moon based on Chang’E-1 data of CCD camera and laser altimeter. J Comput-Aided Des Comput Graph 24(1):37–42, 49 (in Chinese) 2. Li Yun, Jiang Jingshan, Wang Zhenzhan et al (2013) Lunar surface physical temperature retrieved from the measurements by CE-1 lunar microwave sounder. Eng Sci 15(7):106–112 (in Chinese) 3. Elphie RC (1998) Lunar Fe and Ti abundance comparison of lunar prospector and Clementine data. Science 281:1493–1500 4. Meditch JS (1964) On the problem of optimal thrust programming for a lunar soft landing. IEEE Trans Autom Contr 4:477–484 5. Shu JR, Saw AL (2002) Obstacle detection and avoidance for landing on lunar surface. Avoid Astronaut Sci 110(2):35–45 6. Feng H, Chu H-W, Jin Z-K et al (2010) Review of recent progress on wireless sensor network applications. J Comput Res Dev 47(zl):81–87 (in Chinese) 7. Renfa Li, Ye Wei, Fubin Hua et al (2008) A review of middleware for wireless sensor networks. J Comput Res Dev 45(3):383–391 (in Chinese) 8. Sensor Webs of SmartDust: Distributed signal processing/data fusion/inferencing in large microsensor arrays 9. Miu Shifu, Liang Huawei, Meng Qing et al (2007) Design of wireless sensor networks nodes under lunar environment. Trans Microsys Technol 26(8):117–120 (in Chinese)

A Study on Automatic Power Control Method Applied in Astronaut Extravehicular Activity Yin Long(&), Pei Guo, and Yusheng Yi Institute of Manned Space System Engineering, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. The space station mission faces the data interaction requirements between the space station and multiple extravehicular astronauts. The traditional wireless communication mode with constant transmitting power will cause the interference and incompatibility of communication due to the different positions of the extravehicular astronauts. In order to ensure the communication link stability of all extravehicular astronauts, an automatic power control method is proposed. The extravehicular communication device located in the space station receives the real-time data of all extravehicular astronauts, and the signal to noise ratio is estimated. According to the evaluation results, the power is automatically controlled by the two ways of outer loop and inner loop. Finally, the signal to noise ratio of all the astronauts received by the extravehicular communication device is the same, ensuring the quality of extravehicular communication. The method is veriﬁed by building the testbed and carrying out experiment, and the result shows that the multiple signal to noise ratio received is almost the same, and the reliability for multiple extravehicular activity is improved. Keywords: Space station Communication system of astronaut extravehicular activity Reversed signal Automatic power control

1 Introduction With the vigorous development of manned space missions, EVA (extravehicular activity) became its feature and identiﬁcation. Communication technology is the key of EVA as it supports the communication between the astronauts and the spacecraft or space station. There are only a few countries such as USA, Russia and China that can carry out EVA. TDMA is adopted by the international space station to solve the problems mentioned above, but it has the disadvantages of low efﬁciency and more power consume [1]. Shenzhou-7 task completed the ﬁrst EVA of China [2–6], and the EVA system supports the transmission of telemetry and voice. The communication equipment of EVA worn by the astronaut has a constant unaltered transmission power which can only support the point to point communication. The space to space communication system which support transmitting data between two spacecraft also lack © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 115–120, 2020 https://doi.org/10.1007/978-981-13-9409-6_15

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the ability of automatic power control [7, 8]. The space station mission faces the requirement of multi-astronauts EVA, and the area of the EVA is increased. By the above means, the signal to interference ratio of all the EVA communication link will be different when the multi-astronauts move to different positions. As a result, the receiving and demodulation of the signals are effected. In order to improve the quality of the communication of multi-astronauts EVA, a method based on automatic power control is proposed to ensure the same signal to interference ratio of all the links.

2 Communication System of EVA The communication system of EVA is composed of EVA communication equipment, EVA communication antenna, space-suit antenna and space-suit communication equipment. EVA communication equipment is installed in the space station while the EVA communication antenna is ﬁxed on the shell. Space-suit antenna and space-suit communication equipment are both built outside and inside of the space-suit worn by the astronaut. EVA communication equipment plays a key role in the system, and it is single conﬁgured. The amount of space-suit antenna and space-suit communication equipment are conﬁgured due to the task, which normally counts from 1 to 3. For example, the communication system of EVA for 3 astronauts is shown in Fig. 1. The bi-directional communication link between astronauts and space station is established through the system, and the information such as telemetry and voice are transferred. space-suit antenna a

EVA communication antenna

space-suit communication equipment a

space-suit antenna b

space-suit antenna b

EVA communication equipment

space-suit communication equipment b

space-suit communication equipment c

Fig. 1. Communication system for astronaut extravehicular activity

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The UHF band is used by the communication system of EVA. The forward link is speciﬁed as EVA communication equipment to space-suit communication equipment, while the backward link is deﬁned as space-suit communication equipment to EVA communication equipment. FDD (Frequency Division Duplex) and CDMA (Code Division Multiple Access) is adopted to realize the bi-directional communication and 1 to multi communication. QPSK method is applied for the modulation while convolution is adopted for the coding. The system can support 3 astronauts carrying out EVA at least, and error rate of the EVA communication is below 10–5.

3 Automatic Power Control Method for the Backward Signal In order to cover the full scope of EVA wirelessly and ensure the quality of EVA communication, the transmission power of backward signal should be controlled so that the near-far effect is suppressed and the interference is reduced. The power of backward link is controlled by the order transferred from EVA communication equipment to space-suit communication equipment, and the communication link remains consecutive during the power adjustment. EVA communication equipment generates a signal with constant power Pt, while the transmitting power of space-suit communication equipment is adjusted by EVA communication equipment’s order.

Fig. 2. Flow of reversed signal automatic power control

According to the synchronization of the backward link, the open-loop and closed-loop power control method is applied respectively, which is shown in Fig. 2.

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Open-Loop Automatic Power Control

The forward and backward links are both in synchronization process as soon as spacesuit communication equipment turned on. EVA communication equipment cannot control the transmitting power of space-suit communication equipment until the synchronization is achieved. Therefore, space-suit communication equipment can only initialize the transmitting power referred to the forward power received as the quality of backward link is unjustiﬁable. The power control worked in open-loop mode. After the forward synchronization is completed, space-suit communication equipment calculates the received power Pr according to the AGC. The loss of power through forward-link Ploss is calculated by Eq. 1. Ploss ¼ Pt Pr

ð1Þ

The expected power received by EVA communication equipment is speciﬁed as Pexpect, and it is computed by Eq. 2. Pmin and Pmax represent the minimum and max power received respectively. Pmin Pexpect Pmax

ð2Þ

According to Pexpect, the initial transmitting power of space-suit communication equipment is minimized to gain the minimum interference on other EVA. The initial transmitting power of space-suit communication equipment is speciﬁed as Ptt, and can be calculated through Eq. 3. Psurplus is used to reduce the impact of other EVA. Ptt ¼ Ploss þ Pexpect Psurplus

ð3Þ

After the initialization, space-suit communication equipment conﬁrm whether the backward link is established by the ACK from EVA communication equipment. The transmitting power is added by ΔP1 each time until the ACK is received. Close-loop mode is altered until the forward and backward synchronization is ﬁnished. 3.2

Closed-Loop Automatic Power Control

After synchronization, data is transferred bi-directionally, and the power control method works in closed-loop. Closed-loop power control method is divided into outerloop mode and inner-loop mode. Outer-loop mode is adapted to get the minimum signal to interference ratio (SIR) which can maintain the link. Inner-loop mode is used to make sure SIR of all EVA received by EVA communication equipment is almost the same. 3.2.1 Outer-Loop Automatic Power Control SIR speciﬁed as St which is supposed to maintain the communication link of EVA is calculated according to the bit error rate (BER) of EVA communication equipment. As the environment is stable, the data rate of EVA communication equipment is invariable. The period of outer-loop automatic power control is set as multiple of TTTI

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Fig. 3. Outer-loop procedure for power control

(Transmission Timing Interval). St is obtained every period and passed to the module of inner-loop automatic power control. The period is set as 1 s since the environment of channel is stable. St is set according to BER calculated in one period. The flow of outer-loop is shown as Fig. 3

Fig. 4. Frame format for physical layer

means the time of outer-loop. St is calculated periodically and the transmitting power of space-suit communication equipment is adjusted accordingly. 3.2.2 Inner-Loop Automatic Power Control Inner-loop automatic power control means that the power is controlled periodically according to the difference from the actually measured SIR called Sa and St. The control command speciﬁed as TPC is set in the physical layer frame which is shown as Fig. 4. The content of frame includes business data, pilot and TPC. TPC occupies 2 bits that 00 means increasing power, 01 stands for decreasing power, 11 means maintenance while 10 is reserved. Inner-loop automatic power control is carried out as follows. If Sa > St, TPC is set as 01. The transmitting power of space-suit communication equipment is expected to be reduced in one period. If Sa < St, TPC is set as 00. The transmitting power of space-suit communication equipment is expected to be increased in one period. If Sa = St, TPC is set as 11. The transmitting power of space-suit communication equipment is expected to be maintained. TPC is acquired when the frame is received and analyzed. The transmitting power of space-suit communication equipment is adjusted step-by-step according to TPC. The current power is speciﬁed as P(n), while the previous one is speciﬁed as P(n − 1). The equation below shows the relationship.

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PðnÞ ¼ Pðn 1Þ þ DP2 Tpc

ð4Þ

ΔP2 stands for the stepping value adjusted each time, and TPC means the coefﬁcient for TPC. TPC = 1 when TPC equals 00. TPC = −1 when TPC equals 01. TPC = 0 when TPC equals 11. The near-far effect is reduced by the alteration of the power and assimilation of SIR.

4 Conclusion According to the demand of multi-person EVA, the automatic power control method is proposed. At ﬁrst, the initial power is set in open-loop mode when the backward link is not synchronized. The closed-loop mode works since the forward and backward link is set up. St is acquired in outer-loop mode which ensure the maintenance of EVA communication. The power is controlled step-by-step through closed-loop to reduce the interference and near-far effect. The experiment only considered the free space fading, while the near-ﬁeld effect and the shell of spacecraft is ignored. Further study should be carried out.

References 1. Yutao Hao, Liu Baoguo, Wang Ruijun et al (2014) Research on TT&C system in international space station. Manned Spaceflight 20(2):165–172 (in Chinese) 2. Zhi S, Bainan Z, Teng P et al (2009) Research and development of Shenzhou-7. Manned Spaceflight 15(2):16–21, 48 (in Chinese) 3. Chen Jindun, Liu Weibo, Chen Shanguang (2009) The system design and flight application of astronaut EVA in Shenzhou VII mission. Manned Spaceflight 15(2):1–9 4. Xiao Yu, Ma Xiaobing, Zhongqiu Gou (2010) Failure mode and countermeasure design and implement for Shenzhou spaceship’s extravehicular activity. Spacecraft Eng 19(6):56–60 (in Chinese) 5. Zhihao Pang (2008) Development of technologies of extravehicular activities. Sci Technol Rev 26(20):21–27 (in Chinese) 6. Zhu Guangchen, Shijin Jia (2009) The ground veriﬁcation of spacecraft EVA functions. Manned Spaceflight 15(3):48–53 (in Chinese) 7. Shi Yunchi (2011) Space to space communication subsystem manned spaceflight and its key technology. Aerosp Shanghai 28(6):38–42 (in Chinese) 8. Cheng Qinglin, Liang Hong, Wu Yijie et al (2014) The design and implementation of multimode receiver for rendezvous and docking in space. Manned Spaceflight 20(1):58–64 (in Chinese)

Design of EVA Communications Method for Anti-multipath and Full-Range Coverage Yin Long(&), Kewu Huang, and Xin Qi Institute of Manned Space System Engineering, China Academy of Space Technology, 100094 Beijing, China [email protected]

Abstract. Considering the large-scale of manned spacecraft and the increasing scope of EVA, a full-range and anti-multipath communications method for EVA is proposed to solve the problem of low coverage and severe multipath effect which cannot be solved by traditional method. Multiple antennas are evenly distributed around the manned spacecraft to ensure the full communication coverage of EVA. FDD (Frequency Division Dual) is adopted and different frequency is assigned to the forward link and backward link respectively. DSCDMA (Direct-Sequence Code Division Multiple Access) is applied. Diverse spreading codes are distributed to each astronaut of EVA, and the problem of EVA communication interference for multiple astronauts is solved. In order to weaken the multipath effect brought by shield and reflection of manned spacecraft, a communication method by combination is proposed. Time diversity technique is applied that manned spacecraft transmits the forward message through multiple antennas in time staggered mode, and the astronaut of EVA is searching the maximum point in limited time by correlation of sliding window. The rest peaks are found near the original one, and the maximum ratio combining is carried out by the judge of peak value. Space diversity technique is also used that manned spacecraft receives the backward information of astronauts by multiple antennas, and all the peaks are found by the correlation through sliding window. The maximum ratio combining is implemented by the estimation. Simulation is made, and the result shows that by whole-scope communications method for EVA, the signal to noise ratio can be reduced 1–4 dB to realize the BER (Bit Error Rate) of 10–5 comparing with other methods, and it realize the full-range of EVA communication without interruption. Keywords: Full-range EVA Extravehicular communication Multipath effect Time diversity Space diversity

CDMA

1 Introduction As the fast development of manned space technology, EVA has become the key technology for human being’s exploring the space. Communication methods which realize the information exchange between the extravehicular astronaut and the

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spacecraft has become the signiﬁcant part of EVA. Since the quick development of space technique as well as the growing complexity of space missions, the future missions including space station and manned lunar landing have the fundamental requirements of EVA. Considering the demand of multiple extravehicular astronauts, increasing scope of activity, growing bandwidth as well as the multipath effect causing by the shielding, reflecting from the shell of large-scale spacecraft. At present, only a few countries such as USA, Russia and China command the communications technique of EVA all around the world. TDMA has the beneﬁts of anti-multipath and high rate which was adopted by international space station [1], but it has the disadvantage of high power consume and low efﬁciency. Shen zhou-7 [2–6] realizes communication of single astronaut EVA by two ways which are communications method by umbilical cord and wireless communications method based on FDMA. umbilical cord based method has the advantage of high credibility as well as kind performance of antiinterference, but it is not ﬁt for the wide range of EVA as it is length limited. FDMA communications method has a well performance of credibility, but it is only used for single astronaut’s EVA once the frequency is restricted. In order to meet the needs of future EVA, an anti-multipath communications method is proposed to solve the problem of multipath effect, single astronaut EVA supported and low communication efﬁciency, and the range of EVA is extended remarkably both in angle and distance.

2 EVA Communications Method EVA communications system is composed of EVA communication equipment, EVA communication antenna, space-suit antenna and space-suit communication equipment. EVA communication equipment is installed in the space station while the EVA communication antenna is ﬁxed on the shell. Space-suit antenna and space-suit communication equipment are respectively built outside and inside of the space-suit worn by the astronaut. EVA communication equipment plays a key role in the system, and it is often single deployed. The amount of space-suit antenna and space-suit communication equipment are conﬁgured due to the task, which normally counts from 1 to 3. Multiple antennas method realized EVA communication through multiple antennas, and DS-CDMA is often applied. By multiple antennas, the angle range is remarkably improved which can cover 360° easily. The EVA communication method proposed is processed in the following steps and is shown in Fig. 1. Firstly, the system conﬁguration is executed which includes the deployment of EVA communication antenna as well as DS-CDMA initialization. Then, the forward and the backward link are built up synchronously. The forward link applied time diversity technique while the backward uses space diversity technique.

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Fig. 1. Flow of EVA communications method

2.1

Design of Antenna Array

EVA communication cannot be established between single antenna and the astronaut behind considering the shield of spacecraft and limitation of antenna pattern. EVA communication cannot cover 360° around the spacecraft by the single antenna. In order to improve the coverage of EVA communication, antenna array is designed. k antennas are equably deployed around spacecraft and the angel of two closer antennas equals 360°/k. Every antenna is connected to EVA communication equipment by cables and each antenna realizes bi-directional communication. Antenna array realized the coverage of 360° and the EVA range is improved. EVA communication system is shown in Fig. 2.

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Fig. 2. EVA communication system

2.2

DS-CDMA

Future space missions require multiple astronauts carrying out extravehicular activity. DS-CDMA [7] is used to share the time as well as frequency, and the need of multiple astronauts executing EVA is meet. Taking 3 astronauts as example, the forward link is using 3 spreading codes to identify each 3 astronaut’s communication with the spacecraft. The backward link is also using 3 spreading codes to realize all the communications simultaneously. In all, 6 spreading codes are applied. The physical layer frame is shown in Fig. 3 which includes the frame before convolution, the frame after convolution and the frame after spreading.

Fig. 3. Frame format for system

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Time Diversity

Consuming all the forward signals are emitting simultaneously, the phase of each signal received will be various as the signal way differs and it results in the depressed performance of receiving. The signal channels can be distinguished by different spreading codes to avoid the non-positive phase combination. The space-suit communication equipment must be able to receive and demodulate k channels of signal which complicate EVA communication system observably. By spreading, the chips are non-correlative between each other which means that the decline of spreading signals with ﬁxed chips delayed are not interrelated, and time diversity [8, 9] technique is based on the characteristic. EVA communication equipment transmits signals in time-staggered mode and the process is shown in Fig. 4. The spreading signal is transmitted through k channels by k antennas, and the interval between each closer channel is n chips. The longest interval among k channels is (k − 1) * n chips. Space-suit communication equipment receives the k channel signals and ﬁnds the peak value by sliding window correlation. The correlation length is set as integer multiple of frame length L as bL, and the peak value a1 is found by sliding window correlation. The other k − 1 peak values speciﬁed as a2, a3, …, ak are found around peak a1 within (k − 1) * n chips. The amounts of signals to be combined are determined by whether the peak value exceeds the bound, and by the combination of signals, the forward link turns to be much steadier.

Fig. 4. The technology of time diversity

2.4

Space Diversity

EVA communication equipment receives the backward signal by multiple antennas. As the fading characteristics of backward signals are independent with each other, combination of multiple signals is feasible by the space diversity technique. The spreading codes of all the space-suit communication equipment are previously stored in EVA

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communication equipment, and all the backward signals are respectively correlated by sliding window to get the peak value. The steps are taken as follows and shown in Fig. 5. Firstly, the backward signals are correlated in limited length by sliding window. Secondly, the peak value is acquired in designated time. Finally, all the peak values are estimated and chosen to be combined [10].

Fig. 5. The technology of space diversity

3 Conclusion This paper proposed a EVA communications method which has the advantage of full range coverage and anti-multipath. Multiple antennas supporting both emitting and receiving are equably deployed around the shell of spacecraft and covers 360°. Time diversity is applied in the forward link while the space diversity is used in the backward link. By combination of multiple signals, the performance of EVA communication is remarkably improved. Comparing with traditional methods, the method proposed in this paper get a better coverage, higher efﬁciency and better signal to noise ratio. Multipath effect is weakened and the method can be used in the future EVA communication.

References 1. Yutao Hao, Baoguo Liu, Wang Ruijun et al (2014) Research on TT&C system in international space station. Manned Spaceflight 20(2):165–172 (in Chinese) 2. Zhi S, Bainan Z, Teng P et al. (2009) Research and development of Shenzhou-7. Manned Spaceflight 15(2):16–21, 48 (in Chinese) 3. Chen Jindun, Liu Weibo, Chen Shanguang (2009) The system design and flight application of astronaut EVA in Shenzhou VII mission. Manned Spaceflight 15(2):1–9 (in Chinese) 4. Xiao Yu, Ma Xiaobing, Zhongqiu Gou (2010) Failure mode and countermeasure design and implement for Shenzhou spaceship’s extravehicular activity. Spacecraft Eng 19(6):56–60 (in Chinese)

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5. Zhihao Pang (2008) Development of technologies of extravehicular activities. Sci Technol Rev 26(20):21–27 (in Chinese) 6. Guangchen Zhu, Shijin Jia (2009) The ground veriﬁcation of spacecraft EVA functions. Manned Spaceflight 15(3):48–53 (in Chinese) 7. Zhou Geqiang, Xuan Yong, Zou Yongzhong (2010) Application analysis on the novel CDMA technology in extravehicular communication. Manned Spaceflight 3:14–18 8. Guodong Zhao, Xiaoting Chen, Liu Huijie et al (2009) Channel model of LEO satellite and high resolution rake receiver. Aerosp Shanghai 26(5):52–55 9. Li Miao, Lv Shanwei, Zhang Jianglin et al (2004) A novel multistage blind space-time multiple receiver for DS/CDMA. Acta Electronica Sinica 32(9):1553–1555 10. Zhang Lin, Qin Jiayin (2007) New efﬁcient methods for performance analysis of maximal ratio combining diversity receivers. Chin J Radio Sci 22(2):347–350

High Accurate and Efﬁcient Image Retrieval Method Using Semantics for Visual Indoor Positioning Jin Dai1, Lin Ma1(&)

, Danyang Qin2

, and XueZhi Tan1

1

2

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, People’s Republic of China [email protected] Electronic Engineering College, Heilongjiang University, Harbin 150080, People’s Republic of China

Abstract. Visual indoor positioning has a wide application because of its good positioning performance without additional hardware requirement. However, as the indoor scenes and complexity increase, the offline database will inevitably become large and the online retrieval time will also become long, which make visual indoor positioning unpractical. To solve this problem, we propose a Semantic and Content-Based Image Retrieval (SCBIR) method. By dividing the offline database into semantic databases with different semantic types, the retrieval scope of the image is reduced, and the retrieval time is reduced. First, we use the semantic segmentation method to detect the semantics. Then we divide different semantic scenes in terms of the image order and basic pattern of the semantics in the scene. Finally we use the images belonging to each different semantic scene to build a semantic database, so as to achieve online accurate and fast image retrieval. The experiment results indicate that the proposed method is suitable for large scale retrieval database, and it can reduce the retrieval time in the online stage on the premise of ensuring the accuracy of image retrieval that is critical for visual indoor positioning. Keywords: Visual positioning Database classiﬁcation

Image retrieval Semantic database

1 Introduction Nowadays, location based service (LBS) receives extensive attention with the rapid development of smart device [1]. The method of visual indoor positioning is highlighted by its unique advantages because it does not need additional hardware installation to complete image acquisition and positioning [2]. Visual indoor positioning is the most prominent method for future indoor positioning and navigation services. At present, indoor positioning system classiﬁcation based on vision has two stages: offline stage and online stage. The offline stage is a process of data acquisition and establishment of an offline database. The online stage is an image matching process for position estimation by retrieving the online query image within the images in the offline database. At present, there are mainly two ways to reduce the online retrieval time. One © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 128–136, 2020 https://doi.org/10.1007/978-981-13-9409-6_17

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way is to ﬁnd new image feature extraction method with lower complexity and more accurate classiﬁcation. The other way is to classify the offline database, so as to reduce the search area and speed up the image retrieval. For the former one, many researches have been made on the feature extraction in low-complexity. In [3–5], Content-based Image Retrieval (CBIR) method was used for retrieval in large storage of medical images, street grafﬁti images and satellite remote sensing images. For the latter one, the offline database classiﬁcation is analyzed to reduce the search scope while ensuring the accuracy at the same time. Kido et al. [6] used a convolutional neural network and regional convolutional neural network to classify pulmonary diseases and improved the performance of detailed classiﬁcation. In [7], images in the database were roughly divided into four categories by clustering method, and then accurately retrieved from each category. However, the clustering method requires a lot of tests to determine the optimal clustering category, which leads to poor retrieval accuracy. To sum up, the performance of the above methods either focuses on the retrieval accuracy but fail to provide the retrieval efﬁciency or improve the real-time performance but fail to offer an accurate retrieval. Therefore, in view of the above problems, this paper improves the CBIR method and proposes a Semantic and Content-Based Image Retrieval (SCBIR) method. It succeeds to make up for the problem of CBIR poor retrieval efﬁciency due to accuracy requirement. It can not only ensure the number of classiﬁcation categories but also accurately classify multiple landmarks in a single image. In addition, the pixel positions of various semantics in the image can be clearly obtained, which can be applied in more scenes according to requirements.

2 System Model 2.1

Visual Indoor Positioning System Overview

A typical visual indoor positioning system has two stages: offline stage and online stage. The main task of the offline stage is data acquisition, which is to provide the required database for the online stage. The main task of the online stage is to provide users with location services, complete the image user provided retrieval and the positioning. The main workflow is shown in Fig. 1. Offline Stage

Database Construction

User Image

Feature Extraction

Feature Extraction

Image Retrieval

Image Capture

Position Estimation Online Stage

Fig. 1. Flow chart of visual indoor positioning system

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In the offline stage, the image acquisition is carried out, and the images with their associated positions are modeled into a database. Then feature extraction (or image classiﬁcation) is carried out for images in database to form a new feature database or sub-database, which is convenient for efﬁcient and rapid retrieval in the online stage. In the traditional visual indoor positioning system, in order to obtain a higher positioning accuracy, a large number of image data needs to be collected. For the feature extraction method, the online retrieval time will also be increased exponentially, which will affect the real-time online positioning. For the database classiﬁcation method, each sub-database is large that it cannot achieve to reduce the retrieval time. To solve this problem, this paper proposes an image retrieval method based on semantics and content, which not only improves the accuracy of classiﬁcation but also increases the number of semantic databases when the database becomes large, so that the number of images in the semantic database will decrease accordingly. 2.2

SCBIR Method Overview

In this paper, we propose a SCBIR method and the frame is shown in Fig. 2. Sematic Database Semantic Database 1

Semantic Label Classification

Semantic Segmentation Module

Semantic Label Classification

Semantic Database i ...

User Image

Semantic Segmentation Module

...

Image Database

Semantic Database N

Precise Retrieval

Match Image

Fig. 2. Frame of SCBIR method

As can be seen from Fig. 2, the core step of the method is the process of classifying the offline database into semantic databases, which can greatly reduce the retrieval range of the traditional retrieval algorithm and reduce the retrieval time. In the semantic database, high complexity and high precision method can be used to retrieve the results most consistent with the input image to be retrieved. The precision retrieval process of the second step is premised on accuracy. It is assumed that the semantics contained in the positioning environment has c class (excluding the background class), and the total semantic library is deﬁned as S ¼ ½S1 ; S2 ; . . .; Sc , where Si ði 2 cÞ is the corresponding semantics. For each image in the image database, the contents may contain multiple semantics, so the combination of different semantics is required for the classiﬁcation of each image. According to the permutation and combination, the number of semantic combination types can be obtained as N ¼ 2c . We classify the images with the same semantic combination into one category, and ﬁnally get the semantic database DS ¼ ½D1S ; D2S ; . . .; DNS . DiS ði 2 NÞ is the semantic sub-database, which is formed by centralizing images with the same semantics after semantic discrimination of the offline image database.

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3 Proposed Method 3.1

Semantic Segmentation Network Framework

On the basis of Sect. 2, we will analyze the semantic segmentation framework in detail in this section. In the semantic segmentation, we mainly involve the region-based full convolution network in applied machine learning. The main flow diagram is shown in Fig. 3. Semantic Discriminant

Image i

FCN

Feature Map

CONV Layer

RPN PositionSensitive Score Map

ROI

PositionSensitive Pooling Layer

Classify

Semantic Labels i

ROI Sub-network

Fig. 3. Flow chart of R-FCN framework

According to Fig. 3, the R-FCN Network is composed of the FCN (Fully Convolutional Network), RPN (Region Proposal Network), and ROI sub-network. In RPN, because the input image contains location information and category information of various semantics, the overlap rate of both ground truth and ROI need to be calculated to judge the location of the real semantics. This overlapping rate is deﬁned as Intersection over Union, which is a standard to measure the accuracy of detecting the object. It is often evaluated by Jaccard coefﬁcient: JðA; BÞ ¼

jA \ Bj jA \ Bj ¼ ; jA [ Bj j Aj þ jBj jA \ Bj

ð1Þ

where A and B respectively represent the predicted range and the real range. 3.2

Precise Semantic Segmentation

In the ROI sub-network, the convolution operation is also carried out on the feature map output by the FCN [8]. The ROI sub-network uses the convolution operation to generate k position-sensitive score graphs for each category on the entire image. The value on each position-sensitive score map represents the score of the category at that position in the space. For a region proposal box of R S size obtained by RPN, the box can be divided into k k sub-regions, and the size of each sub-region is R S=k 2 . Because too much data will interfere with the subsequent classiﬁcation operations, it is necessary to compress the data with pooling operations. For any sub-region binði; jÞ, ð0 i; j k 1Þ, deﬁne the position-sensitive pooling operation as:

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rc ði; jjHÞ ¼

X

1 zi;j;c ðx þ x0 ; y þ y0 jHÞ; n ðx;yÞ2binði;jÞ

ð2Þ

where rc ði; jjHÞ is the pooled response of sub-region binði; jÞ to category c, zi;j;c is the position sensitive score map corresponding to sub-region binði; jÞ, ðx0 ; y0 Þ represents the pixel coordinates in the upper left corner of the target candidate box, n is the number of pixels in the sub-region, and H represents all the parameters obtained by network learning. Finally, the mean value of the pooling response output rc ði; jjHÞ of the k k sub-region is calculated, and the ðc þ 1Þ-dimensional feature map output by the ROI pooling layer is summed according to the dimensions to obtain a ðc þ 1Þdimensional vector: X rc ði; jjHÞ: ð3Þ rc ðHÞ ¼ i;j

By plugging this vector into the Softmax formula, we can use the Softmax regression class method to get the probability that the target in the search box belongs to each category and classify it according to the maximum probability: sc ðHÞ ¼ e

rc ðHÞ

, c X

erc0 ðHÞ :

ð4Þ

c0

Each semantic category is accompanied by a four-dimensional vector, denoted fx; y; w; hg, which respectively represents the central abscissa, central ordinate, width and height of the current semantic ROI area. In the network, loss function L is composed of classiﬁcation loss function Lcis and position loss function Lreg : Lðs; tx;y;w;h Þ ¼ Lcis ðsc Þ þ k signðc ÞLreg ðt; t Þ 1 c [ 0 ; signðc Þ¼ 0 else

ð5Þ

where c stands for ground truth, and c ¼ 0 means the classiﬁcation is correct. k represents the balance parameter, and if k ¼ 1, it means that the classiﬁcation loss and the location loss are equally important. t represents the semantic location of prediction, and t represents the location of ground truth. In order to learn the extreme case, our method adopt the OHEM (online hard example mining) [9]. 3.3

Efﬁcient Image Retrieval

The semantic information in the image is bound to the image in the form of the label. For input image Itest , if the image contains semantic components, the semantic label may be Stest ¼ ½S1 ; S2 ; . . .; Sk , where 1 k c. Therefore, we deﬁne a semantic discriminant vector X ¼ ½x1 ; x2 ; ; xc T , where:

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xi ¼ i2c

1 Si 6 ; : 0 else

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ð6Þ

In this paper, the semantic information contained in each image is converted into a corresponding digital label. Deﬁne transformation vector K ¼ ½20 ; 21 ; . . .; 2c to convert multiple semantic labels into unique digital label. l ¼ K X.

ð7Þ

Each image in the offline database is input into the network,, the matching image will be found by applying the appropriate precise retrieval method in DiS .

4 Implementation and Performance Analysis 4.1

Experiment Environment

In order to test the performance of our proposed method and compare it with the existing method, we used the 12th floor of Information Building of Harbin Institute of Technology as the experimental environment. The floor plan is shown in Fig. 4.

Start Point

Image Acquisition Path

Fig. 4. Floor plan for the experimental scene

In this experiment, 0.5 m was taken as the data acquisition interval, and the images were collected in the forward and reverse direction respectively. After many experiments, we set the initial learning rate to 0.01 and the iteration times to 7000. 4.2

Experiment Results

After every image in the offline database is semantically labeled, the images in the offline database can be classiﬁed according to semantic components. The classiﬁcation accuracy is shown in Table 1.

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Semantic categories Door Window Heating Poster Exhibition board Ashbin Fire hydrant Emergency exit Vent

Identify correct 531 148 135 180 314 19 65 61 25

Identify wrong 8 1 3 6 7 3 1 2 1

Identify accuracy (%) 98.52 99.33 97.83 96.77 97.82 86.36 98.48 96.83 96.15

As shown in Table 1, the recognition accuracy of each semantic conforms to the accuracy requirements of offline database classiﬁcation, and the following offline database classiﬁcation algorithm can be carried out when each image is given a correct semantic label. The database classiﬁcation confusion matrix of SCBIR algorithm is shown in Fig. 5. The algorithm in this paper automatically divides the database into 35 categories according to semantic information. Each row in the ﬁgure represents the real category of each image, and each column represents the predicted category of the image after passing through the neural network.

Fig. 5. SCBIR algorithm classiﬁes confusion matrices

As can be seen from Fig. 5, semantic segmentation network classiﬁes images accurately in most offline databases. For a small number of categories, the neural network has the lowest classiﬁcation accuracy of 67%, but the misclassiﬁcation of image database will not have a great impact on the following retrieval work. In this paper, the proposed algorithm is compared with the Mean Average Precision (MAP) of

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the traditional CBIR algorithm [10]. The result is shown in Fig. 6a, and this paper also compared the retrieval time costs of the two algorithms under different database capacities, as shown in Fig. 6b.

(a) MAP of two retrieval algorithms

(b) time cost of two retrieval methods

Fig. 6. MAP and time cost of two retrieval algorithms

In Fig. 6a, the average retrieval accuracy of the SCBIR method proposed in this paper fluctuates around 90% with the increase of retrieval times, while the average retrieval accuracy of the traditional CBIR algorithm fluctuates around 60%. As can be seen from Fig. 6b, when the retrieval database capacity is small, the CBIR algorithm has certain advantages. However, after retrieving more than 100 images in the database, the advantages of SCBIR algorithm are revealed. Therefore, the method proposed in this paper plays an important role in reducing the time cost and improving the retrieval accuracy for large-scale retrieval databases.

5 Conclusion In visual indoor positioning, the image retrieval time within the offline database will increases when the offline database become large, which will affect the real-time performance of online positioning. Therefore, this paper proposes an efﬁcient retrieval method based on semantics and content. Simulation results show this method can not only improve the accuracy of retrieval but also has good real-time performance in large database retrieval. Acknowledgements. This paper is supported by National Natural Science Foundation of China (61971162, 61771186, 41861134010) and Heilongjiang Province Natural Science Foundation (F2016019).

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References 1. Liu RP, Hedley M, Yang X (2013) WLAN location service with TXOP. IEEE Trans Comput 62(3):589–598 2. Yuda M, Xiangjun Z, Weiming S et al (2017) Target accurate positioning based on the point cloud created by stereo vision. In: International conference on mechatronics & machine vision in practice. IEEE 3. Parra A, Zhao B, Kim J, et al (2014) Recognition, segmentation and retrieval of gang grafﬁti images on a mobile device. In: IEEE international conference on technologies for homeland security. IEEE, pp 178–183 4. Bouteldja S, Kourgli A (2015) Multiscale texture features for the retrieval of high resolution satellite images. In: International conference on systems, signals and image processing. IEEE, pp 170–173 5. Pradhan J, Pal AK, Banka H (2017) A prominent object region detection based approach for CBIR application. In: Fourth international conference on parallel. IEEE 6. Kido S, Hirano Y, Hashimoto N (2018) Detection and classiﬁcation of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). In: 2018 international workshop on advanced image technology (IWAIT). IEEE 7. Xue H, Ma L, Tan X (2016) A fast visual map building method using video stream for visual-based indoor localization. In: International wireless communications and mobile computing conference. IEEE, pp 650–654 8. Girshick R (2016) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV). IEEE 9. Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining 10. Vikhar P, Karde P (2017) Improved CBIR system using Edge Histogram Descriptor (EHD) and Support Vector Machine (SVM). In: International conference on ICT in business industry & government. IEEE

Massive MIMO Channel Estimation via Generalized Approximate Message Passing Muye Li1 , Xudong Han1 , Weile Zhang2 , and Shun Zhang1(B) 1 Xidian University, Xi’an 710071, People’s Republic of China {myli 96,xdhan 1}@stu.xidian.edu.cn, [email protected] 2 Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China [email protected]

Abstract. In this paper, we proposed a channel estimation scheme for an oﬀ-grid massive MIMO channel model, with the consideration of carrier frequency oﬀset at the BS antenna array. We ﬁrst developed an oﬀgrid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the oﬀ-grid bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme. Keywords: Massive MIMO · Oﬀ-grid Sparse Bayesian learning · GAMP

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· Carrier frequency oﬀset ·

Introduction

As has been a hot research spot for years, massive multiple-input multiple-output (MIMO) has become a critical technology for the 5th generation (5G) and beyond wireless networks, due to its eﬃcient spectral and energy eﬃciency [1]. However, precise channel state information (CSI) is needed for utilizing the advantages, while it will cause tremendously training and feedback overhead in the frequencydivision duplex (FDD) system [2]. To get over this bottleneck, [3] proposed a low-rank model with the help of antenna array theory, and can implement channel estimation without the acquisition of channel covariance matrices (CCMs). Based on this, several channel estimation schemes were proposed [4,5]. As the channel sparsity was conveyed by utilizing normalized discrete Fourier transform (DFT) basis, it may cause serious spatial sample mismatching as well as energy leakage when considering c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 137–144, 2020 https://doi.org/10.1007/978-981-13-9409-6_18

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the randomness of direction of arrivals (DOAs). Furthermore, as the carrier frequency oﬀset (CFO) exists at the transmitter as well as the receiver [6], the using of existing schemes will also incur some estimation errors. In this paper, we build an oﬀ-grid channel model with the consideration of CFO. Then, an expectation maximization (EM) based sparse Bayesian learning framework was proposed to simultaneously estimate channel model parameters as well as the CFO. In the expectation step, generalized approximate message passing (GAMP) algorithm was introduced to achieve needed posterior statistics and reduce the computation complexity.

2

System Model and Channel Characteristics

Consider a downlink massive MIMO network, where Nt 1 uniform linear array (ULA) antennas are equipped at the BS, and K single-antenna users are randomly distributed in the ﬁeld. We adopt a geometric channel model with L emerging paths to the k-th user. Denote θk,l,m as a direction of departure (DOD) of k-th user, l-th path, m-th block, and the BS antenna array spatial steering vector can be deﬁned as: T 2πd 2πd (1) a(θk,l,m ) = 1, ej λ sin(θk,l,m ) , . . . , ej(Nt −1) λ sin(θk,l,m ) , where d ≤ λ/2 is antenna spacing of the BS; λ is the carrier wavelength. It is assumed that the DOD of each path is quasi-static during a block of Lc and changes from block to block, and the DL channels of diﬀerent users are independent statistically. The downlink channel gk,m ∈ CNt ×1 from the BS to the k-th user during the m-th block can be written as [7] gk,m =

L

αk,l a(θk,l ).

(2)

l=1

As in [8], the VCR can be utilized to dig the sparsity of gk,m as rk,m = FNt gk,m , where rk,m is the downlink virtual channel, and FNt is the Nt × Nt uniﬁed discrete Fourier transformation (DFT) matrix. In real transition process, the DODs would not exactly impinging on the DFT basis, and the direction mismatching emerges. Under such circumstance, deﬁne the bias vector ρk , we derive a bias-added DFT matrix, whose spacial index will be added with ρk , i.e. p∗ = p + [ρk ]p . Furthermore, as scattering rings exist, there may be not only one range of DOD to the BS for a speciﬁc user. Before proceeding, we use A to represent FNt for simplicity, the channel vector gk,m can be derived with the Taylor series expansion as gk,m ]Qk = [Φ(ρk )]H gk,m ]Qk , gk,m = [AH + BH diag(ρk )]:,Qk [˜ :,Qk [˜

(3)

where [BH ]:,p is obtained through taking derivative of [AH ]:,p with respect to p, while every element of ρk is the bias added on the corresponding predeﬁned

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˜k,m grid. Φ(ρk ) is the pre-described bias-added DFT matrix. And the variable g ˜k,m ∼ CN (0, Γk ), where Γk = diag(γ) = is a complex Gaussian Markov vector, g ˜k,m . The spatial signature [9] diag([γ1 , γ2 , . . . , γNt ]) is the covariance matrix of g set is determined as d (4) Qk = pp + ρp = Nt sin(θk,l,m ), p ∈ Z , ρk,l ∈ [−0.5, 0.5]. λ As the DL channel model is constructed, the estimation of the channel is equivalent to learning the model parameters γ and ρ. Moreover, as the characteristics of the model change very slow comparing to the long coherence time block, the parameters can be seen as invariant in the following training phase.

3

Parameters Learning Through Generalized Approximate Message Passing Based EM

Without loss of generality, we assume that Lt symbol-time are utilized in the training phase, and the channel is invariant during a long block. As the transmission of each user is the same, we take one user as an example for the illustration simplicity. Denote S as a uniﬁed Lt × Nt random training matrix with its power σp2 and zero mean, and is known at both BS and the speciﬁc user. The received signal can be written as ˜ + n, y = ESΦ(ρ)H g

(5)

J

denotes the independent additive complex Gaussian where n ∼ CN noise and σn2 is the noise variance, E = diag(1, e1×j2π/Lt , . . . , e(Lt −1)×j2π/Lt ) is the Lt × Lt CFO matrix generated by the timing oﬀset at BS, and the CFO is unknown. Deﬁne the set Ξ = {γ, ρ, }. With the receive signal ym , the aim of model parameters learning is to estimate the accurate Ξ. Thus, we employ an EM based sparse Bayesian learning (SBL) framework to capture the unknown parameters set Ξ, where GAMP is utilized in the expectation step. 0, σn2 INt

3.1

EM-based Sparse Signal Learning

The EM algorithm will produce a sequence of estimated Ξ with the iteration runs, and each iteration is separated into two steps: • Expectation step (E-step) ˆ (l−1) = E ˜ ; Ξ) . Q Ξ, Ξ ˆ (l−1) ln p (y, g ˜ |y;Ξ g

(6)

• Maximization step (M-step)

ˆ (l−1) . Ξ (l) = arg max Q Ξ, Ξ Ξ

(7)

In the l-th iteration, the aim of E-step is to update those objective functions, while the M-step aims to update the estimation Ξ (l) by maximizing the current expectation function [10].

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E-Step

In this subsection, we will ﬁrst carefully derive the objective functions of the parameters to be estimated, while obtain the corresponding posterior statistics by employing GAMP. ˆ (l−1) ) can be derived as: The objective function Q(Ξ, Ξ ˆ (l−1) ) + E ˆ (l−1) ) ˆ (l−1) ) = E ln p(y|˜ g , Ξ ln p(˜ g | Ξ Q(Ξ, Ξ (l−1) (l−1) ˆ ˆ ˜ |y;Ξ ˜ |y;Ξ g g (l−1) ˆ g, Ξ ) + Eg˜ |y;Ξˆ (l−1) {ln p(˜ g|γ)} = Eg˜ |y;Ξˆ (l−1) ln p(y|˜ +Eg˜ |y;Ξˆ (l−1) {ln p(γ)} .

(8)

It is easy to ﬁnd that

ˆ (l−1) ) = CN y; J˜ p(y|˜ g, Ξ g, σn2 INt , p(˜ g|γ

(l−1)

) = CN (˜ g; 0, Γ) .

(9) (10)

Plugging (9) and (10) into (8), we can rewrite the expectation function as follows: H H H ˆ (l−1) ) = C− 1 yH y−E Q(Ξ, Ξ {2{y J˜ g }} + E {˜ g J J˜ g } (l−1) (l−1) ˆ ˆ ˜ |y;Ξ ˜ |y;Ξ g g σn2 ˆ˜ (l) Deﬁne g and

˜ } + ln p(γ). − ln |πΓ| − Eg˜ |y;Ξˆ (l−1) {˜ gH Γ−1 g (11) H ˆ (l−1) , ˆ (l−1) , Θ(l) = E ˜ |y, Ξ ˜ ˜ = Eg˜ |y;Ξˆ (l−1) g |y, Ξ g g (l−1) ˆ ˜ |y;Ξ g

Δ = [1, e1×j2π/N , . . . , e(N −1)×j2π/N ]T , T

Δ1 = [0, 1 × j2π/N, . . . , (N − 1) × j2π/N ] , Δ2 = [0, (1 × j2π/N )2 , . . . , ((N − 1) × j2π/N )2 ]T ,

(12) (13) (14)

ˆ˜ 2j +τg˜ , where τg˜ is the posterior varifor further use. It is obvious that [Θ]j,j = g j j ˜j . By employing Taylor series expansion, Δ can be represented ance matrix of g as: Δ ≈ IN + Δ1 + Δ2 2 .

(15)

With the above operation, we will further derive the objective functions for each parameter by doing some useful calculations as: ˆ (l−1) ) = ln |πΓ| + tr{Γ−1 Θ} − ln p(γ) + C1 , Q(γ, Ξ ˆ Q(ρ, Ξ

(l−1)

T

H

) = ρ {(BS SB ) Θ}ρ ˆ˜ ∗ )BSH EH y −2{diag((g −diag(BSH SAH Θ)}T ρ + C2 ,

ˆ Q(, Ξ

(l−1)

(16)

H ∗

ˆ ˜ } ) = 2{ΔT1 diag(y∗ )SΦH g T ∗ ˆ ˜ }2 + C3 , + 2{Δ2 diag(y )SΦH g

(17) (18)

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where (15) and EH E = I is utilized, and C1 , C2 , C3 are the items not related with γ, ρ, and , respectively. It can be found that these functions are dependent on ˆ˜ and Θ, and now we turn to calculate the following two posterior statistics, i.e., g these terms. 3.3

GAMP for Posterior Statistics

ˆ ˜ and Θ In this subsection, our objective is to obtain the posterior statistics g under the channel model (3) observation equation (5). It is clear that the posterior joint PDF can be calculated with Bayes rule as ˆ (l−1) )p(˜ ˆ (l−1) ) (l−1) p(y|˜ g; Ξ g; Ξ ˆ )= p(˜ g|y; Ξ ˆ (l−1) ) p(y; Ξ

(19)

However, it is diﬃcult to directly approach these terms, as the high dimensional integrals exists over the marginal distributions. To tackle this problem, and to embrace higher convergence performance, we will resort to the damped ˜ , with given prior knowledge of GAMP to achieve the MMSE estimation of g g). The details of the damped GAMP are p(˜ g) and a likelihood function p(ym |˜ summarized in Algorithm 1.

Algorithm 1. GAMP for posterior charactoristics 0

ˆ ˜ ← 0. 1: Initialize: T ← |J|2 , τˆ 0g˜ , γ 0 , (σ 2 )0 > 0, s0 , g 2: for k = 1, 2, . . . , Kmax do 3: 1/τ kp ← Tτ kg˜ . ˆk. ˜ 4: pk ← sk−1 + τ kp Jg k k k k 5: τ s ← τ p gs (p , τ p ). 6: sk ← (1 − θs )sk−1 + θs gs (pk , τ kp ). 7: 1/τ kr ← TT τ ks . k ˆ ˜ − τ kr JT sk . 8: rk ← g ← τ kr gg˜ (rk , τ kr ). 9: τ k+1 ˜ g k+1 ˆ ˆ k + θg˜ gg˜ (rk , τ kr ) . ˜ ˜ 10: g ← (1 − θg˜ )g k+1 ˆ ˜ |2 + τ k+1 ). 11: Θk+1 = diag(|g ˜ g

k+1 k+1 2 ˆ ˆ k 2 /g ˆ ˜ ˜ ˜ 12: if g −g < η then 13: BREAK. 14: end if 15: end for k+1 ˆ ˜ , Θk+1 . 16: return g

k+1

ˆ˜ In the algorithm, |J|2 and |g |2 is a component wise operation. θs , θg˜ ∈ (0, 1] are damping factors, Kmax is the maximum allowed number of GAMP iterations, η is the threshold parameter. With the help of sum-product algorithm,

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the MMSE estimation problem is modiﬁed to a sequence of scalar MMSE estimates with intermediate variables p and r by using the output function and input function. the two functions are separately deﬁned as: zm p(ym |zm )CN (zm ; τppm , τp1 )dzm m m , (20) [gs (p, τ p )]m = p(ym |zm )CN (zm ; τppm , τp1 )dzm m m gn ; rn , τrn )d˜ gn g˜n p(xn )CN (˜ , (21) [gg˜ (r, τ r )]n = gn ; rn , τrn )d˜ gn p(˜ gn )CN (˜ ˜ and z = J˜ where p and r denote the approximations of noise inﬂuenced g g, with covariance τ p and τ r , respectively. Further, for the parameterized prior imposed on g˜, we can derive: (p/τ p − y) , σn2 + 1/τ p γ gg˜ (r, τ r ) = r. γ + τr

gs (p, τ p ) =

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ˆ (l) by maximizing (16)–(18). In the following, we will update Ξ (1) Updating γ: As we have derived the objective function (16) only related with γ, by taking the derivation with respect to γ and set as zero, we can obtain: (l)

(l)

ˆ˜ |2 + τ . γ (l) = |g ˜ g

(24)

(2) Updating ρ and : As ρ and are uncoupled, we can separately update them. In the similar way, the following updating equations can be derived: ρ(l) = {(BSH SBH )∗ Θ}−1 {diag((˜ g∗ )BSH EH y H H − diag(BS SA Θ)}, ˜ˆ } {ΔT1 diag(y∗ )SΦH g (l) = − , T ∗ H ˆ˜ } {Δ2 diag(y )SΦ g

(25) (26)

With the estimated model parameters, we can further acquire the estimation of virtual channel accurately.

4

Simulations Results

In this section, we will evaluate the performance of our proposed estimation scheme through numerical simulation. We consider a massive MIMO network where the BS is equipped with Nt = 128 antennas. Lt = 64 is the length of training sequences. We take the DOD range within [−49◦ , −43◦ ] as an example to show the perfect performance. The signal-to-noise ratio SNR = σp2 /σn2 . The

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performance is measured as the average MSEs of the model parameters as well 2 τ i −xi ˜ , ρ, . ,x = g as the virtual channel, i.e., MSEx = τ1 i=1 ˆxx 2 i First, we focus on the convergence of our method. Figure 1 presents the MSE of all the estimated parameters versus the number of EM iteration, with SNR = 20 dB. We can infer from Fig. 1 that the parameters get converged within 7 or 8 iterations, which shows the great convergence speed of our proposed scheme. Then we investigate the relationship between the performance and SNR. To get the best performance, we run EM algorithm for saturated iterations for each SNR case. With the increase of the SNR, Fig. 2 shows that the MSE of all parameters as well as the virtual channel are decreasing almost linearly. Furthermore, although the SNR is low, the MSE of virtual channel is also acceptable.

5

Conclusion

We proposed a novel channel estimation scheme for oﬀ-grid massive MIMO channel model in this paper, where the carrier frequency oﬀset is taken into consideration. ﬁrst, an oﬀ-grid channel model was built. Then, an EM based sparse Bayesian learning framework was introduced to capture the oﬀ-grid bias and the CFO. In the learning process, to acquire the needed posterior statistics, a damped GAMP algorithm was introduced. Numerical results showed the wonderful performance of our proposed scheme.

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References 1. Marzetta TL (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wireless Commun 9(11):3590–3600 2. Noh S, Zoltowski MD, Love DJ (2016) Training sequence design for feedback assisted hybrid beamforming in massive MIMO systems. IEEE Trans Commun 64(1):187–200 3. Xie H, Gao F, Zhang S, Jin S (2017) A uniﬁed transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model. IEEE Trans Veh Technol 66(4):3170–3184 4. Tan W, Matthaiou M, Jin S, Li X (2017) Spectral eﬃciency of DFT-based processing hybrid architectures in massive MIMO. IEEE Wireless Commun. Letters 6(5):586–589 5. Ma J, Zhang S, Li H, Gao F, Jin S (2018) Sparse Bayesian learning for the timevarying massive MIMO channels: acquisition and tracking. IEEE Trans Commun, pp 1–1 6. Wu L, Zhang X, Li P (2008) A low-complexity blind carrier frequency oﬀset estimator for MIMO-OFDM systems. IEEE Signal Process Lett 15:769–772 7. You L, Gao X, Swindlehurst AL, Zhong W (2016) Channel acquisition for massive MIMO-OFDM with adjustable phase shift pilots. IEEE Trans Signal Process 64(6):1461–1476 8. Zhao J, Gao F, Jia W, Zhang S, Jin S, Lin H (2017) Angle domain hybrid precoding and channel tracking for millimeter wave massive MIMO systems. IEEE Trans Wireless Commun 16(10):6868–6880 9. Parvazi P, Gershman AB (2010) Direction-of-arrival and spatial signature estimation in antenna arrays with pairwise sensor calibration. In: 2010 IEEE international conference on acoustics, speech and signal processing, pp 2618–2621 10. Wipf DP, Rao BD (2004) Sparse Bayesian learning for basis selection. IEEE Trans Signal Process 52(8):2153–2164

Study of Key Technological Performance Parameters of Carbon-Fiber Infrared Heating Cage Fei Xu(&), Yan Xia, Guoqing Liu, Yuzhong Li, Jinming Chen, and Chun Liu Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected]

Abstract. Using a thermal-vacuum test and Monte Carlo simulation analysis, this paper examined the key technical performance parameters of the carbonﬁber heating cage and compared them with those of the traditional nickelchromium alloy heating cage. The results indicated that the heating capacity and temperature uniformity of the carbon-ﬁber heating cage for spacecraft were better than those of the traditional nickel-chromium alloy heating cage, and that the electro-thermal properties of the carbon-ﬁber infrared heating cage met the requirements of the spacecraft thermal-vacuum environment. Keywords: Carbon ﬁber Infrared heating cage Heat-flow density Thermal vacuum test Thermal balance test

1 Introduction The traditional infrared heating cage uses black paint coated with a nickel-chromium alloy belt as the heating body. The production process involves cutting nickelchromium alloy into belts, processing the skeleton of the heating cage and the PTFE belts, drilling holes in both the heating and PTFE belts and connecting them with screws and springs, ﬁxing heating strips by spot welding, and ﬁnally spraying the cage with black paint and cleaning up. Most of these steps are manually performed and are labor- and time-intensive [10]. The use of carbon ﬁber as a heating material can avoid the above shortcomings. Carbon-ﬁber composites, which are widely used in the aerospace ﬁeld [1, 9] are carbon materials with emissive values similar to those of blackbodies. They have the advantages of high speciﬁc strength, a high speciﬁc modulus, and high electro-thermal radiation efﬁciency [2, 3, 7, 8]. At the same time, their thermal expansion coefﬁcient in high and low temperature environments is almost zero [4], and they can adapt to the complex thermal radiation environment of space. Xu et al. [10] studied the feasibility of using carbon ﬁbers as heating-cage electrothermal materials. With respect to its convenience of assembly, improved thermal radiation efﬁciency, lightweight heating cage, and adaptability to a complex high and low temperature environment, the carbon ﬁber heating cage has great advantages over the traditional infrared heating cage. In this paper, the design and heating performance

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of the carbon-ﬁber heating cage were analyzed and experimentally studied. The temperature stability and heat-flow uniformity of the carbon-ﬁber heating cage were speciﬁcally tested, as these two characteristics were key technical indicators of the performance of the heating cage and were also important indicators for evaluating whether the carbon-ﬁber heating cage could be applied in a thermal-vacuum test for spacecraft [5, 6].

2 Structural Design of Carbon-Fiber Heating Cage and Layout of Heat-Flow Meter Used in Testing 2.1

Structural Design of Carbon-Fiber Heating Cage

With reference to the structure of the traditional infrared heating cage, we designed and tested the performance of the carbon-ﬁber heating cage shown in Fig. 1 in a thermalvacuum environment. The main difference between the carbon-ﬁber heating cage and the nickel-chromium-alloy heating cage was their different heating belts, which meant their connection methods differed. Because the thermal expansion coefﬁcient of the carbon-ﬁber belt was very low, the length of the belt varied very little during a high and

Fig. 1. Carbon ﬁber heating cage

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low temperature cycle. In the high temperature stage, there was no possibility of contact between the belt and the surface of the spacecraft or other equipment because of the length of the belt. Therefore, the tension spring could be omitted, as could the hanging spring link. In processing, holes were drilled directly on the PTFE board above and below the skeleton of the heating cage, such that the strips could pass through the holes to form a loop, which greatly simpliﬁed the assembly process. In this study, we designed a cubic carbon-ﬁber heating cage to test its performance in a thermal-vacuum environment. This cage had a front, rear, left, right, and top side, with the dimensions of 1000 mm 1000 mm 1000 mm 1000 mm. Each side was controlled independently, and 12 loops were attached to each side. The belt spacing was 40 mm, the belt width was 5 mm, and the coverage coefﬁcient was 0.125. Figure 1 shows a photograph of the carbon-ﬁber heating cage. 2.2

Layout of Heat-Flow Meter Used in Testing

When the heating cage was more than 100 mm away from the satellite surface, the uniformity of the heat flow to the satellite surface was ensured by the use of a heating cage with a coverage coefﬁcient greater than 0.06. Therefore, the carbon-ﬁber heating cage adopted the same parallel belt layout as that of the traditional heating cage, and its coverage coefﬁcient was 0.125. The distance between the heat-flow meter and the heating-belt surface was 280 mm. This design met the uniformity requirements. On the plane 280 mm below the infrared cage at the top, eight heat-flow meters were arranged that point in different directions. As shown in Fig. 2, the layout consisted of four stainless steel square tubes with a rod length of 700 mm and a middle square edge length of 350 mm.

Fig. 2. Layout of heat-flow meter

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3 Testing and Analysis of the Performance in a ThermalVacuum Environment 3.1

Test Preparation

Considering the testing cost and the actual situation to be provided by the space environment simulator, we selected the KM2F space environment simulator for this experiment. After installing the carbon-ﬁber heating cage and the test heat-flow meter in the vacuum container of the KM2F equipment, we connected the heating cable and measuring line. After performing a circuit conduction test to ensure its proper operation, we closed the door of the container. After evacuation, the vacuum vessel pressure reached 10−3 Pa, liquid nitrogen was applied to the heat sink, and the temperature of the heat sink reached 100 K. 3.2

Testing

When the environment of the KM2F simulated chamber met the testing requirement, that is, a vacuum pressure of 10−3 Pa and a heat sink temperature less than 100 K, the test was started. This test involved three aspects: measurement of heat-flow uniformity and heating capacity and a heating-capacity comparison test. 3.2.1

Measurement of Heat-Flow Uniformity in Carbon-Fiber Infrared Cage When the vacuum pressure in the simulated chamber was greater than 10−3 Pa and the heat sink temperature was lower than 100 K, the test began. When heating was applied, the 12 circuits of the top infrared cage were uniformly charged, that is, the applied current of each circuit was the same. When the heat flow recorded by the heat-flow meter became stable, the next current value was applied. The current values applied during the test were 1, 2, and 3 A. The standard deﬁnition used here for a stable heat flow was: within 1 min, the change in the heat-flow meter reading was within 0.1 °C. The following experimental results were based on this standard. 3.2.2 Testing the Heating Capacity of Carbon-Fiber Heating Cage As a satellite simulation specimen, we used a 1-mm thick aluminum plate to fabricate a ﬁve-sided box structure with no bottom panel. The dimensions of the box were 450 mm 450 mm 450 mm. The outer surface of the box was coated with black paint, and three thermocouple temperature-measurement points were ﬁxed to each side. The ﬁve sides of the carbon-ﬁber infrared cage corresponded to the ﬁve sides of the box, with the belt surface 300 mm away from the surfaces of the simulated specimen. The 12 circuits of each infrared cage were uniformly charged, that is, the same current was applied to each circuit. When the heat-flow value measured by the heat-flow meter became stable, the next current value was applied. The current values applied during the test were 1, 2, and 3 A.

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3.2.3

Comparison of Heating Capability with Traditional NickelChromium Alloy Heating Cage Using the same simulated specimen and temperature measurement points, we compared the above results with those of the traditional infrared cage, which has a coverage coefﬁcient of 0.5 and envelope dimensions of 700 mm 700 mm 600 mm. The heating capacities of the two kinds of heating cages under different currents were compared using the same current ladder described above. 3.3

Test Results and Analysis of Heat-Flow Uniformity

Figure 3 shows the test results of the heat-flow meter, in which the abscissa is time and the ordinate is temperature in °C. As shown in Fig. 3, the heating temperature clearly increased as heating current changed from 1 to 2 A and then 3 A.

Fig. 3. Heat-flow meter data curve of carbon-ﬁber heating cage specimen

3.3.1 Calculation of Heat Flux To analyze the test data recorded by the heat-flow meter, we used the StefanBoltzmann formula, as follows: j ¼ erT4 where j heat-flow uniformity, W/m2; e the radiation coefﬁcient of the carbon-ﬁber heating belt, which is 0.91 [10]; r Stefan constant, 5:67 108 W m2 K4 ; T absolute temperature, K

ð1Þ

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3.3.2 Calculation of Heat-Flow Uniformity The heat flux could be calculated using Formula (2): E ¼ ðJmax Jmin Þ ðJmax þ Jmin ) 100%

ð2Þ

where E heat flux; Jmax maximum heating heat flow, W/m2 ; Jmin minimum heating heat flow, W/m2 In Formula (2), Jmax and Jmin were obtained based on the data obtained in the experiment by the eight heat-flow meters after heat-flow stabilization (Table 1). Table 1. Calculations of heat-flow data Heating current, I (A) 1 2 3

Mean heat flow density, Jmean ðW/m2 Þ 236.3 713.2 1458.9

Heat flow density uniformity, E (%) 5.25 7.61 8.76

In addition, we analyzed the data recorded by each heat-flow meter and their deviations from the mean, using the calculation method shown in Formula (3): di ¼ jJi Jmean j Jmean 100%

ð3Þ

where di the deviation in heat flow of the No. i heat-flow meter; where i ranges from 1–8; Ji the heat flow value of the No. i heat-flow meter, W/m2 : The degrees of deviation between the data and the mean value is shown in Fig. 4, where the abscissa identiﬁes each of the eight Nos. i of the heat-flow meters, and the ordinate is di , the degree of heat-flow deviation of each No. i heat-flow meter. Figure 4a: The heat-flux balance of 1 A showed that except for the second meter, the deviation in the heat flux remains below the 5% line. Figure 4b: The heat flux balance of 2 A showed deviations at three points in meter Nos. 1, 2, and 8, which were above the 5% line. Figure 4c: The heat flux balance of 3 A shows deviations at four points in meter Nos. 1, 3, 7, and 8, which were above the 5% line.

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(b) 2A

(c) 3A Fig. 4. Heat-flow density deviation curves for eight heat-flow meters

3.4

Comparison and Analysis of Two Kinds of Heating Cage

3.4.1 Test Results and Analysis Figure 5 shows the temperature data obtained for the carbon-ﬁber heating cage, where the abscissa is time and the ordinate is temperature in °C. As shown in the ﬁgure, there was an obvious increase as the current increased from 1 to 2 A and then 3 A.

Fig. 5. Heating temperature data curve of carbon-ﬁber heating cage

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Fig. 6. Heating temperature data curves of nickel-chromium alloy heating cage

Based on temperature data recorded by 15 thermocouple thermometers, the average heating temperatures were 268.0 K, 358.9 K, and 426.7 K for the heating currents 1 A, 2 A, and 3 A, respectively. Figure 6 shows the temperature data obtained for the heating cage made of nickelchromium alloy, where the abscissa is time and the ordinate is temperature in °C. As shown in Fig. 6, there was an obvious increase as the currents progressed from 1 to 2 A and then 3 A. Based on the temperature data recorded by 15 thermocouple thermometers, the average heating temperatures were 236.1 K, 316.2 K, and 385.0 K for heating currents of 1 A, 2 A, and 3 A, respectively. Figure 7 shows the statistical results for the heating capacities of the two kinds of heating cage.

Fig. 7. Comparison of heating capacities of two kinds of heating cage

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4 Simulation Analysis of Heat-Flow Uniformity 4.1

Monte Carlo Method

Using the Monte Carlo method, the radiation energy was considered to consist of energy beams or energy particles, with each particle having the same level of energy. The radiation direction of the particle was determined based on the probability distribution of Lambert’s law. In this calculation, only the heat flux was considered, and the absorption, reflection, or scattering of each particle was ignored. The basic principle of the Monte Carlo method was the establishment of a mathematical model. As such, the coordinates of the particles emitted by the carbon-ﬁber heating cage on the surface of the satellite were calculated by their geometrical relationship. Given the coordinates of the intersection points, a determination was made regarding whether the particles fall on the surface of the satellite. When enough particles were emitted, the number of particles in the grid on the surface of the satellite was used to visually express the relative heat flux on the satellite surface [11]. 4.2

Parameter Setting

When setting parameters, the heat-flow meter was regarded as being positioned on the satellite surface. In addition to making calculations for a belt spacing of 40 mm, belt spacings of 30 mm and 20 mm were also simulated and calculated. The parameters used in the simulation analysis were as follows: (1) Emissivity of carbon ﬁber heating belt: 0.91 [10]; (2) Conversion efﬁciency of carbon ﬁber heating: >98% [3]; (3) Distance of heating belt from heat-flow meter: 280 mm; (4) Width of carbon-ﬁber heating belt: 5 mm; (5) Belt spacings: 40, 30, 20 mm. 4.3

Simulation Results

Figure 8 shows the results of the Monte Carlo simulation analyses, in which the color indicates the heat flow, where red is the highest, and yellow, green, and blue indicate a gradual respective decrease in heat flow. Simulation analyses were performed for the three belt spacings of 40, 30, and 20 mm to determine the influence of different belt spacing on heat-flow uniformity. According to the ﬁgures, the inhomogeneity of the 40-mm spacing was 12.52%, that of 30-mm was 11.79%, and that of 20-mm was 11.32%. Therefore, the uniformity of the 40-mm belt spacing was lowest, and that of the 20-mm belt spacing was better than the other two.

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Fig. 8. Uniformity and average heat-flow location with 40/30/20-mm spacing

4.4

Comparison of Test and Simulation Analysis Data

For a belt spacing of 40 mm and a heating current of 3 A, Table 2 presents a comparison of the experimental results with those of the computer simulation analysis. Table 2. Comparison of test and Monte Carlo simulation results Mean heat flow density (W/m2) Heat flow density uniformity (%)

Test 1458.9 8.76

Monte Carlo simulation 1428.8 12.52

Table 2 shows that with respect to mean heat-flux density, the test results were basically consistent with the Monte Carlo simulation results. Because of the limitations of the test conditions, only eight heat-flow meters were used to obtain heat-flow measurements. The layout and mode of ﬁxing the heat flow meters greatly influenced the measurement results. As such, the results of the heat-flux test deviated greatly from those of the Monte Carlo simulation analysis.

5 Conclusion Based on a comparison of experimental test results for a carbon-ﬁber heating cage and a traditional nickel-chromium heating cage in a thermal-vacuum environment and a Monte Carlo simulation analysis and test, the following conclusions were made: (1) When the heating currents of a carbon-ﬁber heating cage were 1, 2, and 3 A, the heat flux was less than 10%. (2) The heating capacity of the carbon-ﬁber heating cage was much greater than that of the traditional nickel-chromium alloy heating cage. (3) With respect to the mean heat flux, the results of our Monte Carlo simulation analysis were basically consistent with our experimental results. (4) The electrothermal characteristics of the carbon-ﬁber heating cage met the requirements of a spacecraft vacuum-thermal environment.

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References 1. AliAl A, Philipp P, Michael S (2018) Eco-efﬁciency assessment of manufacturing carbon ﬁber reinforced polymers (CFRP) in aerospace industry. Aerosp Sci Technol 79:669–678 2. Cao WW, Zhu B, Cai X et al (2010) The simulation study of radiative heat flux intensity distribution with different assignments of carbon ﬁber electric heating elements. J Funct Mater 41:130–135 3. Cao WW, Zhu B, Wang CG (2007) Numerical simulation on the radiation intensity distribution of carbon ﬁber infrared electric heating radiator. Chin J Mech Eng-En 43:6–10 4. Dong K, Peng X, Zhang JJ et al (2017) Temperature-dependent thermal expansion behaviors of carbon ﬁber/epoxy plain woven composites: experimental and numerical studies. Compos Struct 176:329–341 5. Huang BC, Ma YL (2002) Space environment test technology of spacecraft. National Defense Industry Press, Beijing 6. Ji XY, Liu GQ, Wang J et al (2019) Experimental veriﬁcation and comparison of different tailoring models for spacecraft electronics thermal cycling tests. Acta Astraunat 159:77–86 7. Qiu L, Guo P, Yang XQ et al (2019) Electro curing of oriented bismaleimide between aligned carbon nanotubes for high mechanical and thermal performances. Carbon 145:650– 657 8. Rani R, Suryasarathi B (2019) Electrodeposited carbon ﬁber and epoxy based sandwich architectures suppress electromagnetic radiation by absorption. Compos Part B-Eng 161:578–585 9. Sagar I, Nikhil T, Narayanamurthy V et al (2017) Analysis of CFRP flight interface brackets under shock loads. Mater Today Proc 4:2492–2500 10. Xu F, Li YZ, Yang WQ et al (2016) The feasibility of using carbon ﬁber as electro-thermal radiant material for infrared heating cage. Spacecraft Environ Eng 33:668–671 11. Yang XN, Sun YW (2008) Influence of infrared heating cage coverage coefﬁcient on flux uniformity. Spacecraft Eng 17:38–41

Research on Switching Power Supply Based on Soft Switching Technology Zhihong Zhang1(&) and Hong He2 1

2

Tianjin City Electrical Engineering Technology Research Institute, Tianjin 300232, China [email protected] Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China [email protected]

Abstract. Aiming at the problem that it is difﬁcult to realize zero voltage and zero current switching (ZVZCS) in the current switching power supply, the resonance energy of the lagging arm is insufﬁcient. In this paper, with TMS320F2812 as the control core, the DC/DC part of the switching power supply is designed by using the PWM phase shift control full-bridge ZVZCS technology in the series diode of the hysteresis arm of the converter circuit. The soft switch is well implemented in the case of load change. The MATLAB simulation results show that the soft switching power supply has the advantages of high output precision, fast dynamic response and small overshoot. Keywords: Switching power supply switching PID control technology

Digital signal processing Soft

1 Introduction Energy plays an important role in many aspects of modern society. Switching power supply has been widely used because of its high efﬁciency and high control precision. At present, due to the high switching frequency and large operating current of the power supply, the switching loss and electromagnetic interference will occur in the process of switching on and off. So the soft switching technology realized by advanced control technology and digital microprocessor has been applied more and more in switching power supply [1]. Aiming at the problem that it is difﬁcult to realize zero voltage and zero current switching (ZVZCS) in the current switching power supply, the resonance energy of the lagging arm is insufﬁcient. In this paper, the control chip TMS320F2812, based on DSP is used to design the DC/DC part of the switching power supply in the phaseshifted full-bridge zero-voltage zero-current converter circuit by using the PID control technology and the PWM phase-shift control full-bridge ZVZCS technology. Under the condition of load change, the soft switch can be realized well, and the lead arm zero voltage switch (ZVS) and the lag arm zero current switch (ZCS) can be realized. The MATLAB simulation results show that the soft switching power supply has the advantages of high output precision, fast dynamic response and small overshoot [2]. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 156–164, 2020 https://doi.org/10.1007/978-981-13-9409-6_20

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2 The Soft Switching Realization of Switching Power Supply 2.1

Section Heading (“H1”)

The soft-switching mode of phase-shifted full-bridge switching circuit is divided into zero voltage and zero current switch and their combination. The basic principle is to use the capacitance and inductance in the circuit to resonant to realize the current and voltage crossing. At this point, ZVS and ZCS. can be realized by switching on the switch [3]. The phase-shifted full-bridge ZVS forearm has enough resonant energy of inductance to realize zero voltage turn-on. However, because the primary current of transformer is small at turn on, the secondary rectiﬁer diode forms a recurrent loop, which is similar to short circuit. Therefore, it is difﬁcult to realize ZVS, with small inductance and large duty cycle loss. As shown in Fig. 1, the converter is composed of VD7 and VD8 diodes in series with the hysteresis arm to realize the ZVZCS. of the hysteresis arm. The leading arm is composed of VT1 and VT2, the lagging arm is composed of VT3 and VT4, the isolation capacitor is Cb, the primary current of transformer is Ip, and the secondary rectiﬁer diode is VD5 and VD6.

The dc load

Fig. 1. Switch power supply circuit diagram

The working waveform of ZVZCS converter is shown in Fig. 2, where [t0–t6] is half a period and is divided into six switching modes. Switching mode 0 [t0]. At t0, VT1 and VT4 are on, and transformer primary current Ip charges isolation capacitor Cb. Switching mode 1 [t0, t1], turning off VT1, Ip from VT1 to C2 and C1, charging C1, discharging C2, increasing linearly the voltage of C1 from zero, decreasing voltage of C2 linearly from Uin to VT1 is zero voltage turn-off at t 0. At T1, the voltage of C2 drops to zero, and VT2s anti-parallel diode D2 naturally leads to the end of mode 1.

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Fig. 2. Working waveform of ZVZCS convert

Switching mode 2 [t1, t2], when D2 is switched on, the VT2 zero voltage is turned on. Because D2 and VT4 are on at the same time, the leakage inductance of Uab = 0, is smaller, but the isolation capacitance is larger, the isolation capacitance voltage is basically unchanged, the primary current is linearly reduced, and at T2 moment, and the primary current dropped to zero. Switching mode 3 [t2, t3], the primary current Ip is zero, the A point to ground voltage is zero, and the B point to ground voltage is-Ucbp, secondary rectiﬁer current. Switch mode 4 [t3, t4], when VT4, is turned off at T3, there is no current flowing through the VT4, so the VT4 is zero current turn-off. After a very small delay, the primary current can’t be abrupt because of the existence of leakage inductance. The VT3 is zero current on. Switching Mode 5 [t4, t5], starting from the T4 moment, the primary side provides energy for the load, and simultaneously recharges the isolation capacitor. The voltage on the isolation capacitor is ready for the next VT2 zero current turn-off and the VT4 zero current turn-on. 2.2

TMS320F2812 Control Hardware Implementation

The block diagram of the TMS320F2812 control system of the switching power supply is shown in Fig. 3, which is mainly composed of the controller TMS320F2812 and the circuit.

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The main circuit Inverter circuit

transformer

Rectifier filter circuit

The dc load

The fault signal Protection circuit

Driver circuit

Current and voltage signal detection and adjustment module Current and voltage feedback signals A/D sampling, processing

PWM generator Current regulation

TMS320F2812

Voltage regulation

Voltage for a given

Fig. 3. TMS320F2812 control system block diagram of switching power supply

The main circuit includes inverter circuit, transformer, rectiﬁer ﬁlter circuit, dc load, drive protection circuit and current and voltage signal detection and adjustment module. TMS320F2812 mainly realizes A/D sampling processing of voltage and current feedback signal, voltage and current regulation (PID operation), PWM pulse output and other functions. The current output voltage analog signals such as the current voltage signal detection and adjustment module test and adjust to the appropriate level to the A/D mouth of TMS320F2812, the digital PID algorithm with A given voltage, and the results as the given value of the current regulator, then through the current PID regulator, the output as the corresponding PWM output pulse generator control register values, adjust the output relative duty ratio of PWM pulse [4]. After the ampliﬁcation of the power ampliﬁer in the drive circuit, each switch tube is driven to operate to realize the dynamic regulation of output voltage and current and phase shift pulse. At the same time, the fault signal enters the TMS320F2812 protection I/O port, pulls down the D0 port of the general input/output interface (GPIO), and enters the protection pin PDPINTA to interrupt, making the output PWM pulse of the PWM generator present a high resistance state forcibly, thus realizing the protection of the hardware. 2.3

Software Implementation of TMS320F2812 Control

The flow chart of the program design is shown in Fig. 4, which can be divided into four modules: TMS320F2812 initialization, real-time data acquisition, interrupt, PWM pulse generation and PID regulation. • TMS320F2812 Initialization Module This module includes system, constant and variable initialization, ADC module initialization, GPIO port initialization, watchdog WD initialization, event management module EVA/B and interrupt initialization, etc., to ensure that the controller TMS320F2812 works reliably and stably.

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• Real-Time Data Acquisition Module This module in each cycle collection and output current and voltage signal, in order to meet the accuracy requirement of the power to control the output voltage ripple, set the frequency of the A/D sampler to 20 kHz, selection of continuous sampling average of 3 times as A sampling of data and increase the accuracy of the order will be picked by the data operation result CMPR values in an array in preparation for the corresponding processing of late.

Program started

System, constant, variable, A/D, EVA,WD, Interrupt initialization

Start timer, open interrupt Read the A/D sample values Filtering and actual numerical conversion

Interrupt the entrance

The value of the dynamic assignment comparison register CMPR

Clear interrupt flag to allow for other interrupts

Interrupt return

PID calculation of voltage and current

Fig. 4. Flow chart of program design

• Interrupt and PWM Pulse Generation Module The voltage and current signals are collected in real time when A/D is interrupted. After ﬁltering and rectiﬁcation and corresponding processing and calculation, the output can be automatically and dynamically adjusted by dynamically changing the value of CMPR in the comparison register of TMS320F2812 event manager EVA/B when the event manager is interrupted or the cycle is interrupted. After the CMPR value of the comparison register is input into the PWM circuit and compared with the symmetric or asymmetric waveform set by T1CON, some square waves are generated. These square waves are input into the dead-time circuit to produce two dead-time signals. Then the output logic of each PWM is set through the output logic circuit to generate the required PWM signals. • ID Control Module The double closed-loop control mode of current inner loop and voltage outer loop is adopted to carry out PID operation according to the difference between sampling value and reference value. The result is used as the input value of PWM wave module to carry out pulse width modulation for PWM wave, and the relative duty ratio and phase

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shift Angle of PWM signal are calculated in real time to make the converter output stable dc voltage and required current. The real-time parameters were calculated in advance and determined in the Simulink simulation environment, and then the parameters were adjusted through speciﬁc experiments to achieve the PID control with good steady-state and dynamic characteristics [5, 6]. 2.4

System Simulation Model

Simulink is used to simulate the system. The system simulation model is shown in Fig. 5.

Fig. 5. System simulation model

• PWM Module and PID Module Design In Fig. 5, the output of the Repeating Sequence module is the triangular wave of amplitude −1 to 1, as shown in Fig. 6.

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Fig. 6. Triangular wave shape

PID output value and its negative value are compared with this triangular wave respectively. After Boolean operation, the output is inversed separately, that is, two groups of PWM waves with the same frequency as triangular wave are output, as shown in Fig. 7.

Fig. 7. PWM waveform

The traditional PID control is realized by the analog PID controller, while the current PID control is realized by the digital PID, namely the computer program. The main parameters of the PID module are set according to its control algorithm, where Kp ¼ 0:01 Ki ¼ 40; Kd ¼ 0:5e 1: • Power Supply Module and Transformer Module In practical industrial applications, most power supplies are three-phase 380 V, and the output voltage after a series of rectiﬁer ﬁltering is about 513 V dc voltage. Therefore, the output voltage of IGBT bridge is set as 513 V. The rated frequency of the transformer is set at 50 Hz, the rated power is set at 12000 W, the rated voltage of winding 1 is set at 256 V, and the rated voltage of winding 2 and winding 3 is set at 12 V.

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3 Simulation Results and Analysis According to the system simulation model and parameter Settings in Fig. 5, the simulation output voltage waveform of the Manual Switch on the voltage side is shown in Fig. 8, and the simulation output current waveform of the current side on is shown in Fig. 9 (the simulation time is changed).

Fig. 8. Simulation output voltage waveform

Fig. 9. Simulation output current waveform

As can be seen from the simulation results and system parameter Settings of the two ﬁgures, the output voltage is 0–12 V, the voltage stabilizing accuracy is less than 1.8%, the output current is 0–1000 A, the current stabilizing accuracy is less than 1.3%, the ripple is less than 2%, and the stabilizing time is less than 0.001 s.

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4 Conclusion In this paper, the soft switch technology energy saving anti-interference characteristics combined with TMS320F2812 controller of intelligent control, use of the advantages of simple and easy to implement PID control, the phase-shift full bridge zero voltage zero current converter circuit of lagging arm series diode, the MATLAB simulation results show that the design of the soft switch technology output switching power supply system has high precision, fast dynamic response, and the advantages of small overshoot amount. Acknowledgements. This work is supported by Fund Project: Tianjin Science and Technology Special Fund to support major science and technology projects (14ZCDGSF00028). And thanks Tianjin Key Laboratory for Control Theory and Application in Complicated Systems, Tianjin University of Technology, Tianjin 300191, and China.

References 1. Hou Y, Xia R (2017) Power emergency integrated communication system based on soft switching technology. Electr Power Inf Commun Technol 4:60–64 2. Gupta AC, Kalita N, Gaur H et al (2016) Peak of spectral energy distribution plays an important role in intra-day variability of blazars. Mon Not R Astron Soc 462(2):1508–1516 3. Shah P, Agashe S (2016) Review of fractional PID controller. Mechatronics 38:29–41 4. Lin F, Wang Y, Wang Z et al (2016) The design of electric car DC/DC converter based on the phase-shifted full-bridge ZVS Control. Energy Procedia 88:940–944 5. Johnson N, Fletcher JD, Humphreys DA et al (2017) Ultrafast voltage sampling using singleelectron wavepackets. Appl Phys Lett 110(10):102–105 6. Grebennikov A (2016) High-efﬁciency class-E power ampliﬁer With shunt capacitance and shunt ﬁlter. IEEE Trans Circuits Syst I Regul Pap 63(1):12–22

Grid Adaptive DOA Estimation Method in Monostatic MIMO Radar Using Sparse Bayesian Learning Yue Wang1,2 , Kangyong You1 , Dan Wang1 , and Wenbin Guo1,2(B) 1

Wireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, China {wangxiaoyue, ykyyiwang, wangdan121, gwb}@bupt.edu.cn 2 Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang, China

Abstract. In monostatic Multi-Input Multi-Output (MIMO) radar system, Direction Of Arrival (DOA) estimation is important for target detection. However, conventional MIMO DOA estimation approaches suﬀers from the oﬀ-grid issue which refers that the real DOAs deviate from the predeﬁned grid points. In this paper, a grid adaptive DOA estimation method is proposed to address the oﬀ-grid error and the improper initial grid problem for monostatic MIMO radar system. We construct a Bayesian learning framework with Laplacian prior to adjust grid and observation dictionary adaptively. Simulation results show the superior performance of the proposed method in terms of high angle resolution and robustness against the noise by comparing with the state-of-the-art DOA estimation methods in MIMO radar system.

Keywords: Monostatic MIMO radar Compressed sensing

1

· DOA estimation · Oﬀ-grid ·

Introduction

Compared with conventional phased-array radar, MIMO radar has the advantages of high reliability and high precision. Moreover, it also has the ability of anti-interference and anti-stealth [1]. According to the antenna conﬁguration, MIMO radar can be classiﬁed into statistical MIMO radar and colocated MIMO radar. In the statistical MIMO radar [2], the distance between the antennas is signiﬁcant to obtain a spatial diversity gain. In the colocated MIMO radar [3] which including monostatic and bistatic MIMO radar, both transmitting and receiving antennas elements are closely spaced. Colocated MIMO radar can achieve higher angle resolution because it introduces the idea of transmitting waveform diversity to form the large virtual aperture. In this paper, a novel DOA estimation method is investigated in the monostatic MIMO radar system. c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 165–178, 2020 https://doi.org/10.1007/978-981-13-9409-6_21

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Many DOA estimation methods [4–11] have been proposed for MIMO radar based on conventional array parameter estimation methods, such as Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) method [5,6] and Multiple Signal Classiﬁcation (MUSIC) method [7]. In recent years, compressed sensing (CS) [12] provides a new perspective for DOA estimation in MIMO radar [13, 14]. By discretizing the angle domain into a number of grid points or cells, the spatial sparsity of the target DOAs provides the possibility to apply compressed sensing method. A large quantity of literature has studied the DOA estimation model based on CS theory [15–17]. However, most of them assume that the target DOAs fall on the ﬁxed grid points. When the real DOAs deviate from the ﬁxed grid points, these algorithms will suﬀer from decreased performances. In [18], the oﬀ-grid error is treated as a disturbance, and the grid is adjusted once based on linear approximation. Then, the idea of interpolation is introduced and DOA estimation problem is solved by block sparse compressed sensing algorithm in [19]. However, these methods still need a suitable initial grid granularity. A coarse grid may result in performance degradation or losing eﬀectiveness while a dense grid has to pay a price of massive computation. Therefore, it is necessary and important for MIMO radar system to develop a grid adaptive method for accurate DOA estimation, even in the case of a coarse grid. Bayesian compressive sensing (BCS) [20,21] method has attracted a growing interest because it is user parameter-free and can get the estimation variance. In this paper, we propose a grid adaptive DOA estimation method using sparse Bayesian learning. The core idea is to adjust the grid and the observation dictionary adaptively. We refer to our algorithm as grid adaptive DOA estimation algorithm (GADE) in the body of the paper. Numerical simulations show that the proposed method outperforms the state-of-the-art methods. Notations used in this paper are as follows. Vectors and matrices are signiﬁed by bold face letters. x ¯, xT and xH denote complex conjugate, transpose and conjugate transpose of a vector x, respectively. · denotes the -norm. T r(·) and |·| denote the determinant and trace operator, respectively. xj is the j-th entry of the vector x. Ai , Aj and Aij are the i-th row, j-th column and (i, j)-th entry of the matrix A, respectively. diag(x) is a diagonal matrix with vector x being its diagonal elements. diag(A) denotes a column vector composed of the diagonal elements of matrix A. x (θ) is the derivative of x(θ) with respect to θ. , ⊗ and ◦ denote the Hadamard (element-wise) product operator, Kronecker product operator and Khatri-Rao product operator, respectively. The rest of the paper is organized as follows. The system model is described in Sect. 2. Section 3 explains the proposed method in detail. Simulation results are presented in Sect. 4. Section 5 concludes the whole paper.

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System Model MIMO Radar Signal Model

The monostatic MIMO radar [17] is one type of colocated MIMO radar, which is equipped with closely-located transmitting antennas and receiving antennas. Considered a narrow-band monostatic MIMO radar system shown in Fig. 1. Speciﬁcally, Mt transmitting antennas (mt = 1, . . . , Mt ) are all located in a line with the distance of dt from each other, and Mr receiving antennas (mr = 1, . . . , Mr ) are all located in a line with the distance of dr from each other. Mt orthogonal narrow-band waveforms are transmitted simultaneously by the transmitting antennas, and the waveforms are donated by B. After reﬂected by targets, the echoes are received by the receiving antennas. Assuming that there are K far-ﬁeld targets in the coverage area. Since all antennas are located in a small area, all the transmitting antennas and the receiving antennas view the k-th target from almost the same direction, i.e., DOA and DOD (Direction Of Departure) are the same and can be denoted by θk , k = 1, . . . , K. The signal from the transmitter to the K targets and received by the receiver is Z=

K

ar (θk )ξk ej2πfdk t at T (θk )B + W ,

(1)

k=1

where ξk , fdk and W are the reﬂection coeﬃcient, the Doppler shift of the k-th target and the additive Gaussian white noise, respectively. The steering vectors of the transmitter and the receiver are respectively denoted as at (θk ) = [1, e− ar (θk ) = [1, e−

j2πdt sin(θk ) λ j2πdr sin(θk ) λ

, . . . , e− , . . . , e−

j2π(Mt −1)dt sin(θk ) λ

T

] ,

j2π(Mr −1)dr sin(θk ) λ

T

] ,

(2) (3)

where λ is the wavelength of the signal. After matched ﬁltering operation, the observed signals are y(t) = (AR ◦ AT )s(t) + w(t) = As(t) + w(t),

(4)

where AR = [ar (θ1 ), . . . , ar (θK )] ∈ C Mr ×K consists of K steering vectors of the receiver, AT = [at (θ1 ), . . . , at (θK )] ∈ C Mt ×K consists of K steering vectors of the transmitter, s(t) = [s1 (t), . . . , sK (t)]T = [ξ1 ej2πfd1 t , . . . , ξK ej2πfdK t ]T is a column vector whose elements are the products of the reﬂection coeﬃcients and the Doppler shifts of K targets, and w(t) is Gaussian white noise. The transmit-receive steering matrix is denoted as A which can be formulated as A = [ar (θ1 ) ⊗ at (θ1 ), . . . , ar (θK ) ⊗ at (θK )].

(5)

Given T snapshots, the observed signal matrix can be expressed as Y = [y(t1 ), y(t2 ), . . . , y(tT )], leading to the multiple measurement vector (MMV) observation model [1] Y = (AR ◦ AT )S + W = AS + W ,

(6)

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where S = [s(t1 ), s(t2 ), . . . , s(tT )] ∈ C K×T and W = [w(t1 ), w(t2 ), . . . , w(tT )] ∈ C Mr Mt ×T , respectively. 2.2

Traditional On-Grid Model

The central idea of DOA estimation based on CS is extending the observation dictionary A to form an overcomplete dictionary Φ, Φ = [ar (θ˜1 ) ⊗ at (θ˜1 ), . . . , ar (θ˜N ) ⊗ at (θ˜N )].

(7)

θ˜ = {θ˜1 , θ˜2 , . . . , θ˜N } is a ﬁxed and uniform grid in angle range of [−90◦ , 90◦ ], where N denotes the grid number. The traditional on-grid model assumes that the real DOAs {θ1 , θ2 , . . . , θk } is a subset of {θ˜1 , θ˜2 , . . . , θ˜N }. When K < Mt Mr 0 and Λ = diag(α) being the covariance matrix. It is proved that all columns of X are independent and share the same sparse prior [22]. (3) Oﬀ-Grid Oﬀset Model: We assume Δ follows a uniform prior 1 1 Δ ∼ U([− r, r]N ), 2 2

(19)

with r > 0 and r being the initialized grid interval. By combining all stages of the hierarchical Bayesian model, the joint PDF is formulated as p(X,Y ,α,β,Δ) = p(Y |X, β, Δ)p(X|α)p(α)p(β)p(Δ).

(20)

The distributions on the right side of the equation are deﬁned by (16), (17), (18), (15) and (19), respectively. (4) Bayesian Inference: According to the chain rule, p(X, α, Δ, β|Y ) = p(X|Y , α, Δ, β)p(α, Δ, β|Y ).

(21)

It is derived that the posterior probability distribution of X obeys complex Gaussian distribution, i.e. p(X|Y , α, β, Δ) =

T t=1

CN (x(t)|μ(t), Σ)

(22)

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with μ(t) = βΣΦH y(t), t = 1, . . . , T, Σ = (βΦH Φ + Λ−1 )−1 .

(23)

Note that α, β and Δ are needed to calculate Σ and μ(t). However, p(X, α, Δ, β|Y ) can not be solved explicitly. Thus, we estimate α, β and Δ by its maximum a posteriori estimation (MAP) ˆ Δ) = arg max p(α, β, Δ|Y ). (α, β, α ,β,Δ

(24)

Equation (24) is equivalent to maximum likelihood (ML) estimation problem ˆ Δ) = arg max p(α, β, Δ, Y ). (α, β, α ,β,Δ

(25)

Notice that (25) is equivalent to maximize ln p(α, β, Δ, Y ). We use the expectation maximization (EM) algorithm to iteratively maximize the lower bound of the marginal likelihood p(α, β, Δ, Y ) by treating X as a hidden variable ˆ Δ) = arg max E{log p(Y , X, α, β, Δ)}, (α, β, α ,β,Δ

(26)

where E{·} denotes the expectation with respect to the posterior of X as given in (22). Denote U = {μ(1), . . . , μ(T )} = βΣΦH Y , X = √XT , Y = √YT , U = √UT and ζ =

ζ T

. Then, the parameters are updated as follows n 1 + 4ζ(Σnn + U 22 ) − 1 new , αn = 2ζ βnnew =

(a − 1) + T Mr Mt b + T · E{ Y − ΦX 22 }

,

(27)

(28)

N with E{ Y − ΦX 22 } = Y − ΦU 22 +β −1 n=1 (1 − αn−1 Σnn ). For details, the readers can refer to [22]. For Δ, maximizing E{log p(Y |X, β, Δ)p(Δ)} is equivalent to minimizing E{

=

T 1 y(t) − Φx(t)22 } T t=1 T 1 y(t) − Φμ(t) 22 + tr{ΦΣΦH } T t=1

= ΔT P Δ + 2v T Δ + const,

(29)

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where P is a positive semi-deﬁnite matrix ˜H B ˜ (Σ + U U H )}, P = R{B

(30)

T ˜ H AΣ) ˜ − 1 ˜ H (y(t) − Bμ(t))}. ˜ v = R{diag(B diag(μ(t))B T t=1

(31)

As a result, the optimization problem of the grid oﬀset vector Δ is represented as Δnew = arg

min

Δ∈[− 12 r, 12 r]N

{ΔT P Δ + 2v T Δ}.

(32)

Let J be the above optimization problem, ∂J = 2(P Δ + v). ∂Δ

(33)

When P is invertible, we have 1 1 Δnew = −P −1 v ∈ [− r, r]N 2 2

(34)

by making partial derivative equal 0. Otherwise, we update one δn by ﬁxing the other entries of Δ at each step −v n − (P n )T−n Δ−n , δˆn = P nn

(35)

where (P n )T−n represents the rest except the n-th entry in the (P n )T vector. In order to constrain δn ∈ [− 12 r, 12 r], we have δnnew

⎧ ˆ ⎪ ⎨δn , = − 21 r, ⎪ ⎩1 2 r,

if if if

δˆn ∈ [− 12 r, 12 r]N ; δˆn < − 12 r; δˆn > 12 r.

(36)

With the grid oﬀset vector, the grid can be reﬁned as θ˜nnew = θ˜nold + δnnew .

(37)

Through the above analysis, problem (13) is solved. 3.2

The Proposed GADE Algorithm

(1) GADE : The proposed grid adaptive DOA estimation (GADE) algorithm is shown in Algorithm 1.

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Algorithm 1 GADE Input: Y , K ˆ , θ˜ Output: X ˜ α, β, r, set a, b, o = 0; 1: Initialize θ, 2: while Y − ΦX < threshold or o < max Outiter do 3: o = o + 1; //Update external loop iteration counter 4: Δ = 0, i = 0; ˜ ˜ B ˜ using current θ; 5: Calculate A, 6: while Y − ΦX < threshold or i < max Initer do 7: i = i + 1; //Update internal loop iteration counter 8: Update Φ according to (9) using current Δ; 9: Calculate Σ, μ according to (23) using current α, β, Φ; 10: Update α, β, Δ according to (27) (28) (34) (35); 11: end while 12: Update θ˜ using current θ˜ and Δ according to (37); 13: end while ˜ ˆ ← U , θ; 14: Return X

(2) Implement Details: In fact, we only need to update the grid oﬀset vector at the support. In other words, we only adaptively adjust the grid points closest to the real DOAs. In this way, we can reduce the N -dimensional calculation to K-dimensional calculation. Note that, in our algorithm, parameters are continually updated until the residual energy is less than the threshold or the number of iteration reach the maximum. The algorithm adjusts the grid based on the last estimation and makes a more accurate estimation based on the new grid. Therefore, the proposed algorithm has a strong grid adaptation capability. With the estimation of support, the corresponding coeﬃcients of support are reﬂected ˜ are the estimated DOAs. powers, and the corresponding grid directions in θ

4

Numerical Simulations

In this section, numerical simulation results are presented. The simulation parameters are given in Table 1. In all of the simulations, a narrowband monostatic MIMO radar system is considered. We compare the proposed method with the state-of-the-art MIMO DOA estimation methods, i.e. MIMO MUSIC [8], MIMO CAPON [10], MIMO Propagator Method (PM) [11] and MIMO OGSBI [18] which is regarded as one of the best methods for oﬀ grid problems. In each trial, target DOAs are generated within [−90◦ , 90◦ ] randomly. The evaluation metric is the root mean square error (RMSE) which can be calculated over R = 500 trials

K R 1 2 θkr − θˆkr 2 . (38) RMSE = RK r=1 k=1

where the superscript r refers to the r-th trial.

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MIMO MUSIC

MIMO PM

Proposed

Power (dB)

0

-10

-20

-30

-40

-80

-60

-40

-20

0

20

40

60

80

DOA (degrees)

Fig. 2. The spatial spectrum for DOA estimation Table 1. Simulation parameters Parameter

value

The number of snapshots T

100

The detection DOA range

[−90◦ , 90◦ ]

The number of transmitting antennas Mt 4

4.1

The number of receiving antennas Mr

4

The transmitting antennas space dt

0.5 wavelength

The receiving antennas space dr

2 wavelength

Spatial Spectrum

Firstly, to illustrate the accuracy of the proposed GADE method, we study the property of the spatial spectrum. Considering that there are three targets in far ﬁeld located at θ1 = −55.5426◦ , θ2 = −5.5364◦ and θ3 = 35.5279◦ with SNR = 0 dB. The grid is set from −90◦ to 90◦ with the uniform interval 10◦ . Figure 2 plots the normalized spatial spectrums. From Fig. 2, we can see that the proposed GADE method achieves the best spatial spectrum, which can be explained by the oﬀ-grid model and the strong grid adaptation capability. More speciﬁcally, the spatial spectrum of the proposed GADE is highly peaked at the true DOAs. In contrast, MIMO MUSIC and MIMO PM method only estimate the ﬁrst DOA at θ1 = −60◦ , but almost fail to estimate the rest DOAs. Therefore, we can conclude that the proposed GADE method is not only eﬀective for MIMO DOA estimation, but also enjoys a higher accuracy even initialized with a coarse grid.

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Robustness Against Measurement Noise

Secondly, we evaluate the impact of diﬀerent measurement noise levels on the estimation performance. Considering three signals in the far ﬁeld randomly impinging on the receiver from diﬀerent directions. We compare our method with three classical methods (MIMO MUSIC, MIMO PM, MIMO CAPON) and MIMO OGSBI. The grid interval for all methods is set to 1◦ . Figure 3 presents the RMSE results when SNR varies from 0 to 10 dB. It is obvious that the proposed method outperforms the classic MIMO DOA estimation methods and MIMO OGSBI. To be speciﬁc, MIMO MUSIC and MIMO CAPON show the same performance and they are more accurate than MIMO PM under all noise levels. However, the performance platform appears on all the three classic MIMO DOA estimation methods, which can be explained by the bottleneck of the on-grid model. On the contrary, the methods based on oﬀ-grid model, i.e. MIMO OGSBI and the proposed method, can break through the on-grid bottleneck and obtain more accurate estimation. Interestingly enough in Fig. 3, although both MIMO OGSBI and the proposed GADE method adopt the oﬀ-grid model, the RMSE of MIMO OGSBI is nearly constant to the noise levels, while the RMSE of the proposed GADE method continuously decreases as SNR increases. The reason lies that MIMO OGSBI just reﬁnes the grid once while the proposed GADE method allows continuous grid adjustment by powerful internal and external iterative grid adaptation procedures. Thus, we can conclude that the proposed GADE method achieves more accurate estimation and is more robustness to the measurement noise. 0.9 MIMO MUSIC MIMO CAPON MIMO PM MIMO OGSBI Proposed

0.8

RMSE

0.7 0.6 0.5 0.4 0.3

0

1

2

3

4

5

6

7

8

9

10

SNR (dB)

Fig. 3. The DOA estimation performance versus SNR

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1.3 1.2

RMSE

1.1 1 0.9 0.8 0.7 0.6 0.5 0.4

1

2

3

4

5

6

7

Grid interval (degrees)

Fig. 4. The DOA estimation performance versus grid interval

4.3

Sensitivity to Initial Grid Granularity

Finally, we investigate the sensitivity of the proposed GADE method and MIMO OGSBI to the initial grid granularity. We set the DOAs the same as in Sect. 4.2 and set SNR = 5 dB. Figure 4 shows the results when the grid interval is changed from ﬁne to coarse. It is observed from Fig. 4 that the proposed GADE method is tolerant to the grid granularity to some degree and outperforms the MIMO OGSBI method. In particular, the RMSE of MIMO OGSBI gradually increases as the grid interval increases, while the proposed GADE method is almost unaﬀected. With the grid interval increasing to 7◦ , the RMSE of MIMO OGSBI increases to 1.4070 while the RMSE of the proposed GADE method is still below 0.5. The reason is that MIMO OGSBI only adjusts grid once, which is only suitable for scenarios where the oﬀ-grid gap is relatively small, while the proposed GADE method continuously adjusts the initial grid which can continuously approach the real DOAs. Thus, it can be concluded that even with a coarse grid, the proposed GADE method can still achieve DOA estimation with a high angle resolution.

5

Conclusion

In this paper, in order to improve the estimation accuracy and solve the improper initial grid problem for DOA estimation in MIMO radar system, we established an dynamic grid model in angle domain. We constructed a hierarchical probability framework with Laplacian sparse prior for the model and proposed a novel grid adaptive DOA estimation method (GADE) from the perspective of sparse Bayesian learning. By updating the grid and the observation dictionary iteratively in the grid reﬁnement procedure, the grid points approach to the real

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target DOAs adaptively. Simulation results highlight the proposed method with high estimation accuracy and a good robustness against noise in MIMO DOA estimation. More importantly, the proposed method can still obtain a good angle resolution even initialized with a coarse grid.

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20. Ji S, Xue Y, Carin L (2008) Bayesian compressive sensing. IEEE Trans Signal Process 56(6):2346–2356 21. You K, Guo W, Liu Y, Wang W, Sun Z (2018) Grid evolution: joint dictionary learning and sparse Bayesian recovery for multiple oﬀ-grid targets localization. IEEE Commun Lett 22(99):2068–2071 22. Babacan S, Molina R, Katsaggelos A (2010) Bayesian compressive sensing using laplace priors. IEEE Trans Image Process 19(1):53–63

Global Deep Feature Representation for Person Re-Identiﬁcation Meixia Fu1,2,3(&), Songlin Sun1,2,3, Na Chen1,2,3, Xiaoyun Tong1,2,3, Xifang Wu1,2,3, Zhongjie Huang1,2,3, and Kaili Ni1,2,3 1

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China [email protected], [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China

Abstract. Person re-identiﬁcation (re-ID) has attracted tremendous attention in the ﬁeld of computer vision, especially in intelligent visual surveillance (IVS). The propose of re-ID is to retrieval the interest person across different cameras. There are still lots of challenges and difﬁculties that are the same appearance such as clothes, the lens distance, various poses and different shooting angles, all of which influence the performance of re-ID. In this paper, we propose a novel architecture, called global deep convolutional network (GDCN), which applies classical convolutional network as the backbone network and calculates the similarity between query and gallery. We evaluate the proposed GDCN on three large-scale public datasets: Market-1501 by 92.72% in Rank-1 and 88.86% in mAP, CHUK03 by 60.78% in Rank-1 and 62.47% in mAP, DukeMTMC-re-ID by 82.22% in Rank-1 and 77.99% in mAP, respectively. Besides, we compare the experimental results with previous work to verify the state-of-art performance of the proposed method that is implemented by NVIDIA Ge-Force GTX 1080Ti. Keywords: Person re-identiﬁcation Computer vision surveillance Global deep convolutional network

Intelligent visual

1 Introduction Recently, person re-identiﬁcation has increasingly become a popular task in academic world, which is also the basic research for high-level tasks such as action recognition, video understanding and pedestrian anomaly detection [1]. Re-ID aims to look for the pedestrian appearing under different cameras though given interest image. However, there are still improvement space due to the difﬁculties in this task, such as the similar appearance, various pose and illumination. Many prior methods have achieved well

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 179–186, 2020 https://doi.org/10.1007/978-981-13-9409-6_22

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GDCN backbone

FC Layer2

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Gallery Rank

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Fig. 1. The structure of Global Deep Convolutional Network (GDCN) for re-ID

performance for re-ID [2]. There are two mainly categories about them: hand-crafted and deep learning. Hand-crafted applies the methods based-image processing into reID. However, we focus primarily on presenting the previous work of re-ID based on deep learning. Many prior methods have achieved well performance in person re-ID [3–7]. Barbosa et al. [3] proposed an inception-based network, SOMAnet capturing structural aspects of the human body, which showed robust performance under complex appearance. Zhao et al. [4] showed a novel Spindle Net to capture competitive fusion features though extracting body region features and global features. It adopted global pooling layer to transfer feature maps to feature vector. Su et al. [5] explicitly proposed a pose-driven Deep Convolutional (PDC) model to leveraged the global features based on a global network and local features based on a pose driven feature weighting network, in which global pooling was used. Zheng et al. [6] introduced a pedestrian alignment network (PAN) that could address misalignment problem with extra annotations, in which the global pooling was applied on the feature maps. Wei et al. [7] proposed a Global-Local-Alignment Descriptor (GLAD) that integrated global and local features and an efﬁcient retrieval framework that gathered top-k groups in the gallery set to decrease large redundancy. GLAD still applied global average pooling to classiﬁcation. The mentioned methods based on global-local features with the global pooling perform well in the task of re-ID. In this paper, we propose a novel GDCN, which represents the feature though a simple global deep convolutional network and calculate the similarity using cosine similarity function. Besides, the performance among four popular deep convolutional networks that achieved excellent results on ImageNet is compared to choose the backbone network. The reminder of this paper is organized as follows. In Sect. 2, we closely introduce the structure of the proposed GDCN and the evaluation method. In

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Sect. 3, we present the experimental results of the proposed method on three public datasets and compare with prior work. In Sect. 4, we give a conclusion about this paper and show the future plan.

2 The Proposed Method The structure of the proposed GDCN is shown in Fig. 1, which consists of two components that are the training structure and the testing structure. The training structure includes a X-net as the backbone network, the global average pooling (GAL) and two fully connection (FC) layers. Here, X-net is listed four popular deep convolutional networks: Inceptionv3 [8], Resnet50 [9], Resnet101 [9], Densenet121 [10]. We compare the performance among them for re-ID. For testing, the feature (batchsize, 2048) after GAL is the input of the distance metric. Query is the target dataset and gallery is the retrieval dataset. Then we calculate the similarity between them that share parameters in feature extraction and order gallery from large to small according the similarity score. For training, the loss function is the cross-entropy to compute one pedestrian image within one batch: Loss FunctionðX; labelÞ ¼

K X

Loss FunctionðX; labelÞk

k¼0

¼

K X

logðsoftmaxðXlabel ÞÞ

k¼0

¼

K X k¼0

exlabel log PN xn n¼0 e

ð1Þ

!

where, X is the output of the network and label is the target class. Xlable is the output that corresponds to the label in k loss function. N is the amount of the identiﬁes. K is the value of the batchsize. A similarity measure is a popular tool to evaluate the similarity between two vectors [11]. The function is introduced as follows: similarity ¼

Pn AB i Ai Bi qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ Pn Pn k AkkBk 2 2 ð A Þ i i i ðB i Þ

ð2Þ

Here, A and B are the two vectors, the dimension of which is n. A and B is the norm. The value of similarity is limited between 0 and 1. In this paper, A and B are the feature vectors before the ﬁrst FC layer. We use the Cumulated Matching Characteristics (CMC) and the mean average precision (mAP) to evaluate the performance of the proposed method on three public datasets [12]. For CMC, we present the cumulated matching accuracy at Rank-1, Rank-5 and Rank10 to show the match accuracy among query and gallery. For mAP, we calculate the average match accuracy with single-query under considering recall accuracy.

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3 Experiments In this section, we present many experiments of the proposed method on three largescale datasets and compare with the prior work. In our experiments, all the images are resized to (384, 192) sent to the model. The value of batchsize is set to 32. The training times are set to 100 epochs. The initial learning rate is 0.1 and decays by a factor of 0.1 every 40 epochs. 3.1

Datasets

We evaluate the performance of the proposed method on three large-scale public person re-identiﬁcation datasets. The evaluation results would be calculated at singlequery for these three datasets with and without re-ranking method in [13]. Three datasets are shown in Fig. 2. The ﬁrst is Market1501 [12] that contains 36,036 images of 1501 identities captured from 6 cameras. It has 32,668 labeled bounding boxes and 3368 query images.

Market1501

CUHK03-detected

DukeMTMC-re-ID

Fig. 2. Sample images of Market1501, CUHK03-detected, DukeMTMC-re-ID

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This dataset is divided into two parts including training set that has 12,936 images from 751 identiﬁes and testing set that has 19,732 gallery images and 3368 query images from another 750 identiﬁes. The second is CUHK03 [14] that contains 14,096 images of 1467 identiﬁes captured from 5-pair cameras. In order to obtain test protocol like Market1501, Zhun et al. [13] organized the dataset into two categories: “detected” data and “labeled” data. In this paper, we choose “detected” data that has 7364 images of 767 identiﬁes for training set, and 1400 query images and 5332 gallery images of another 700 identiﬁes for testing set. The third is DukeMTMC-re-ID [15] that has 36,411 images of 1812 identiﬁes from 8 cameras. This dataset is divided into training set that has 16,522 images of 702 identiﬁes, and testing set that has 2228 query images and 17,661 gallery images of another 702 identiﬁes and 408 distractor identiﬁes. 3.2

Comparison of Four Backbone Networks on GDCN

In this section, we compare the performance of GDCN with four backbone networks on Market1501, CUHK03-detected and DukeMTMC-re-ID. Rank accuracy (%) and mAP (%) are listed in Table 1. On Market1501, Densenet121 achieves the best performance with Rank-1 accuracy of 90.94% and mAP of 75.64% that are higher 1.54% and 1.27% than Resnet101 but higher 12.35% and 22.31% than Resnet50, and 25.56% and 40.57% than Inceptionv3. That means as the network deepens, we achieve more excellent performance on Market1501. Besides, we evaluate this dataset with Densenet121-reranking and obtain Rank-1 accuracy of 92.72% and mAP of 88.86%.

Table 1. Comparison of GDCN with four backbone networks on Market1501, CUHK03detected and DukeMTMC-re-ID. Rank accuracy (%) and mAP (%) are listed Market1501 Model Inceptionv3 Resnet50 Resnet101 Densenet121 Resnet101_rerank Densnet121_rerank

Rank-1 65.38 78.59 89.81 90.94 92.48 92.72

CUHK03-detected mAP 35.07 53.33 74.37 75.64 88.30 88.86

Rank-1 10.18 41.71 50.57 41.78 60.78 51.28

mAP 3.61 37.02 44.93 38.33 62.47 53.93

DukeMTMC-reID Rank-1 mAP 64.18 41.99 66.38 46.69 68.13 45.74 77.42 58.49 76.61 68.98 82.22 77.99

On CHUK03D-detected, Resnet101 achieves the best performance with Rank-1 accuracy of 50.57% and mAP of 37.02% that are higher 8.79 and 6.66% than Densenet121, which is a bit unexpected comparing with Market1501. The most possible reason is that the value of identiﬁes between them are close but the images amount of Market1501 is ﬁve times as much as CHUK03D-detected. Moreover, we evaluate this dataset with Resnet101-reranking and obtain Rank-1 accuracy of 60.78% and mAP of 62.47%. On DukeMTMC-re-ID, Densenet121 still obtains the best performance with Rank-1 accuracy of 77.42% and mAP of 58.49%. We also obtain Rank-1 accuracy of

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82.22% and mAP of 77.99% using Densenet121-reranking. The experimental results on three datasets demonstrate the state-of-the-art performance of the proposed GDCN. 3.3

Comparison with Prior Methods

In this section, we compare our proposed method with prior methods based on deep leaning on three datasets. On Market1501, we list four methods, like SOMAnet [3], Spindle [4], PAN [6], AWTL [16]. AWTL integrated a convolutional neural network and an adaptive weighted triplet loss for re-ID. Our proposed method GDCN exceeds Rank-1 accuracy of 3.26% and mAP of 13.19% compared with AWTL that achieved the highest score in global feature group. On CUHK03-detected, there are three models that we present: PAN [6], SVDNet [17] and HA-CNN [18]. HA-CNN jointed learning of soft pixel attention and hard regional attention for feature representations based on deep learning. GDCN achieves Rank-1 accuracy of 60.78% and mAP of 64.27% that increase by 16.38 and 21.47% compared with HA-CNN. On DukeMTMC-re-ID, three previous methods are shown, such as PAN [6], SVDNet [17], AWTL [16]. GDCN obtains Rank-1 accuracy of 82.22% and mAP of 77.99%, which exceed 2.42 and 14.59% compared with AWTL. Hence, we verify the simple and effective performance of our method on three large-scale datasets (Table 2).

Table 2. Comparison with the previous work on Market1501, CUHK03-detected and DukeMTMC-re-ID Market1501 Model SOMAnet [3] Spindle [4] PAN [6] AWTL [16] GDCN (ours) CUHK03-detected Model PAN [6] SVDNet [17] HA-CNN [18] GDCN (ours) DukeMTMC-re-ID Model PAN [6] SVDNet [17] AWTL [16] GDCN (ours)

Rank-1 73.87 76.90 82.81 89.46 92.72

Rank-5 88.03 91.50 93.53 – 96.08

Rank-10 92.22 94.60 97.06 – 97.20

mAP 47.89 – 63.35 75.67 88.86

Rank-1 36.29 41.50 44.40 60.78

Rank-5 55.50 – – 73.00

Rank-10 75.07 – – 80.07

mAP 34.00 37.30 41.00 62.47

Rank-1 71.59 76.70 79.80 82.22

Rank-5 83.89 86.40 – 89.90

Rank-10 90.62 89.90 – 92.91

mAP 51.51 56.80 63.40 77.99

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4 Conclusion In this paper, we have proposed a global deep convolutional network that has achieved excellent performance on Densenet121 and Resnet101 comparing with other two shallow backbone networks. Besides, we compare the experimental results with previous work to verify the state-of-art performance of the proposed method. An adaptive model for feature extraction is an important task for re-ID. In the future, we intend to focus on attention mechanism and the correlation among local features. Acknowledgements. This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References 1. Bedagkar-Gala A, Shah SK (2014) A survey of approaches and trends in person reidentiﬁcation. Image Vis Comput 32(4):270–286 2. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identiﬁcation: past, present and future. arXiv preprint arXiv:1610.02984 3. Barbosa IB, Cristani M, Caputo B et al (2018) Looking beyond appearances: synthetic training data for deep CNNS in re-identiﬁcation. Comput Vis Image Underst 167:50–62 4. Zhao H, Tian M, Sun S et al (2017) Spindle net: person re-identiﬁcation with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085 5. Su C, Li J, Zhang S et al (2017) Pose-driven deep convolutional model for person reidentiﬁcation. In: Proceedings of the IEEE international conference on computer vision, pp 3960–3969 6. Zheng Z, Zheng L, Yang Y (2018) Pedestrian alignment network for large-scale person reidentiﬁcation. IEEE Trans Circ Syst Video Technol 7. Wei L, Zhang S, Yao H et al (2017) Glad: global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 2017 ACM on multimedia conference. ACM, pp 420–428 8. Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826 9. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 10. Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708 11. Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math Comput Model 53(1–2):91–97 12. Zheng L, Shen L, Tian L et al (2015) Scalable person re-identiﬁcation: a benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124 13. Zhong Z, Zheng L, Cao D et al (2017) Re-ranking person re-identiﬁcation with k-reciprocal encoding. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3652–3661

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14. Li W, Zhao R, Xiao T et al (2014) Deepre-ID: deep ﬁlter pairing neural network for person re-identiﬁcation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152–159 15. Ristani E, Solera F, Zou R et al (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision. Springer, Cham, pp 17– 35 16. Ristani E, Tomasi C (2018) Features for multi-target multi-camera tracking and reidentiﬁcation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6036–6046 17. Sun Y, Zheng L, Deng W et al (2017) SVDNet for pedestrian retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 3800–3808 18. Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identiﬁcation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285– 2294

Hybrid Precoding Based on Phase Extraction for Partially-Connected mmWave MIMO Systems Mingyang Cui1 , Weixia Zou1,2(B) , and Ran Zhang1 1

Key Laboratory of Universal Wireless Communications MOE, Beijing University of Posts and Telecommunications, Beijing 100876, People’s Republic of China [email protected] 2 State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, People’s Republic of China

Abstract. Millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO) has been regarded as an attractive solution for the next generation of communications. Restricted by the hardware and energy consumption, a hybrid analog and digital precoding structure is widely adopted. However, high-computational complexity is the fundamental restrictions of the most existing hybrid precoding schemes. To overcome these limitations, this paper proposes a high-performance hybrid precoding algorithm for partially-connected mmWave MIMO systems. Due to the special partially-connected structure, we decompose the analog precoding problem into a series of optimization problems. For each subproblem, we use the method of phase extraction to optimize one column of analog precoding matrix. Then the digital precoding matrix is obtained based on the least square algorithm. Simulation results verify that the proposed algorithm outperforms the existing ones.

Keywords: mmWave extraction

1

· MIMO · Partially-connected · Phase

Introduction

Wireless data traﬃc is projected to skyrocket 1000-fold by the year 2020 [1], thus promoting the development of the ﬁfth generation (5G) concept to cope with the requirements of high-data-rate applications [2]. The explosive growth of mobile traﬃc has signiﬁcantly aggravated the spectrum congestion of traditional frequency bands. In this context, millimeter wave (mmWave) massive multipleinput multiple-output (MIMO) has drawn global attention due to its wide band and high transmission rate [3,4]. With the large dimension of antenna for mmWave massive MIMO, full digital precoding structure, which requires the allocation of individual radio frequency c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 187–195, 2020 https://doi.org/10.1007/978-981-13-9409-6_23

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(RF) chain to each data stream, is unfeasible due to the hardware constraint and power consumption. The hybrid precoding architecture is more practical and cost eﬀective to deploy, which uses a combination of analog precoder in the RF domain, associated with digital precoder in the baseband [5,6]. According to the mapping from RF chains to antennas, two hybrid precoding structures have drawn much attention, which can be categorized into the fullyconnected and partially-connected structures. Since the ﬁrst one can get full beamforming gain for each transceiver, it has become the focus of research [7–9]. In [7], exploiting the sparse structure of mmWave channels, the hybrid precoding problem is approximately solved by minimizing the Euclidean distance between hybrid precoding matrix and optimal digital precoding matrix. Based on the spatial sparseness of mmWave, [8] provides a new method of building the joint RF and baseband precoder that reduces the computation complexity and enables highly parallel hardware architecture. In addition, the authors of [9] formulate the hybrid precoder design as a matrix factorization problem, and propose an iteration algorithm based on manifold optimization. Partially-connected structure is also widely concerned by researchers because of its simple structure and easy implementation [10, 11]. The authors of [10] ﬁrst decompose hybrid precoding optimization problem into a series of sub-rate optimization problems, and design a hybrid precoding algorithm based on successive interference cancelation (SIC), which can achieve desired results. Moreover, [9] proposes a semideﬁnite relaxation based AltMin (SDR-AltMin) algorithm, which is the ﬁrst eﬀort directly optimizing the hybrid precoders in such a structure. In this paper, we propose a hybrid precoding algorithm for partiallyconnected structure based on phase extraction (HPP-PE). Firstly, we decompose the analog precoding problem into a series of subproblems. Secondly, we complete the design of analog precoder through the method of phase extraction to optimize each column of analog precoding matrix. Finally, we obtain the digital precoding matrix by using the least square algorithm. According to the simulation results, we show that the proposed algorithm outperforms the existing ones. In the case that the channel state information (CSI) is imperfect, we show that the proposed HPF-PE algorithm also has superior adaptabilities. We use the following notation throughout this paper: A, a and a represent a matrix, a vector, and a scalar, respectively. E(·) denotes the expectation of a complex variable. angle(·) is the phase of a complex number. Ai,j is the entry on the ith row and jth column of A. AT , AH and |A| are its transpose, conjugate transpose and determinant of A, respectively. AF is its Frobenius norm. Tr(A) indicates the trace. IN is the N × N identity matrix.

2

System Model

In this paper, we focus on the downlink of a single-user massive MIMO system using hybrid analog and digital precoding and present the system model and channel model.

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2.1

System Model

RF Chain

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N RF . .

RF Chain Analog Precoder FRF

Chain

. . .

Nt

.. . M

.

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Fig. 1. Block diagrams of mmWave single-user system

Consider a single-user mmWave MIMO system using a hybrid structure as shown in Fig. 1 [10,11], where the transmitter is equipped with Nt = NRF × M antennas but only NRF independent RF chains to simultaneously transmit Ns data streams, which is subjected to Ns ≤ NRF ≤ Nt . The transmitted symbols are ﬁrstly processed by an NRF × Ns digital precoder FBB , then pass through RF chains. After that, the symbols are precoded by an Nt × NRF RF precoder FRF before transmission. Since FRF is implemented using analog phase shifters, its elements are constrained to satisfy |[FRF ]m,n |2 = 1. The normalized transmit 2 power constraint is given by FRF FBB F = Ns . Then the transmitted signal can be written as (1) x = FRF FBB s, where s is the Ns × 1 signal vector such that E[ssH ] = N1s INs . We consider a narrow band block-fading propagation channel which yields the received signal √ (2) y = ρHFRF FBB s + n, where ρ represents the average power of the received signal, and n is the additive white Gaussian noise vector of i.i.d. CN (0, σn2 ). H is the Nr ×Nt channel matrix, 2 satisfying the constraint E[HF ] = Nr Nt , where Nr is the number of antennas of the receiver. In this study, we implicitly assume that the perfect CSI is known at both transmitter and receiver, which can be eﬃciently obtained by channel estimation at the receiver and further shared at the transmitter [12]. When the Gaussian signals are transmitted via the mmWave channel, the spectral eﬃciency of the system is given by [10,11] R(FRF , FBB ) = log2 (|INs +

ρ H H HFRF FBB FH BB FRF H |). Ns σ 2

(3)

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Channel Model

According to the mmWave channel measurement results, the large-scale antenna array structure transceiver leads to high correlation of the antenna, and the number of propagation paths is much smaller than that of transmission antennas. Therefore, the mmWave channel will not obey the Rayleigh distribution. In this paper, we adopt the Saleh-Valenzuela model with a ﬁnite scattering to characterize the mmWave MIMO channel [13]. If the antenna elements are modeled as being ideal sectored elements, the channel matrix is given as Ncl N ray Nt Nr H αi,j ar (ϕri,j )at (ϕti,j ) , (4) H= Ncl Nray i=1 j=1 where Ncl and Nray represent the number of clusters and the number of rays per cluster, respectively. αi,j is the complex gain of the jth ray in the ith cluster, which is a random identically distributed random variable that follows the i.i.d 2 2 ). σα,i denotes the power of cluster and the powers of clusters are CN (0, σα,i Ncl 2 subject to i=1 σα,i = Ncl . In this paper, the half-wavelength uniform linear array (ULA) is used. The expression of the array response vector is given by 1 aULA (ϕ) = √ [1, ejπ sin(ϕ) , . . . ejπ(Nt −1) sin(ϕ) ]T . Nt

3

(5)

Hybrid Precoding for the Partially-Connected Structure Based on Phase Extraction (HPP-PE)

As shown in [7,9], the design of precoder and decoder can be separated into two subproblems, i.e., the precoding and decoding problems. Therefore, we will mainly focus on the precoder design in the remaining part of this paper and the algorithm proposed in this paper can be equally applied for the decoder. The problem of precoding can be expressed as arg min FRF ,FBB ⎧ ⎨ s.t. ⎩

2

Fopt − FRF FBB F (FRF )i,j = 1, ∀i, j,

(6)

||FRF FBB ||2F = Ns ,

where Fopt stands for the optimal digital precoder. 3.1

Analog Precoder Design of HPP-PE

According to the special properties of partially-connected structure, we can write the analog precoder FRF as follows: ⎡ ⎤ p1 0 · · · 0 ⎢ 0 p2 · · · 0 ⎥ ⎢ ⎥ FRF = ⎢ . . . (7) . ⎥. ⎣ .. .. . . .. ⎦ 0 0 · · · pNRF

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For convenience of analysis, we write FBB as a block matrix and each element of the block matrix corresponds to the M lines of the FBB , that is, FBB = T [q1 , q2 , . . . , qNRF ] . Similarly, we rewrite the optimal digital precoder as Fopt = T T [F1 , F2 , . . . , FNRF ] . We can obtain FRF FBB = [p1 q1 , p2 q2 , . . . , pNRF qNRF ] . So question (6) can be reformulated into a series of optimization problems 2

Fi − pi qi F , 1 ≤ i ≤ NRF .

arg min pi ,qi

(8)

We ﬁrst design an analog precoding matrix, assuming that the digital precoding matrix is ﬁxed. Taking the ith optimization problem as an example, (8) can converted as Fi − pi qi F = Tr[(Fi − pi qi )(Fi − pi qi )H ] 2 2 = Fi F + M qi F − 2Tr(pi qi FH i ). 2

2

(9)

2

Since Fi F and qi F are constants, the optimization problem (9) can be translated into Tr(pi qi FH (10) arg max i ), 1 ≤ i ≤ NRF . pi

Therefore, one solution is obtained by using phase extraction method, namely pi = e−j×angle(qi Fi ) , 1 ≤ i ≤ NRF . H

(11)

After calculating all pi (1 ≤ i ≤ NRF ), we can obtain the analog precoding matrix FRF . 3.2

Digital Precoder Design of HPP-PE

For given analog precoder, we can design digital precoder to improve spectral eﬃciency for transmitter. The problem of digital precoder can be written as arg min FBB

2

Fopt − FRF FBB F .

(12)

Then the objective function in (12) can be further recast as 2

Fopt − FRF FBB F H = Tr[(Fopt − FRF FBB ) (Fopt − FRF FBB )] 2 H H H H = Fopt F + Tr(FH BB FRF FRF FBB − Fopt FRF FBB − FBB FRF Fopt ).

(13)

We derive (13) with respect to FH BB and make the result equal to 0. The closed form solution of (12) can be obtained as −1 H FRF Fopt . FBB = (FH RF FRF )

(14)

Combining the design of analog precoder in 3.1 and the design of digital precoder in 3.2, the HPP-PE algorithm proposed in this paper can be described as follows.

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Algorithm 1. Hybrid Precoding for the Partially-connected Structure based on phase extraction (HPP-PE). Require: Fopt , NRF , M . 1: for i = 1 to NRF do 2: Obtain Fi , and Initialize pi ; −1 H pi Fi ; 3: qi = (pH i pi ) −j×angle(qi FH i ); 4: pi = e 5: end for 6: Reconstruct FRF with pi ; ¯ BB = (FH F )−1 FRF Fopt ; 7: F √ RF RFF ¯ BB 8: FBB = Ns ||F F 2 ; ¯ RF BB ||F Ensure: FRF , FBB .

As for the initialization of pi , the algorithm uses the phase of the correspondf , where Fi = [fi,1 , fi,2 , . . . , fi,Ns ]. ing column in Fi , such as pi = |Fi,i i,i | 3.3

Complexity Evaluation

In this subsection, we provide the complexity evaluation of diﬀerent algorithms based on the number of complex multiplications. For the proposed algorithm, we can observe that the main complexity comes from step 3 and 4. The main complexity of step 3 is O(M + M NRF ), and that of step 4 is O(M NRF ). Therefore, the complexity of the proposed HPP-PE algorithm is approximately O(Nt ). According to the description of [9], we can know that the complexity of the SDR-AltMin algorithm mainly comes from the previous calculations and the SDR. The complexity of just calculating C in the previous calculation is O(Nt2 ), which is higher than the total complexity of the proposed algorithm. The authors of [10] point out that the complexity of the SIC algorithm is O(NRF S + Nr )M 2 , where S is the number of iterations. It can be seen that the complexity of the proposed algorithm is slightly lower than that of the SIC algorithm.

4

Simulation Results

In this section, we show the performance achieved by proposed and reference algorithms. The default simulation parameters are described as follows. The transmitter with Nt = 144 antennas and sends Ns = 3 data streams to a receiver with Nr = 36 antennas, while both are equipped with ULA. The channel includes Ncl = 5 clusters and each cluster contains Nray = 3 rays. The azimuth and elevation AoDs and AoAs follow the Laplacian distribution with uniformly distributed mean angles over [0, 2π) and angular spread of 7.5◦ . All the reported results are the average of 500 random channel realizations.

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In Fig. 2, we provide spectral eﬃciency of the proposed HPP-PE algorithm, SDR-AltMin algorithm [9] and SIC algorithm [10] in the case of NRF = Ns = 3. Since the SIC algorithm can only design hybrid precoder at the transmitter, we assume that the optimal digital decoder is adopted at the receiver, which is also employed for the other algorithms. It can be seen that in all cases, the performance of the proposed algorithm exceeds the performance of the other two algorithms. Moreover, we evaluate the performance of diﬀerent algorithms with imperfect CSI. According to [14], the estimated channel matrix can be expressed as ˜ = δH + 1 − δ 2 Ψ (15) H where 0 ≤ δ ≤ 1 denotes the reliability of the estimated channel and Ψ is the noise matrix whose entries follow i.i.d CN (0, 1). Figure 3 plots the spectral eﬃciency of diﬀerent algorithms with diﬀerent CSI conditions for mmWave MIMO system. Compared with Fig. 2, it can be concluded that there is some performance loss for all algorithms in this case due to the error of channel estimation. It is observed that the proposed algorithm performs better than the other two algorithms under the same δ. Moreover, the performance of the algorithms will gradually deteriorate as δ decreases. Therefore, we need to estimate the CSI more accurately to ensure the performance.

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5

Conclusion

In this paper, we propose a hybrid precoding algorithm under the partiallyconnected structure. Using the special partially-connected structure, we decompose the analog precoding problem into a series of optimization problems and optimize each column of analog precoding matrix based on phase extraction method. Then we use the least squares solution to design digital precoder. The numerical results demonstrate signiﬁcant performance gains of the proposed algorithm over existing hybrid precoding algorithms. And it is still eﬀective even with imperfect CSI. In our future work, it is a worthy problem to investigate the multi-user MIMO scenarios and channel estimation. Acknowledgements. This work was supported by NSFC (No. 61571055), fund of SKL of MMW (No. K201815), Important National Science & Technology Speciﬁc Projects (2017ZX03001028).

References 1. Huang H, Song Y, Yang J, Gui G, Adachi F (2019) Deep-learning-based millimeterwave massive MIMO for hybrid precoding. IEEE Trans Veh Technol 68(3):3027– 3032 2. Xiao M, Mumtaz S, Huang Y, Dai L, Li Y, Matthaiou M, Karagiannidis GK, Bj¨ ornson E, Yang K, Chih-Lin I, Ghosh A (2017) Millimeter wave communications for future mobile networks. IEEE J Sel Areas Commun 35(9):1909–1935 3. Heath RW, Gonz´ alez-Prelcic N, Rangan S, Roh W, Sayeed AM (2016) An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Top Signal Process 10(3):436–453

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4. Chen K, Qi C (2019) Beam training based on dynamic hierarchical codebook for millimeter wave massive MIMO. IEEE Commun Lett 23(1):132–135 5. Han S, Chih-Lin I, Xu Z, Rowell C (2015) Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun Mag 53(1):186–194 6. Molisch AF, Ratnam VV, Han S, Li Z, Nguyen SLH, Li L, Haneda K (2017) Hybrid beamforming for massive MIMO: A survey. IEEE Commun Mag 55(9):134–141 7. Ayach OE, Rajagopal S, Abu-Surra S, Pi Z, Heath RW (2014) Spatially sparse precoding in millimeter wave MIMO systems. IEEE Trans Wireless Commun 13(3):1499–1513 8. Lee Y, Wang C, Huang Y (2015) A hybrid RF/baseband precoding processor based on parallel-index-selection matrix-inversion-bypass simultaneous orthogonal matching pursuit for millimeter wave MIMO systems. IEEE Trans Signal Process 63(2):305–317 9. Yu X, Shen J, Zhang J, Letaief KB (2016) Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE J Sel Top Signal Process 10(3):485–500 10. Gao X, Dai L, Han S, Chih-Lin I, Heath RW (2016) Energy-eﬃcient hybrid analog and digital precoding for mmwave MIMO systems with large antenna arrays. IEEE J Sel Areas Commun 34(4):998–1009 11. Du J, Xu W, Shen H, Dong X, Zhao C (2018) Hybrid precoding architecture for massive multiuser MIMO with dissipation: Sub-connected or fully connected structures? IEEE Trans Wireless Commun 17(8):5465–5479 12. Gonz´ alez-Coma JP, Rodr´ıguez-Fern´ andez J, Gonz´ alez-Prelcic N, Castedo L, Heath RW (2018) Channel estimation and hybrid precoding for frequency selective multiuser mmwave MIMO systems. IEEE J Sel Top Signal Process 12(2):353–367 13. Akdeniz MR, Liu Y, Samimi MK, Sun S, Rangan S, Rappaport TS, Erkip E (2014) Millimeter wave channel modeling and cellular capacity evaluation. IEEE J Sel Areas Commun 32(6):1164–1179 14. Zhang D, Wang Y, Li X, Xiang W (2018) Hybridly connected structure for hybrid beamforming in mmwave massive MIMO systems. IEEE Trans Commun 66(2):662– 674

Research on the Fusion of Warning Radar and Secondary Radar Intelligence Information Jinliang Dong1(&), Yumeng Zhang1, Baozhou Du2, and Xiaoyan Zhang2 1

Nanjing Research Institute of Electronics Technology, Nanjing, China [email protected] 2 Troops of No. 63850, Baicheng, China

Abstract. Based on the research of multi-sensor fusion tracking, combined with the working characteristics of warning radar and secondary radar, this paper pro-poses a point fusion and track fusion structure suitable for its engineering application and a speciﬁc fusion process. The track fusion algorithm proposed in this paper not only approaches the point fusion algorithm in tracking accuracy, but also retains the advantages of distributed fusion structure and has broad application prospects. The effectiveness and superiority of the algorithm are veriﬁed by related simulations. Keywords: Warning radar Secondary surveillance radar Plot fusion Track fusion Fusion tracking

1 Introduction Secondary radar has been widely used in many aspects such as air trafﬁc control, enemy and enemy identiﬁcation and target tracking. Its development is almost parallel with primary radar. The fundamental difference between the two is the different working methods. The primary radar relies on the target’s reflection of the electromagnetic waves emitted by the radar to actively detect the target and locate the target, while the secondary radar must rely on the cooperation of the interrogator and the target transponder. The secondary radar works in a challenge-response mode, and the detection and localization of the target is accomplished by two active radiated electromagnetic waves (one inquiry and one response). With the cooperation of the target transponder, the secondary radar has many advantages that the radar does not have [1]: (1) The response echo of the secondary radar is only attenuated by the one-way propagation distance, and its interrogation distance is only proportional to the square root of the transmission power. When the speciﬁed distance is reached, the secondary radar transmit power can be much smaller than the primary radar’s transmit power, and the volumetric quality is also much smaller. (2) Since the RF wavelengths of the RF and response RF are different, the ground clutter, meteorological clutter and Sinpo can be eliminated. (3) The secondary radar echo is independent of the effective reflection area of the target and the target attitude, and there is no target flicker. (4) The height data of the secondary radar is derived from the barometric altimeter and can obtain three-dimensional coordinate estimation without complicated technology. Its accuracy © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 196–205, 2020 https://doi.org/10.1007/978-981-13-9409-6_24

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is also higher than that of the primary radar. (5) Secondary radars can use coded signals to provide and exchange rich information such as identiﬁcation, compromise, and faults. The use of the target transponder allows the secondary radar to gain an advantage while limiting its scope of use, i.e. requiring the target to carry an answering machine (also known as a cooperative target). In order to make up for this shortcoming, the secondary radar and the warning primary radar are often used together, which not only can expand the coverage area of the monitored airspace, but also improve the tracking accuracy of the cooperation target in the public coverage area [2]. Literatures [3] and [4] analyzed the key technologies and system performance for the radar network data fusion system. Literatures [5] and [6] respectively introduce the application of dot fusion and track fusion algorithm in multi-sensor fusion. Combining the working characteristics of warning radar and secondary radar, this paper proposes a fusion tracking process for warning radar and secondary radar suitable for engineering applications.

2 Point Fusion of Warning Radar and Secondary Radar 2.1

Point-and-Shoot Fusion Structure of Warning Radar and Secondary Radar

The point data reported by the warning radar and the secondary radar to the fusion center is usually not synchronized, and since the warning radar mainly detects the remote target, the sampling data rate is not very high. According to the above characteristics, in the centralized system, we adopt the dot-strip merging algorithm which is more suitable for the fusion tracking of the warning radar and the secondary radar. The speciﬁc dotted fusion processing structure is shown in Fig. 1.

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Fig. 1. Point-and-shoot fusion structure of warning radar and secondary radar

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(1) After the alert radar detects the target, the target echo is signal processed to give a frame point. Then, the frame trace report is pre-processed such as defuzziﬁcation and dot combination, and ﬁnally the attraction trace report is generated and sent to the fusion center. After the secondary radar ﬁnds the target, it calculates the

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distance and azimuth of the target according to its response signal, solves the target code and altitude data, and then sends the target point report directly to the fusion center. (2) The fusion center spatially calibrates the point data of the warning radar and the secondary radar, that is, respectively transforms them into the reference coordinate system of the information fusion center. (3) The points sent to the fusion center are sequentially associated with the system track in the database according to certain criteria in the order of their detection time. In order to accurately complete the track-track interconnection task, it is necessary to predict the state of the system track at the moment of the occurrence of the trace, in preparation for the point-track interconnection. (4) For the points on the association, the idea of serial combination of points is adopted [7], and the system track is updated by Kalman ﬁltering technology to make the system track continue. For traces that are not associated, as a possible new target, in the following cycles, if there is no subsequent trace associated with it, it is considered a false marker and is eliminated according to certain criteria; otherwise, Start a new system track.

3 Track Fusion of Warning Radar and Secondary Radar 3.1

Track Fusion Structure of Warning Radar and Secondary Radar

In the distributed system, based on the idea of point-and-serial serial merging in centralized fusion tracking system, an alternative fusion algorithm for asynchronous trajectory in distributed fusion tracking system is proposed, and the warning radar and secondary radar are given. The track fusion process, the speciﬁc track fusion processing structure is shown in Fig. 2.

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Fig. 2. Track fusion structure of warning radar and secondary radar

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Track Fusion Process and Algorithm

(1) After the alert radar detects the target, the target echo is signal processed to give a frame point. Then, the frame trace report is subjected to pre-processing such as deblurring and dot combination to generate a scenic spot. The scenic spot is processed by the local tracker to form a radar track and sent to the fusion center.

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After the target is found by the secondary radar, the parameters such as the distance and azimuth of the target are calculated according to the response signal, and the code and height data of the target are solved. After the local tracker processes, the secondary radar track is formed and sent to the fusion center. (2) The warning radar and the secondary radar track are sent to the fusion center via the communication network, and they are spatially calibrated at the fusion center, that is, they are respectively transformed into the reference coordinate system of the information fusion center. (3) The local track sent to the fusion center takes the system track from the sliding window in the system track library in the order of its output time, and correlates according to certain criteria. In order to accurately complete the track-track interconnection task, the system track state needs to be extrapolated to the moment when the next reported local track may occur. (4) For the local track on the association, the following alternate asynchronous track fusion algorithm is used to update the system track. For the track that is not associated, it is programmed into the system track library as a new system track. The alternate asynchronous track fusion timing diagram is shown in Fig. 3. Here, the two local sensors are given, and the results can be directly extended to the multisensor fusion system. The local trackers 1 and 2 respectively give tracking tracks of the same target by the two local sensors, and the sampling period of each local track and the update period of the system track are not ﬁxed. Each local track is ﬁrst associated with the system track according to the order of arrival to the fusion center, and then the system track is updated in turn. In most cases, the sample timestamps for each local track are different, so they can alternately update the system track. If at some time (such as the time in Fig. 3), the sample timestamps of the two local tracks are equal (according to actual needs, when the time difference between the two local track samples is less than a certain threshold, it can be considered equal). We can ﬁrst simply combine the two local trajectories and then update the system trajectory with the fusion value [8].

Local Tracker1 System Track

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Fig. 3. Alternating asynchronous track fusion timing diagram

It is assumed that each local tracker and fusion center uses Kalman ﬁltering to estimate the target state. The track reports reported by each local tracker to the fusion center include: sample time stamp, target state estimation, and error covariance matrix.

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The state estimation value of each local track and its error covariance are used as the original measurement and error covariance when the system track is updated, and the matrix is HðkÞ I measured at this time. Set the system track update time to kTk , ^i ðkjkÞ of the k time sensor iði ¼ 1 or 2Þ and abbreviated as k. If the local state estimate X its error covariance matrix Pi ðkjkÞ are sent to the fusion center, the standard Kalman ﬁlter equation for the system track is: ^ ðkjk 1Þ ¼ Uðk; k 1ÞX ^ ðk 1jk 1Þ X

ð1Þ

Pðkjk 1Þ ¼ Uðk; k 1ÞPðk 1jk 1ÞUT ðk; k 1Þ þ Qðk; k 1Þ

ð2Þ

SðkÞ ¼ Pðkjk 1Þ þ Pi ðkjkÞ

ð3Þ

K ðk Þ ¼ Pðkjk 1ÞS1 ðk Þ

ð4Þ

^ ðkjkÞ ¼ X ^ ðkjk 1Þ þ K ðkÞ X ^i ðkjkÞ X ^ ðkjk 1Þ X

ð5Þ

PðkjkÞ ¼ Pðkjk 1Þ K ðk ÞSðk ÞK T ðk Þ

ð6Þ

¼ ½I K ðk ÞPðkjk 1Þ

4 Simulation and Performance Analysis In order to verify the performance of the algorithm, we use the Monte Carlo method to perform 500 simulations in Matlab environment. The duration of each simulation tracking is 50 s. The Monte Carlo simulation flow chart is shown in Fig. 4. The actual trajectory of the tracked target is shown in Fig. 5. Each target is uniformly accelerated in the X, Y, and Z directions. The initial states are: X1 ð0Þ ¼ 30; 000 m 400 m/s X2 ð0Þ ¼ 30; 000 m 420 m/s X3 ð0Þ ¼ 30; 000 m 380 m/s X4 ð0Þ ¼ 30; 000 m 300 m/s

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The multi-sensor fusion tracking system consists of a warning radar and a secondary radar. The system adopts a distributed structure. The root mean square error of the distance, azimuth and elevation angle of the warning radar is rq1 ¼ 130 m, rh1 ¼ 10 mrad, re1 ¼ 10 mrad, sampling start time t1 ¼ 0:5 s, sampling period T1 ¼ 1 s. The rms errors of the distance, azimuth and elevation angles of the secondary radar are rq2 ¼ 150 m, rh2 ¼ 13 mrad, re2 ¼ 6 mrad, sampling start time t2 ¼ 1 s, sampling period T2 ¼ 1 s. At the sampling center of the fusion center Tf ðkÞ ¼ kðk 1Þ and k is an integer, the two radars are asynchronously sampled, and the CA (even acceleration) model is used for the conversion measurement Kalman ﬁlter [9, 10].

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Simulation Start Setting Simulation times N Generating the Real Track of the Target Generating Measurements from the Real Track of the Target N=N-1 Plot Fusion

Genearting Local Tracks by Radar Tracking and Filtering

Track filtering and updating

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N=1 Yes

Calculating the RMSE of Plot Fusion and Track Fusion of each radar Output of Simulation Results End of Simulation

Fig. 4. Monte Carlo simulation flow chart

Figures 6, 7, 8, 9 and 10 are the warning radar, the secondary radar, and the radial distance, azimuth, elevation, and radial velocity of the target 1 using the above-mentioned dot fusion and track fusion algorithm. The sum of the acceleration root mean square error (RMSE) curve is similar to the target 1 for the other target tracking fusion. It can be seen from the ﬁgure that the tracking accuracy of the target 1 is better than that of the single radar by the point fusion and the track fusion algorithm, and can provide a more detailed description of the trajectory. Because the centralized fusion structure has high requirements on the communication bandwidth and the processing power of the fusion center, the distributed fusion structure is usually selected when the clutter or target is dense and the system is vigilant. A centralized fusion structure is used when there are few clutter and targets that require more precise tracking of the target. The track fusion algorithm given in this section

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not only approaches the point fusion algorithm in tracking accuracy, but also retains the advantages of distributed fusion structure, so it has broad application prospects.

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5 Conclusion This paper ﬁrst introduces the advantages of the combination of warning radar and secondary radar. Combined with the working characteristics of the warning radar and the secondary radar, this paper proposes a point fusion and track fusion structure suitable for its engineering application and a speciﬁc fusion process. Finally, through simulation, the advantages and disadvantages of the point fusion and track fusion algorithms are analyzed.

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References 1. Lynn PA (1987) Secondary radar. Radar systems. Springer, US 2. Tan Y, Yang J, Li L et al (2012) Data fusion of radar and IFF for aircraft identiﬁcation. J Syst Eng Electron 23(5):715–722 3. Ma K, Zhang H, Wang R et al (2018) Target tracking system for multi-sensor data fusion. Technology, networking, electronic & automation control conference. IEEE 4. Wang Y, Scharf LL, Santamaría I et al (2017) Canonical correlations for target detection in a passive radar network. In: Conference on signals, systems & computers. IEEE 5. Garcia F, Cerri P, Broggi A et al (2012) Data fusion for overtaking vehicle detection based on radar and optical flow. IEEE intelligent vehicles symposium. IEEE 6. Zhang B, Luo X, Lin H et al (2015) Researches on multiple-radar multiple-platform plot data fusion. Syst Eng Electron 37(7):1512–1517 7. Lee EH, Song TL (2017) Multi-sensor track-to-track fusion with target existence in cluttered environments. IET Radar Sonar Navig 11(7):1108–1115 8. Belmonte-Hernandez A, Hernandez-Penaloza G, Alvarez F et al (2017) Adaptive ﬁngerprinting in multi-sensor fusion for accurate indoor tracking. IEEE Sens J:1 9. Sobhani B, Paolini E, Giorgetti A et al (2017) Target tracking for UWB multistatic radar sensor networks. IEEE J Sel Top Sign Process 8(1):125–136 10. Belik BV, Belov SG (2017) Using of extended Kalman ﬁlter for mobile target tracking in the passive air based radar system. Procedia Comput Sci 103:280–286

Antenna Array Design for Directional Modulation Bo Zhang1 , Wei Liu2 , and Cheng Wang1(B) 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, College of Electronic and Communication Engineering Tianjin Normal University, Tianjin 300387, China [email protected], [email protected] 2 Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheﬃeld, Sheﬃeld S1 4ET, UK [email protected]

Abstract. Directional modulation (DM) has been applied to linear antenna arrays to increase security of signal transmission. However, only the azimuth angle is considered in the design, due to inherent limitation of the linear array structure, since linear antenna array lacks the ability to scan in the three dimensional (3-D) space. To solve the problem, planar antenna arrays are introduced in the design, where both the elevation angle and azimuth angle are considered. Moreover, a magnitude constraint for weight coeﬃcients is introduced. Design examples are provided to verify the eﬀectiveness of the proposed design.

Keywords: Directional modulation antenna array

1

· Magnitude constraint · Planar

Introduction

Directional modulation (DM) as a physical layer security technique was introduced to keep known constellation mappings in a desired direction or directions, while scrambling them for the remaining ones [1]. In [2], a four-element reconﬁgurable array was designed, and the DM design can be achieved by changing elements for each symbol. Then, the genetic algorithm based on phased antenna array was introduced to DM [3], where the same carrier frequency was used for all antennas. By changing the weight coeﬃcients properly for each symbol, DM design can be achieved, and its low bit error rate (BER) range is narrower than traditional beamforming design. In [4], directional antennas were used in the design to replace isotropic antennas, and the provided examples show that a narrower low BER range is achieved. Moreover, to solve the problem that both the eavesdroppers and the desired users will receive the same signal when they c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 206–213, 2020 https://doi.org/10.1007/978-981-13-9409-6_25

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are in the same direction of the antenna array, two positional modulation (PD) schemes were proposed. One introduces a reﬂecting surface [5], where the multipath eﬀect is exploited, and signals via both line of sight (LOS) and reﬂected paths are combined at the receiver side. The other is to use multiple antenna arrays [6], and the principle of the design is that the eavesdropper located in the same direction as the desired user for one antenna array may not be in the same direction for another antenna array. To increase the capacity of the DM system, a multi-carrier based phased antenna array design was proposed, employing an inverse Discrete Fourier Transform (IDFT) structure [7,8]. Another solution is to use crossed-dipole antenna array as the transmitter [9], where DM and polarisation were combined together in the proposed design. A method named dual beam DM was introduced in [10]. Diﬀerent from the traditional design where inphase and quadrature (IQ) components of signals are transmitted by the same antenna, dual beam DM design is to transmit these two components by diﬀerent antennas. In [11], the BER was employed for DM transmitter synthesis by linking the BER performance to the settings of phase shifters. A pattern synthesis approach was presented in [12,13], where information pattern and interference patterns are created together to achieve DM, followed by an artiﬁcial-noise-aided zero-forcing synthesis approach in [14], and a multi-relay design in [15]. An eightelement time-modulated antenna array with constant instantaneous directivity in desired directions was proposed in [16]. The main idea of the design is that the array transmits signals without time modulation in the desired direction, while transmitting time-modulated signals in other directions. Recently, the introduction of artiﬁcial noise has further advanced the directional modulation technology. Artiﬁcial noise (AN) can be divided into ‘static’ AN and ‘dynamic’ AN. Static AN means that the introduced AN vector is ﬁxed, so that the constellation points for the received signal in undesired directions do not change with time. As a result, after a long period of observation, it is possible for eavesdroppers to crack the received signal. To solve this problem, ‘dynamic’ AN is introduced, where the AN vector is continuously updated, and the constellation of the signal in the undesired direction changes constantly, increasing the diﬃculty of the eavesdroppers to decode the signal correctly. For the construction of AN, two methods were introduced. One is the orthogonal vector method [17,18], where the added AN vector is orthogonal to the steering vector of the desired direction. The other one is the AN projection matrix method [19, 20], where by designing an artiﬁcial noise projection matrix, the AN vector is projected into the zero space of the derivative of the desired direction. However, to our best knowledge, almost all of the existing studies are focused on one dimensional DM, which is normally achieved using linear antenna arrays, and these designs lack the ability to scan in the 3-D space. For eﬀective DM in the 3-D space, in this work, we introduce a planar antenna array based design for two-dimensional DM, where both the elevation angle and azimuth angle are studied.

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The remaining part of this paper is structured as follows. A review of planar antenna array based beamforming is given in Sect. 2. DM design for a uniform planar antenna array with the corresponding formulations is presented in Sect. 3. In Sect. 4, design examples are provided, with conclusions drawn in Sect. 5.

2

Review of Planar Antenna Array Based Beamforming

A narrowband planar antenna array for transmit beamforming is shown in Fig. 1, which consists of N equally spaced omni-directional antennas along the x-axis, and K equally spaced omni-directional antennas along the y-axis. The spacings from the ﬁrst antenna to its subsequent antennas along the x-axis and y-axis are represented by dx,n and dy,k , respectively for n = 0, . . . , N − 1 and k = 0, . . . , K − 1. The elevation angle θ ∈ [0◦ , 180◦ ], and azimuth angle φ ∈ [0◦ , 180◦ ] ∪ [0◦ , −180◦ ]. The weight coeﬃcient for the antenna on the n-th position of the axis and k-th position of the y-axis is denoted by wn,k (n = 0, . . . , N − 1 and k = 0, . . . , K − 1). The steering vector of the array as a function of angular frequency ω, elevation angle θ and azimuth angle φ, is given by s(ω, θ, φ) = [1, ejω(dx,0 sin θ cos φ+dy,0 sin θ sin φ)/c , . . . , ejω(dx,0 sin θ cos φ+dy,K−1 sin θ sin φ)/c , . . . ,

(1)

jω(dx,N −1 sin θ cos φ+dy,K−1 sin θ sin φ)/c T

e

] ,

where {·}T is the transpose operation, and c is the speed of propagation. For a uniform planar array (UPA) with a half-wavelength spacing (dx,n −dx,n−1 = λ/2 and dy,k − dy,k−1 = λ/2), the steering vector of the UPA is s(ω, θ, φ) = [1, ejπ(sin θ cos φ+sin θ sin φ) , . . . , ejπ(sin θ cos φ+(K−1) sin θ sin φ) , . . . ,

(2)

jπ((N −1) sin θ cos φ+(K−1) sin θ sin φ) T

e

] .

All weight coeﬃcients can be put together to form a vector represented by w, w = [wx0 ,yo , wx0 ,y1 , . . . , wx0 ,yK−1 , . . . , wxN −1 ,yK−1 ]T .

(3)

Then the beam response of the array is given by p(ω, θ, φ) = wH s(ω, θ, φ),

(4)

where {·}H represents the Hermitian transpose.

3

DM Design for the Uniform Planar Antenna Array

DM design is to keep the received signal following known constellation mappings in a desired direction or directions, while scrambling the phase and make the magnitude as low as possible for the rest of directions. The method to achieve DM

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is to ﬁnd the corresponding weight vector for each symbol. For M-ary signaling, such as multiple phase shift keying (MPSK), we assume the corresponding weight vector is given by wm = [wm,x0 ,y0 , wm,x0 ,y1 , . . . , wm,x0 ,yK−1 , . . . , wm,xN −1 ,yK−1 ]T ,

(5)

z d y,K

1

d y ,0 d x ,0 d x, N

y

1

x Fig. 1. An equally spaced planar array.

m = 0, . . . , M − 1. The desired response pm (ω, θ, φ) for the m-th constellation point, as a function of θ and φ is split into two regions: the mainlobe response and the sidelobe response, represented by pm,M L and pm,SL , respectively. Without loss of generality, we assume there are R elevation angles sampled for each azimuth angle φv (v = 0, 1, . . . , V − 1), and the desired directions in the 3-D space is θ0 , θ1 , . . . , θr−1 and φ0 . Then, we have pm,M L = [pm (ω, θ0 , φ0 ), pm (ω, θ1 , φ0 ), . . . , pm (ω, θr−1 , φ0 )], pm,SL = [pm (ω, θr , φ0 ), pm (ω, θr+1 , φ0 ), . . . , pm (ω, θR−1,φ0 ), pm (ω, θ0 , φ1 ), (6) . . . , pm (ω, θR−1 , φ1 ), . . . , pm (ω, θR−1 , φV −1 )]. As shown in (1), the steering vector of the array with a ﬁxed θ and φ is the same for all M constellation points. Therefore, we have steering matrix SSL for sidelobe regions and SM L for mainlobe directions, SM L =[s(ω, θ0 , φ0 ), s(ω, θ1 , φ0 ), . . . , s(ω, θr−1 , φ0 )], SSL =[s(ω, θr , φ0 ), s(ω, θr+1 , φ0 ), . . . , s(ω, θR−1,φ0 ), s(ω, θ0 , φ1 ), . . . , s(ω, θR−1 , φ1 ), . . . , s(ω, θR−1 , φV −1 )].

(7)

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For the m-th constellation point, its corresponding weight coeﬃcients can be obtained by solving the following linearly constrained optimisation problem min subject to

||pm,SL − wH m SSL ||2

(8)

wH m SM L = pm,M L ,

where ||·||2 denotes the l2 norm. The cost function in (8) is to keep the minimum diﬀerence between desired and designed sidelobe responses, and the equality constraint is to make sure that the response in the mainlobe directions exactly takes the speciﬁed constellation values. Here, we set the desired phase response in sidelobe regions randomly and the beam responses as low as possible (pm,SL ) to keep the received signal scrambled in the IQ complex plane. Moreover, to restrain the maximum value of weight coeﬃcient, we introduce the corresponding constraint ||wm ||∞ ≤ β,

(9)

where || · ||∞ represents the L-inﬁnity norm, and β is the pre-deﬁned maximum value for weight coeﬃcients. Therefore, the DM design with the weight coeﬃcient magnitude constraint is given by min subject to

||pm,SL − wH m SSL ||2 wH m SM L = pm,M L

(10)

||wm ||∞ ≤ β. The above problem can be solved using cvx in MATLAB, a package for specifying and solving convex problems [21, 22].

4

Design Examples

In this section, we consider an N × K = 21 × 20 uniform planar antenna array with a half wavelength spacing between adjacent antennas. Without loss

Beam pattern (dB)

0 -10 -20 -30 -40 Symbol 00 Symbol 01 Symbol 11 Symbol 10

-50 -60 -90

-60

-30

0

30

60

90

(degree)

Fig. 2. Resultant beam responses based on the UPA design in (10).

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180

Phase angle

135 90 45 0 -45 -90

Symbol 00 Symbol 01 Symbol 11 Symbol 10

-135 -180 -90

-60

-30

0

30

60

90

(degree)

Fig. 3. Resultant phase responses based on the UPA design in (10).

of generality, the desired elevation angle is 0◦ and azimuth angle is φ = 90◦ . The sidelobe regions are θSL ∈ [5◦ , 90◦ ] for φ = ±90◦ . The desired response in the mainlobe direction is a value of one (magnitude) with 90◦ phase shift (QPSK), i.e., √ √ √ √ √ √ √ √ 2 2 2 2 2 2 2 2 +i ,− +i ,− −i , −i (11) 2 2 2 2 2 2 2 2 for symbols ‘00’, ‘01’, ‘11’, ‘10’, and a value of 0.1 (magnitude) with random phase shifts over the sidelobe regions. The maximum value of weight coeﬃcient β = 0.1. Bit error rate (BER) is also calculated based on in which quadrant the received signal lies in the IQ complex plane, BER =

Error bits . T otal number of bits

(12)

BER of QPSK with awgn

100

Bit error rate

10-1 10-2 10-3 10-4 10-5 -90

-60

-30

0

30

Elevation angle ( degree) with a fixed

60

= 90 °

Fig. 4. BER based on the UPA design in (10).

90

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Here the signal to noise ratio (SNR) is set at 12 dB in the mainlobe direction, and then with the unit average power of all randomly generated 106 transmitted bits in the mainlobe, the noise variance σ 2 is 0.0631. We also assume that the additive white Gaussian noise (AWGN) level is the same for all directions, and a random noise with this power level is generated for each direction. The resultant beam pattern in (10) for each constellation point is shown in Fig. 2. Here we can see that all main beams are exactly pointed to the desired direction 0◦ with a low sidelobe level. As shown in Fig. 3, the phase in the desired direction 0◦ is 90◦ spaced, i.e., 45◦ , 135◦ , −135◦ and −45◦ for symbols ‘00’, ‘01’, ‘11’ and ‘10’, respectively, whereas the phase is random in the sidelobe directions. Moreover, Fig. 4 shows the BER for all transmission angles. It can be seen that in the desired direction BER is down to 10−5 , while it is around 0.5 in other directions, further demonstrating the eﬀectiveness of the design.

5

Conclusions

Directional modulation has been applied to uniform planar antenna arrays for the ﬁrst time, and two-dimensional directional modulation has been achieved eﬀectively by the proposed design method. As shown in the provided design examples, the mainlobe is pointing to the desired direction, with a low power level for the rest of the directions; simultaneously, the transmitted signal’s phase in the desired direction follows the required constellations, whereas its values in other directions are scrambled. The BER result shows that error bits received in the desired direction is the lowest, while in other directions the BER is about 0.5, indicating that it would be extremely diﬃcult for eavesdroppers located in these regions to crack the information. Acknowledgements. This work was supported by the Funding Program of Tianjin Higher Education Creative Team. The authors acknowledge the Natural Science Foundation of Tianjin City (18JCYBJC86000), and the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (2018KJ153) for funding this work. C.W. acknowledges the Distinguished Young Talent Recruitment Program of Tianjin Normal University (011/5RL153).

References 1. Babakhani A, Rutledge DB, Hajimiri A (2009) Near-ﬁeld direct antenna modulation. IEEE Microw Mag 10(1):36–46 2. Daly MP, Bernhard JT (March 2010) Beamsteering in pattern reconﬁgurable arrays using directional modulation. IEEE Trans Antennas Propag 58(7):2259– 2265 3. Daly MP, Bernhard JT (2009) Directional modulation technique for phased arrays. IEEE Trans Antennas Propag 57(9):2633–2640 4. Shi HZ, Tennant A (2013) Enhancing the security of communication via directly modulated antenna arrays. IET Microw Antennas Propag 7(8):606–611

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5. Zhang B, Liu W (2018) Antenna array based positional modulation with a two-ray multi-path model. In: Proceedings sensor array and multichannel signal processing workshop 2018 (SAM2018). Sheﬃeld, pp 203–207 6. Zhang B, Liu W (2019) Positional modulation design based on multiple phased antenna arrays. IEEE Access 7:33 898–33 905 7. Zhang B, Liu W (2018) Multi-carrier based phased antenna array design for directional modulation. IET Microw Antennas Propag 12(5):765–772 8. Zhang B, Liu W, Li Q (2019) Multi-carrier waveform design for directional modulation under peak to average power ratio constraint. IEEE Access 1–1 9. Zhang B, Liu W, Lan X (2019) Orthogonally polarized dual-channel directional modulation based on crossed-dipole arrays. IEEE Access 7:34 198–34 206 10. Hong T, Song MZ, Liu Y (2011) Dual-beam directional modulation technique for physical-layer secure communication. IEEE Antennas Wirel Propag Lett 10:1417– 1420 11. Ding Y, Fusco V (2013) Directional modulation transmitter synthesis using particle swarm optimization. In: Proceedings Loughborough antennas and propagation conference. Loughborough, pp 500–503 12. Ding Y, Fusco V (2013) Directional modulation transmitter radiation pattern considerations. IET Microw Antennas Propag 7(15):1201–1206 13. Ding Y, Fusco V (2015) Directional modulation far-ﬁeld pattern separation synthesis approach. IET Microw Antennas Propag 9(1):41–48 14. Xie T, Zhu J, Li Y (2017) Artiﬁcial-noise-aided zero-forcing synthesis approach for secure multi-beam directional modulation. IEEE Commun Lett PP(99):1–1 15. Zhu W, Shu F, Liu T, Zhou X, Hu J, Liu G, Gui L, Li J, Lu J (2017) Secure precise transmission with multi-relay-aided directional modulation. In: 2017 9th international conference on wireless communications and signal processing (WCSP), pp 1–5 16. Zhu QJ, Yang SW, Yao RL, Nie ZP (2014) Directional modulation based on 4-D antenna arrays. IEEE Trans Antennas Propag 62(2):621–628 17. Ding Y, Fusco V (2014) A vector approach for the analysis and synthesis of directional modulation transmitters. IEEE Trans Antennas Propag 62(1):361–370 18. Ding Y, Fusco V (2014) Vector representation of directional modulation transmitters. In: The 8th European conference on antennas and propagation (EuCAP 2014), pp 367–371 19. Hu J, Shu F, Li J (2016) Robust synthesis method for secure directional modulation with imperfect direction angle. IEEE Commun Lett 20(6):1084–1087 20. Hu J, Yan S, Shu F, Wang J, Li J, Zhang Y (2017) Artiﬁcial-noise-aided secure transmission with directional modulation based on random frequency diverse arrays. IEEE Access 5:1658–1667 21. Grant M, Boyd S (2008) Graph implementations for nonsmooth convex programs. In: Blondel V, Boyd S, Kimura H (eds) Recent advances in learning and control, ser. Lecture notes in control and information sciences. Springer Limited, pp 95–110. http://stanford.edu/∼boyd/graph$ $dcp.html 22. CVX Research: CVX: Matlab software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx (2012)

Capturing the Sparsity for Massive MIMO Channel with Approximate Message Passing Xudong Han1,3 , Shun Zhang1,3(B) , Anteneh Mohammed1,3 , Weile Zhang2 , Nan Zhao1,3 , and Yuantao Gu4 1

3

Xidian University, Xi’an 710071, People’s Republic of China xdhan [email protected], [email protected], [email protected], [email protected] 2 Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China [email protected] Dalian University of Technology, Dalian 116024, People’s Republic of China 4 Tsinghua University, Beijing 100084, People’s Republic of China [email protected]

Abstract. In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations. Keywords: Massive MIMO · Sparse Bayesian learning maximization · Approximate message passing

1

· Expectation

Introduction

Due to massive MIMO’s enhanced capacity and energy eﬃciency, it is a promising technology for the future-generation wireless cellular networks [1]. However, harvesting its full beneﬁt requires accurate channel state information (CSI). For frequency-division duplex (FDD) scenario, this would lead to huge overhead and will lessen the possible improvement. Fortunately, under some scenarios, the massive MIMO channel contains the sparsity [2]. In [3], Wen et al. proposed a channel estimation scheme based on sparse Bayesian learning methods for a multicell environment to reduce the pilot contamination. In [4], Gao et al. designed adaptive channel estimation and feedback scheme with low-overhead to exploit the spatially common sparsity and c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 214–222, 2020 https://doi.org/10.1007/978-981-13-9409-6_26

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temporal correlation in the channel. Our previous work [5] proposed a scheme for the time-varying massive MIMO channel tracking. However, the channel powers are closely related with the carrier frequency, which can not be perfectly inferred from the UL ones. This paper will further examine the spatial common sparsity and temporal correlation to design the eﬀective channel estimation algorithm for the FDD massive MIMO networks. Specially, with the virtual channel representation, the time-varying massive MIMO channel is reformulated. Furthermore, one dynamical state space (DSS) model will be constructed to depict the channel’s temporal correlation. Then, we will design the expectation maximization (EM) algorithm based sparse Bayesian learning (SBL) framework to capture the unknown parameters, where the powerful approximate message passing (AMP) will be utilized to track the posterior in the expectation step.

2

System Model

In this work, we will consider a single-cell massive MIMO system, where the BS is equipped with Nv × Nh antenna array in the form of uniform Rectangular Array (URA). K single antenna users are randomly distributed in the coverage area. We assume that the channel is quasi-static during a block of Lc channel uses and changes from block to block. During the m-th time block, the physical DL channel from the BS to the k-th user can be written as max

+∞ θk

ϕmax k

A(θ, ϕ)ej2πνmLTs k (θ, ϕ, ν)dθdϕdν,

Hk,m =

(1)

−∞ θ min ϕmin k k

where k (θ, ϕ, ν) is the joint angle-Doppler channel gain function of the k-th user corresponding to the Doppler frequency ν, the vertical and horizontal direction of departure (DOD) θ and ϕ; T1s is the system sampling rate. Moreover, A(θ, ϕ) denotes the BS’s array response vector with respect to the elevation angle θ and azimuth angle ϕ, and can be deﬁned as ⎤ ⎡ ... ej(Nh −1)h) 1 ej(h) ⎢ ej(v) ej[v+h] ... ej[v+(Nh −1)h] ⎥ ⎥ ⎢ A(θ, ϕ) = ⎢ (2) ⎥ .. .. .. .. ⎦ ⎣ . . . . ej(Nv −1)v ej[(Nv −1)v+h] . . . ej[(Nv −1)v+(Nh −1)h] 2πd where v = 2πd λ cos θ, h = λ sin θ cos ϕ, λ is the signal carrier wavelength, and d represents the antenna spacing. The channels from the BS to diﬀerent users are assumed to be statistically independent. As in [6], the virtual channel k,m = representation (VCR) can be utilized to dig the the sparsity of Hk,m as H k,m is the beam domain channel of Hk,m ; FN and FN FNv Hk,m FNh . where H v h are the normalized discrete Fourier transformation (DFT) matrices. In order

k,m = FT ⊗ to facilitate the operation, we rewrite the virtual channel as h Nh

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FNv vec Hk,m . Furthermore, we can adopt the simultaneously sparse signal k,m as [5] model to depict the dynamics of h k,m =diag(ck )rk,m , h (3) rk,m =αk rk,m−1 + υ k,m , where rk,m ∈ C Nh Nv ×1 represents one time-varying Gaussian Markov random processes, αk is the transmission factor, υ k,m ∼ CN (0, Λk ) is the noise vector, and the diagonal matrix Λk = diag([λ2k,1 , λ2k,2 , . . . , λ2k,Nh Nv ]). The spatial signature vector ck can be determined by the set Qk = {(p, q)| Nv λd (cos θk )min ≤ p ≤ Nv λd (cos θk )max , Nh λd (sin θk cos ϕk )min ≤ q ≤ d Nh λ (sin θk cos ϕk )max , p and q ∈ Z} as 1 if i = (q − 1)Nv + p, (p, q) ∈ Qk , ck,i = (4) 0 otherwise. ck and rk,m can With (3) and (4), the conditional be writ PDF of hk,m on

ten as p(hk,m |ck , rk,m ) = i∈Qk δ hk,m,i − rk,m,i i ∈Q / k δ hk,m,i . Further Nv Nh more we can achieve the prior distribution for hk,m as p(hk,m ) = i=1 [(1 − λ2k,i ρk )δ ]. hk,m,i + ρk CN hk,m,i ; 0, 1−α 2 k From the above equation, we can know that the statistics of the virtual k,m can be achieved through capturing the model parameter set Ξk = channel h {ρk , αk , Λk }.

3

Learning Sparse Virtual Channel Model Parameters Through DL Training

Without loss of generality, we use M channel blocks. During the m-th block, the BS transmit the training matrix Xm of size Nh Nv × P to all the users. Then, within the m-th block, the received training signal at user k can be collected into a P × 1 vector as

H k,m + nk,m , (5) yk,m = XTm FTNh ⊗ FNv h where nk,m ∼ CN (0, σn2 ) noise vector. Obviously, diﬀerent users can independently learn their prior model parameters and the spatial signature vectors. To simplify the notation, in the following, we will ignore the subscript k. 3.1

Problem Formulation

The objective of learning is to estimate the best ﬁtting parameters set Ξ with the given observation vector y. Theoretically, the ML estimator for Ξ can be formulated as = arg Ξ

max

1≥α≥0, λi ≥0, 1≥ρ≥0

ln p(y; Ξ).

(6)

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and Obviously, such estimator involves all possible combinations of the h is not feasible to directly achieve the ML solution due to its high dimensional search. Nonetheless, one alternative method is to search the solution iteratively via the classical EM algorithm. 3.2

Expectation Step

Since the received samples y are known, with some calculations, the objective

(l−1) can be expressed as function Q Ξ, Ξ Q

Ξ, Ξ

(l−1)

=

M

2α tr

m=2 2

−α tr +

−1

Λ

N

E

1− E

−1

Λ

E

H

rm−1 rm y, Ξ

rm−1 rm−1 y, Ξ H

ci |y, Ξ

(l−1)

(l−1)

(l−1)

−tr

ln (1 −ρ) + E

Λ 1 − α2

ci |y, Ξ

−1

(l−1)

E r1 rH 1

ln ρ

i=1

−

M

2

ln Λ + ln 1 − α

+ C1 ,

(7)

m=1

where C1 is the items not related with Ξ. (l−1) is dependent on four posFrom (7) it can be found that Q Ξ, Ξ terior statistics. Now we turn to the calculations of these terms. Before (l−1) , calculating posterior statistics, let us deﬁne ˆ c(l−1) = E c|y, Ξ (l−1) −1) (l (l−1) (l−1) , Θ (l−1) , and Π ˆ rm = E rm |y, Ξ = E rm rH m m |yk ,Ξ m−1,m = (l−1) for further use. E rm−1 rH m |y,Ξ 3.3

Deriving the Posterior Statistics with AMP

(l−1) , our objective is to infer the posterior statistics ˆ With given y and Ξ c(l−1) , (l−1) (l−1) (l−1) ˆ rm , Θm , and Π m−1,m under the state-space model: ˆ (l−1) rm−1 + υ m , rm = α

ym = XTm F∗Nh ⊗ FH Nv diag(c)rm + nm , m = 1, 2, . . . , M,

(8) (9)

Bm

where υ m = [υ T1,m , υ T2,m , . . . , υ Tτ,m ]T ∼ CN (0, Λ(l−1) ), and the elements of the sparse vectors are i.i.d. Bernoulli distributed with the parameter ρˆ(l−1) . As it is intractable to directly compute the required posterior statistics, we will resort to the factor graph and message passing algorithms. First, the posterior joint

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PDF can be factorized as

r|y; Ξ p c, h,

(l−1)

P M N m ∝ p ym,p |h p hm,i |ci , rm,i m=1 p=1

i=1

N (l−1) ˆ (l−1) ˆ , λi p ci ; ρˆ(l−1) , p rm,i |rm,i−1 ; α

(10)

i=1

r|y; Ξ (l−1) ) Fig. 1. The factor graph representation of p(c, h,

m) = CN(ym,p ; [Bm ]p,: h m , σ 2), p( where p(ym,p |h hm,i |ci , rm,i) = δ( hm,i − ci rm,i ), n (l−1) ˆ (l−1) (l−1) ˆ (l−1) )2 ), and p(ci ; ρˆ(l−1) ) = ˆ , λi ) = CN(rm,i ; α ˆ rm−1,i , (λ p(rm,i |rm−1,i ; α i 1−ci (l−1) ci

ρˆ . 1 − ρˆ(l−1) r|y; Ξ (l−1) ) can be denoted with a factor graph, as shown in Fig. 1, Then, p(c, h, Due to the belief cycles, BP cannot be directly applied for Fig. 1. Nonetheless, the proper message scheduling and approximate belief propagation algorithms can be adopted to eﬀectively approximate the posterior distribution within the given allowable iterations [7]. Specially, the message scheduling can be implemented through four steps, i.e., the message passing into the time block m, the message exchanging within the time blockm,themessageﬂowingoutofthetimeblock m,andthemessage exchanging between the adjacent time blocks. 3.4

Maximization Step

(l) . Due to the uncou (l−1) ) we will derive Ξ In this step, by maximizing Q(Ξ, Ξ pled structure in (7), we can break down the maximization into two subproblems: N (l) (l−1) (l−1) 1−ˆ c ln (1 − ρ) + ˆ c ln ρ (11) ρ = arg max ρ

α(l) ,Λ(l) = argmax α,Λ

i=1

M −1 (l−1) 2 −1 (l−1) 2α tr( {Λ Πm−1,m })−α tr(Λ Θm−1 )

m=2

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M −1 M

Λ (l−1) 2 2 . (12) − ln Λ+ln 1−α − −tr Θ1 ln Λ+ln 1−α 1−α2 m=1 m=1 Taking the derivatives of (11) and (12) with respect to ρ(l) , α(l) and Λ(l) , respectively, and equating them to zero, we can obtain the estimation of these parameters in the l-th iteration. Furthermore, the related posterior characteristics in (11)–(12) can be derived from the achieved results in the expectation step as follows.

M B →c ρˆ(l−1) m=1 πfm,i i = M

M (l−1) B →c + 1 − ρ B 1 − π ρˆ(l−1) m=1 πfm,i ˆ fm,i →ci m=1 i −1 1 1 1 (l−1) + + × rˆm,i = C B C νfm+1,i ν ν →rm,i fm,i →rm,i fm,i →rm,i C B →r C →r μfm+1,i μfm,i →rm,i μfm,i m,i m,i + + C B →r C →r νfm+1,i νfm,i →rm,i νfm,i m,i m,i −1 ! 2 1 1 1 (l−1) m = + + + rˆm,i Θ C B →r C →r νfm+1,i νfm,i νfm,i i,i →rm,i m,i m,i (l−1) cˆi

m−1,m ]i,i = [Π

(13)

(14)

(15)

ˆ (l−1) )2 μ C (¯ C μ νrm−1,i+(¯ μrm−1,i )2 )+(λ ¯rm−1,i α ˆ (l−1) νrm,i→fm,i rm,i →fm, i i ˆ l−1 )2 ) C (νrm,i →fm,i + (λ i

,

(16) where

μ ¯rm−1,i

ν¯rm−1,i = νrm,i =

νr νr

C ν0 m−1,i →fm,i

C +ν0 m−1,i →fm,i

, μrm,i =

ˆ (l−1) )2 C (λi m,i →fm,i ˆ (l−1) )2 νr C +(λi m,i →fm,i νr

ν0 μr

=

C +μ0 νr C m−1,i →fm,i m−1,i →fm,i

νr

C +ν0 m−1,i →fm,i (l−1) 2 (l−1) ˆ α ˆ rm−1,i νr ) μr C +(λi C m,i →fm,i m,i →fm,i (l−1) 2 ˆ νr +( λ ) C i m,i →fm,i

,

, and

.

To clearly get the main ideas of the EM-based parameter learning, we put the corresponding algorithm in Algorithm 1 and Algorithm 2.

4

Simulations Results

In this section, we will evaluate the performance of our proposed algorithms numerically. The antenna array size is Nh × Nv = 32 × 32. The signal-to-noise ratio (SNR) is deﬁned as SNR = 10 log10 λ2i 1−α2

σp2 2 σn

dB, and the variance of rk,m in (3)

= 1. We use the normalized MSEs for both the model parameters −x||2 . We set the and the virtual channel which is deﬁned as MSEx = E ||ˆx||x|| 2 is set as

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Algorithm 1 Parameter learning 1: Input: , σn2 , y, Bm , N, M, maximum number of EM iteration (Lmax ) ˆ ˆ ρˆ, Λ 2: Output: Ξk = α, ˆ i,i = 0.01, μ C 3: Initialize: ∀m and ∀i: α ˆ (0) = 1, ρˆ(0) = 0.25, [Λ] f →r1,i = 0, νf C 1,i

(0)

1, πci →f C = ρˆ , μf C = 0, νf C = ∞, πf C →ci = 0.5. 1,i m+1,i →rm,i m+1,i →rm,i m,i 4: for l = 1, 2, ..., Lmax do 5: for m = 1, 2, ..., M do 6: Pass belief about ci and rm,i into the m time block:

ρ ˆ(l−1)

πci →f B = m,i

π B f

m ,i

(1−π B f →ci m ,i m = m ν C ν C f →rm,i f →rm,i m,i m+1,i +ν C f

f C →rm,i m,i

μrm,i→f B =νrm,i→f C m,i

7: 8:

m =m

(1−ρ ˆ(l−1) )

νrm,i→f B = ν m,i

→rm,i

μ m+1,i

m,i

f C →rm,i m,i

ν C f

m,i

→rm,i

1,i →r1,i

→ci

)+ρ ˆ(l−1)

m = m

,

π B f

m ,i

→ci

,

μ C f

+ν

→rm,i m+1,i

.

fC →rm,i m+1,i

Do the Algorithm 2. Pass belief about ci and rm,i out of the m time block within the m time block: (J)

πf B

m,i →ci

=

(J)

CN (0; φm,i − μf B

(J) CN 0; φm,i −μf B

m,i

,χ ¯m + νf B →rm,i ) m,i , (J) (J) (J) , χ ¯ +ν ¯m B m →rm,i f →rm,i +CN 0; φm,i , χ m,i →rm,i

m,i

1 (J) 1 (J) i ϑ¯f B →rm,i = (1−φ(, πci →f B ))CN (rm ; φm,i , 2 χ ¯m ) m,i m,i (J) i (J) ¯m ). + φ(, πci →f B )CN (rm ; φm,i , χ m,i

9:

Perform the forward inter-block message passing: α ˆ (l−1) ( ν

νf C

m+1,i →rm+1,i

13: 14: 15:

16:

ν C f

μf C

m+1,i →rm+1,i

10: 11: 12:

=

m,i

ν

→rm,i f B →rm,i m,i

f

m,i

→rm,i

f

m,i

μ C f

)( ν

+ν B f C →rm,i f →rm,i m,i m,i ν B ν C (l−1) 2 fm,i →rm,i fm,i →rm,i (α ˆ ) ν C +ν B

→rm,i

m,i

→rm,i

f C →rm,i m,i

+

μ B f →rm,i m,i ν B f →rm,i m,i

(l−1) 2

ˆ ) + (λ i

) ,

end for for m = M − 1, M − 2, ..., 1 do Perform the backward inter-block message passing: = ∞, νf C m+1,i →rm,i ν ν fC →rm+1,i f B →rm+1,i m+2,i m+1,i 1 × μf C (l−1) →r ν +ν m,i α ˆ m+1,i fC →rm+1,i fB →rm+1,i m+2,i m+1,i μ μ fC →rm+1,i fB →rm+1,i m+2,i , + ν m+1,i ν C f →rm+1,i fB →rm+1,i m+2,i m+1,i ν C ν B f →rm+1,i f →rm+1,i (l−1) 2 m+2,i m+1,i 1 ˆ νf C + ( λ ) i +ν B C m+1,i →rm,i (αˆ (l−1) )2 νfm+2,i →rm+1,i f →rm+1,i m+1,i Perform step 6 → 8. end for acquire the posteriors and : the parameters (l−1) (l−1) m−1,m , rk,m,i , [Θk,m ]i,i , Π ← (13)–(16). cˆi i,i

(l) ˆ ˆ (l) , Λ ← (11)–(12) ρˆ(l) , α i,i end for

),

Capturing the Sparsity for Massive MIMO Channel

221

Algorithm 2 AMP algorithm 1: Input: maximum number of AMP (J). (1) (1) (1) 2: Initialize: ∀p : κ ¯ m,p = ym,p , μh = 0, χ ¯m = N i=1 νrm,i →f B . m,i

m,i

3: for j = 1, 2, ..., J do P (j) (j) (j) ¯ m,p + μh . 4: φm,i= [Bm ]∗p,i κ m,i

p=1

5:

(j)

τm,i=

(1−π

ci →f B m,i

)(ν

rm,i →f B m,i (j) π χ ¯m ci →f B m,i

⎛ ⎡

(j)

+χ ¯m )

(j) |φm,i |2 +2 rm,i →f B m,i

1

m,i

1+τm,i

μh

7:

νh

8:

χ ¯m

9:

=

(j+1) m,i

=

(j)

1

(j) 1+τm,i

(j+1)

=σn2 +

(j+1) κ ¯ m,p =ym,p

μ∗

(j)

−χ ¯m |μ

|2 rm,i →f B m,i

⎥⎟ ⎦⎠.

(j) χ ¯m rm,i →f B m,i (j) ν +χ ¯m rm,i →f B m,i (j) ν χ ¯m 2 rm,i →f B (j) (j) m,i (j) m,i h ν +χ ¯m m,i rm,i →f B m,i

ν

(j+1)

6:

⎤⎞

(j) (j) χ ¯m φm,i rm,i →f B m,i (j) (j) χ ¯m (ν +χ ¯m ) rm,i →f B m,i

ν

⎜ ⎢ exp ⎝−⎣

×

1 P

−

10: end for

rm,i →f B m,i

(j)

φm,i +μ

.

+τ

N

i=1 N i=1

(j+1)

νh

m,i

μ

.

.

(j+1) [Bm ]p,i μh m,i

(j)

+

κ ¯ m,p P

N

i=1

(j) hm,i (j+1) κ ¯ m,p

ν

.

length of the training time as P = 150, the maximum number EM iteration as Lmax = 5, the maximum number of the AMP iteration as J = 50. Figure 2a gives the MSE performance of our model parameter versus SNR. We can see that, with the increase of the SNR, the MSE curves decrease. From Fig. 2a , it can be found that as the velocity increases, the MSEs of λ decrease while that of α increase. This can be justiﬁed as follows. With the increase in velocity, the temporal correlation between diﬀerent time blocks will become less, and the less information will be achieved for α. On the other hand, with the ﬁxed steady variance, the real values of the elements in λ become bigger, and the noise eﬀect on this parameters’ estimation becomes weaker. In Fig. 2b, we study the virtual channel estimation within the parameter ˜ 10 . learning phase at diﬀerent SNRs. We set M = 10 and present the MSEs of h ◦ ◦ ◦ Here, we adopt three values for both ASs, i.e. 5 , 10 , and 15 . From Fig. 2b, it can be seen that the MSEh10 curves becomes higher with increasing the ASs, as ˜ 10 is. the bigger the ASs is, the more the non-zero elements in h

5

Conclusion

In this paper, we proposed a DL channel tracking scheme for massive MIMO system. First, we formulated the time-varying channel model with the help of VCR and AR modeling. Then, we developed a EM-algorithm based SBL framework

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(a)

(b)

Parameter learning | N = 1024, P = 150

N = 1024, P = 150 -16

MSE , 120km/h MSE , 240km/h MSE , 240km/h

-10 -15 -20 -25 -30 5

10

15

SNR [dB]

20

25

Mean Square Error (MSE) [dB]

Mean Square Error (MSE) [dB]

MSE , 120km/h

-5

AS:5° ,120km/h AS:10° ,120km/h AS:15° ,120km/h AS:5° ,240km/h AS:10° ,240km/h AS:15° ,240km/h

-18 -20 -22 -24 -26 -28 -30 -32 -34 5

10

15

20

25

SNR [dB]

Fig. 2. a The MSEs of the model parameters α, λ, versus SNR. b Performance of AMP based virtual channel recovery at diﬀerent AS

to learn the model parameters. To track the posteriors in the expectation step of EM-algorithm, we applied approximate message passing. Numerical results showed that the proposed scheme has low estimation MSE.

References 1. Jungnickel V, Manolakis K, Zirwas W, Panzner B, Braun V, Lossow M, Sternad M, Apelfrojd R, Svensson T (2014) The role of small cells, coordinated multipoint, and massive MIMO in 5G. IEEE Commun Mag 52(5):44–51 2. Berger C-R, Zhaohui W, Huang J, Zhou S (2010) Application of compressive sensing to sparse channel estimation. IEEE Commun Mag 48(11):164–174 3. Wen C-K, Jin S, Wong K-K, Chen J-C, Ting P (2015) Channel estimation for massive MIMO using Gaussian-mixture Bayesian learning. IEEE Trans Wirel Commun 14(3):1356–1368 4. Gao Z, Dai L, Wang Z, Chen S (2015) Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Trans Signal Process 63(23):6169–6183 5. Ma J, Zhang S, Li H, Gao F, Jin S (2018) Sparse Bayesian learning for the timevarying massive MIMO channels: acquisition and tracking. IEEE Trans Commun 6. Zhang D, Wang X, Xu K, Yang Y, Xie W (2018) Multiuser 3D massive MIMO transmission in full-duplex cellular system. EURASIP J Wirel Commun Netw 2018(1):203 7. Ziniel J, Schniter P (2013) Dynamic compressive sensing of time-varying signals via approximate message passing. IEEE Trans Signal Process 61(21):5270–5284 8. Marzetta TL (2006) How much training is required for multiuser MIMO? In: Fortieth Asilomar conference on signals, systems and computers. IEEE, pp 359–363

An On-Line EMC Test System for Liquid Flow Meters Haijiao An(&), Xin Shi, and Xigang Wang Tianjin Institute of Metrological Supervision and Testing, No.4, Keyanxi Road, Nankai District, Tianjin, China [email protected]

Abstract. Electromagnetic interference causes metrological performance degradation of intelligent flow meters due to the electronic components. Therefore, the electromagnetic compatibility (EMC) tests are particularly important to evaluate the performance of flow meters under interferences. Relative test methods have been presented in some works. This paper proposes a kind of on-line EMC test system for liquid flow meters. By using a compact liquid flowrate standard facility, the system can realize the actual flow calibration under electromagnetic interference. Besides, simplicity is another advantage of the system proposed in this paper. Finally, contrast experiments are carried out which reveal that the system has a clear advantage that the variation of the metrological performance of flow meters can be measured during electromagnetic interference. Keywords: On-line EMC test method

Zero flow method Actual flow calibration

1 Introduction As the rapid development of electronic technique, intelligentization of flow meters is especially remarkable, however, intelligent flow meters are more susceptible to electromagnetic interference due to the electronic components. Therefore, electromagnetic compatibility of flow meters, especially water meters, is speciﬁed in many technical standards [1–3]. Normally, the EMC tests are implemented by zero flow method or actual flow calibration method. The zero flow method which is adopted extensively can only indicate if the data storing function of the meters is acceptable when the electromagnetic interference is applied. Unfortunately, the variation of the metrological performance of flow meters during the interference is cannot be measured by this method. To realize the actual flow calibration, some laboratories built special equipment whose pipeline perforates through the anechoic chamber. The main components like pump are placed outside the chamber, and the meter under tested is installed in it. However, this method may result in damage of chamber in a certain extent. An on-line EMC test system for liquid flow meters is proposed to avoid the defects of aforementioned methods. Utilizing a compact liquid flowrate standard facility, the system can realize the actual flow calibration under electromagnetic interference, and the variation of the metrological performance can be analyzed. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 223–230, 2020 https://doi.org/10.1007/978-981-13-9409-6_27

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2 Design of Compact Liquid Flowrate Standard Facility The compact liquid flowrate standard facility is composed of water tank, pump, surge tank, standard flow meter, and valves, as shown in Fig. 1. Because of the requirement of high compactness, the components should be designed as small as possible.

Fig. 1. Structure diagram of compact liquid flowrate standard facility

2.1

Design of Surge Tank

Surge tank which buffers pressure fluctuation can improve the flow stability of the standard facility. The plates placed in the tank and the compressed air inside make the flow smooth and steady [4, 5]. The most popular structure in engineering application places both horizontal and vertical plates in the tank. A number of circular holes are evenly distributed on the horizontal plate, which reduces flow velocity in the tank. Vertical plate is solid which avoids the water flowing into the outlet from inlet directly. The flow area of the horizontal plate is generally designed as 5 times of sectional area of inlet pipe. Because the pipe size is 50 mm, the flow area is 0.0098 m2. And the total area of horizontal plate Sh is 0.012 when the flow area ratio is set as 0.8. The structure diagram for radial cross section of surge tank is shown in Fig. 2. There are three horizontal plates in tank. Considering the mechanical strength and difﬁculty of machine work, the thickness of the plates is designed as 5 mm. The bore diameters of the plates from bottom to top are 12.5 mm, 8 mm, and 6 mm, respectively, and the hole center distances are 16 mm, 11 mm, and 8 mm, respectively. The vertical plate which is perpendicular to the inlet pipe is placed at 1/3 length of diameter of the tank, as shown in Fig. 2.

An On-Line EMC Test System for Liquid Flow Meters

225

Fig. 2. Structure diagram for radial cross section of surge tank

The area of horizontal plate Sh is approximatively expressed as pﬃﬃﬃ 2 4 2 2 D Sh ¼ a b ¼ 3 27

ð1Þ

Then, the diameter of the tank D is calculated as 0.24 m, and the width of vertical plate b is 0.2 m. The structure diagram for axial cross section of surge tank is shown in Fig. 3. The height of tank consists of four parts, which are the height of compressed air space hs, the height from the lower edge of compressed air space to the top of vertical plate hv,

Fig. 3. Structure diagram for axial cross section of surge tank

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the height from the top of vertical plate to the center line of inlet hi, and the height from the center line of inlet to the bottom of tank ht, respectively. The compressed air in the top of tank buffers the fluctuation of water level. The volume of this space is ambulatory, but the minimal volume can be obtained by empirical formula which is expressed as follows. Vmin ¼

s dq qmax pdp

ð2Þ

where s is time factor, dq is the maximal relative fluctuation quantity of the pump’s flow, dq is the maximal relative fluctuation quantity of the tank’s pressure, and qmax is the maximal flow of the standard facility. According to the design requirements, dq is not greater than 5%, and dq is not greater than 0.1%. Substituting these parameters into (2) yields Vmin = 0.0796 m3. The surge tank uses spherical cap, and the cap height is 0.1 m. The volume of the cap is approximatively calculated by (3). The desired height hs are obtained according to (4). 2 pD2 hc 3 4

ð3Þ

4 ðVmin Vc Þ þ hc pD2

ð4Þ

Vc ¼ hs ¼

After flowing through three horizontal plates, the water flows over the top of vertical plate and goes into the right side of tank. To ensure the water flowing to the outlet steady, the flow velocity vf is designed as 0.15 m/s. The hv is calculated by (5). qmax ¼ vf b hv

ð5Þ

According to experiences, the height from the top of vertical plate to the center line of inlet is suited to design as 10 times of inlet pipe size. The height from the center line of inlet to the bottom of tank is not related to the performance of surge tank. In this paper, the height ht is set to 0.25 m. According to above analysis, the minimum height of the tank is 1.04 m. 2.2

Design of Water Tank

Besides compactness, some other problems should be considered when we design a water tank. Firstly, the tank can store all water in the facility; Secondly, the water level in tank should not fluctuate too much in a test, normally less than 5% of the level; thirdly, the flow velocity in tank should less than 0.015 m/s in a test; ﬁnally, there should be enough space between the top of tank and water surface.

An On-Line EMC Test System for Liquid Flow Meters

227

Considering all above problems, the length, width, and height of tank are designed as 1 m, 0.6 m, and 0.6 m, respectively.

3 Analysis of Hydraulic Resistance The hydraulic resistance includes frictional and local ones. The pump should output the maximal flow while overcoming all these resistances of the standard facility. So the analysis of hydraulic resistance determines the choice of pump. According to technical standards, usual flow parameters of different pipe diameter of water meters are given in Table 1, depending on which the hydraulic resistance is analyzed as flows. Table 1. Usual flow parameters of different pipe diameter of water meters Pipe diameter DN15 DN20 DN25 DN32 DN40 DN50

Minimum flow rate (m3/h) 0.040 0.063 0.10 0.16 0.25 0.4

Permanent flow rate (m3/h) 4.0 6.3 10 16 25 40

Fig. 4. Resistant coefﬁcients on the flow path of standard facility

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As shown in Fig. 4, the standard facility has only one flow path. The resistance is greater as flow velocity increases. Therefore, the maximal hydraulic resistance can be obtained under the greatest reference flow which is 28.5 m3/h. The head loss of frictional resistance is calculated according to (6). hw ¼

X

ki

li v2i di 2g

ð6Þ

where k is frictional resistant coefﬁcient, l is the length of pipeline, d is the internal diameter of pipeline, v is the flow velocity, and g is acceleration of gravity. By substituting values in Table 2 into (6), the head loss of frictional resistance is obtained as 0.29 m. Table 2. Parameters for calculating the head loss of frictional resistance Sequence number 1 2 3 4 5 6 7

k 0.021 0.021 0.021 0.019 0.019 0.020 0.020

d (mm) 0.048 0.048 0.048 0.032 0.032 0.036 0.036

v (m/s) 4.38 4.38 4.38 9.85 9.85 7.78 7.78

l (m) 0.050 0.020 0.026 0.055 0.018 0.008 0.012

hw (m) 0.021 0.009 0.011 0.162 0.053 0.014 0.021

The local resistant coefﬁcients for standard facility are shown in Table 3. According to (7), the head loss of local resistance is calculated as 17.76 m. Table 3. Local resistant coefﬁcients for standard facility Local resistant coefﬁcients n1 ; n2 ; n3 ; n6 ; n8 ; n5 ; n13 ; n16 ; n17 n4 n7 n9 ; n12 ; n15 n10 n11 n14 n18

Value 0.5 1.0 0.05 0.1 0.01 0.31 2.06 0.08

An On-Line EMC Test System for Liquid Flow Meters

hj ¼

X

ni

v2i 2g

229

ð7Þ

The height between the inlet of pump and the top of facility is 1.1 m. So the maximal efﬁcient head of the standard facility is 19.15 m, and the delivery lift of pump should not be less than this value. On the basis of above analysis, this paper chose a pump whose delivery lift is 36 m, and the maximal output is 30 m3/h.

4 Experimental Research of on-Line EMC Test System To verify the performance of the on-line EMC test system, a DN15 water meter is tested during the application of radiated electromagnetic ﬁelds, as shown in Fig. 5. The frequency range for the radiated electromagnetic ﬁelds is 26 MHz to 1 GHz, which is divided to 17 steps. During each step, experimentalist increase the carrier frequency to the next one, and measure the error (called actual flow calibration method) or record stored value (called zero flow method) of the meter at the same time.

Fig. 5. On-line EMC test system

The stored values and errors of indication in each step obtained by zero flow method and actual flow calibration method are given in Tables 4 and 5, respectively. By comparing the experimental results above, it is seen that the zero flow method can only indicate if the data storing function of meters is acceptable when the interference is applied. Normally, this function is possibly not susceptible to radiated electromagnetic ﬁelds. However, the errors of indication of meter vary compared with the ones before applying the ﬁeld. It is obvious that zero flow method can barely illustrate variations of metrological characteristics during the test. On the contrary, the on-line EMC test system proposed in this paper can measure the change quantitatively.

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H. An et al. Table 4. Experimental results obtained by zero flow method

Stored value before applying the ﬁeld(m3)

14

During the application of ﬁeld Step 1 2 3 14 14 14 Stored value (m3) Step 10 11 12 Stored 14 14 14 value (m3)

4 14

5 14

6 14

7 14

8 14

9 14

13 14

14 14

15 14

16 14

17 14

/ /

Table 5. Experimental results obtained by the system proposed in this paper Error of indication before applying the ﬁeld (%) 1.3

During the application Step 1 2 Error 1.2 1.3 (%) Step 10 11 Error 1.4 1.5 (%)

of ﬁeld 3 4 1.2 1.4 12 1.5

13 1.6

5 1.2

6 1.4

7 1.5

8 1.3

9 1.5

14 1.6

15 1.7

16 1.6

17 1.5

/ /

References 1. Zhan ZJ, Zhao JL, Zhang LQ et al (2009) Cold water meter. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Beijing 2. Li MH, Ye XC, Chen HZ et al (2005) Measurement of water flow in fully charged closed conduits-Meters for cold potable water and hot water. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Beijing 3. ISO 4064 (2014) Measurement of water flow in fully charged closed conditions-Meters for cold potable water and hot water. The international Organization for Standardization, Switzerland 4. Wang J, Du F Jr, Pan RF (1987) Dynamic mathematical model of surge tank with compressed air. J Sci Instrum 8(2):135–142 5. Ma K (2004) Research and design on efﬁcient combination type water flow standard facility. Tianjin University, Tianjin

Research on Kinematic Simulation for Space Mirrors Positioning 6DOF Robot Zhang Yalin(&), Liang Fengchao, He Haiyan, Wang Chun, Tan Shuang, and Lin Zhe Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China [email protected]

Abstract. Six-degree of freedom (6DOF) parallel robot for space mirrors positioning is one of the effective way to adjust position and attitude of the space mirrors and improve the image quality of space camera. In order to realize the 3D simulation of Kinematic for the space mirrors positioning 6DOF robot, this paper construct the 6DOF kinematic model and algorithms, and then the Human-Computer interaction interface is programmed based on MFC frameworks and 3D simulation interface is achieved based on OpenGL. The experimental results show that the simulation system can display the movement of the space mirrors positioning 6DOF robot precisely and verify the dynamics algorithms with a friendly interface. Keywords: 6DOF

Kinematic OpenGL Robot

During the imaging process of the space camera, the position error caused by the tilt, which is affected by factors such as launch shock, vibration and on-orbit temperature environment changes and stress release, can be effectively offset if the posed of the space mirrors adjusted promptly and precisely meanwhile the degradation of the camera image quality is also avoided [1]. Stewart’s six-degree-of-freedom (6DOF) parallel robot has the advantages of high precision, strength, and stability, small error and friction, and good dynamic performance. It is the main tool for precisely adjustment of space camera mirrors [2–4]. However, the 6DOF parallel robot has many characteristics such as large number inputs and outputs, strong coupling of the poles and complicated control process. Therefore, real-time dynamic simulation can be used to further understand the robot and verify the kinematics algorithms of the robot. It can be visualized and observed. In 3D simulation tools, OpenGL is a high-performance open graphics library technology that provides basic 3D graphics elements and abundant graphics functions, powerful 3D modeling capabilities, frame buffer animation technology and real-time interactive operations. At the same time, OpenGL is well applied to the Windows environment and effectively integrated with Visual Studio, so that the 3D simulation function module can be easily integrated into the measurement and control program of the entire 6DOF parallel robot based on the MFC framework. In this paper, OpenGL simulation development tool is used to establish a real-time simulation platform based on the kinematics model of the space mirrors positioning 6DOF parallel robot. This simulation is of great signiﬁcance for the research and application of the robot. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 231–238, 2020 https://doi.org/10.1007/978-981-13-9409-6_28

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1 The Position Analysis of 6DOF Parallel Robot 1.1

6DOF Parallel Robot Model

The Stewart platform is a typical 6DOF parallel robot. This paper studies the 6-SPS shown in Fig. 1. The upper and lower platforms of the mechanism are connected by 6 poles. Each poles has two ball joints at both ends, and the middle is a movement vice. The driver pushes the moving pair to move relatively to each other, changing the length of each pole, and changing the position and posture of the upper platform in space. b3 b 2

y

b1 x

P

b4

l4

z

b6

b5

l3

l2 l6

l5 B3

O

B5

B2

Y

Z

B4

l1

B1 X

B6

Fig. 1. 6DOF parallel robot

The hinge points of the upper and lower platforms are respectively recorded as bi and Bi ði ¼ 1; . . .; 6Þ, and bi distributed on a circle with radius r, b1 , b3 , b5 and b2 , b4 , b6 , respectively, forming two equilateral triangles, and their relative angles are a as shown in Fig. 2a; Bi distributed on a circle of radius R, B1 , B3 , B5 and B2 , B4 , B6 respectively forming two equilateral triangles, the relative angle of which is b shown in Fig. 2b. The upper platform is a moving platform, and a dynamic coordinate system P-xyz ﬁxed to the upper platform is established. The lower platform is a ﬁxed platform, and the static coordinate system O-XYZ is established. The sitting of the vector v in the P-xyz moving coordinate system is marked as Pv , as v in the sitting mark in the O-XYZ reference coordinate system. The six drive poles are recorded as li the length of the poles as li ði ¼ 1; . . .; 6Þ.

Research on Kinematic Simulation for Space Mirrors Positioning

(a)

233

(b) B2

b2 b3

B1 B3

R

b1 b6

r

B4

b4

B6

b5

B5

Fig. 2. 6DOF parallel platform hinge points distribution

In the upper and lower platform coordinate systems, the coordinate expressions of the vector sum are bi ¼ r½cos ai sin ai 0T ði ¼ 1; . . .; 6Þ

ð1:1Þ

Bi ¼ R½cos bi sin bi 0T ði ¼ 1; . . .; 6Þ

ð1:2Þ

p

1.2

Inverse Solution of the Position of 6DOF Parallel Robot

After the coordinate system is determined, the pose of the moving platform is represented by a generalized coordinate vector q, where q ¼ ½q1 ; q2 ; q3 ; q4 ; q5 ; q6 T , ½q1 ; q2 ; q3 T the coordinate vector representing the center of the motion platform in the inertial coordinate system, ½q4 ; q5 ; q6 T represents the attitude angle of the motion platform in the inertial coordinate system, that is, the Euler angle. These six parameters determine the spatial pose of the moving platform. Decomposing the ﬁnite rotation of a rigid body around an axis mentioned in Euler’s theorem into three ﬁnite rotations around a coordinate axis in a certain order, in this paper, the rotation order is selected as x!y!z coordinate axis. The ﬁnal rotation transformation matrix can be obtained from the properties of the rotation matrix: A BR

¼R 2 ðx; U Þ Rðy; V Þ Rðz; W Þ cVcW sVsW ¼ 4 sUsVcW þ cUsW sUsVsW þ cUcW cUsVcW þ sUsW cUsVsW þ sUcW

3 sV cVsU 5 cUcV

ð1:3Þ

where cU ¼ cos U, cV ¼ cos V, cW ¼ cos W, sU ¼ sin U, sV ¼ sin V, sW ¼ sin W. The generalized coordinate vector in the moving platform q ¼ ½x; y; z; U; V; W T , the pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ length of the pole is li ¼ jli j ¼ lTi li , li is the vector bi Bi , jli j is the length of the pole, i ¼ 1; 2; . . .; 6.

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Positive Solution of the Position of 6DOF Parallel Robot

The positive solution is more complicated than the inverse solution of the position of the 6DOF parallel robot. The following is a solution to the positive solution using the Newton-Raphson method. Deﬁne a target vector function to describe the estimated value of the actuator’s telescopic length li and the actual value li . 2

3 2 23 f1 l21 l1 6.7 6 7 f ¼ 4 .. 5 ¼ 4 ... 5 2 f6 l26 l6

ð1:5Þ

The Newton-Raphson method takes the minimum value of the target vector function as the target. The steps to solve the pose vector P of the 6DOF parallel robot are as follows: 1. Measurement l, select the initial position P of the moving platform; 2. Based on P and Calculate l, form a vector function f; 3. If it PT P\e1 is true, then P is the desired pose, otherwise, proceed to the next step; 4. Calculate the Jacobian matrix @f J ¼ @P ; 5. Use JdP ¼ f to calculate the pose correction value dP; 6. If it dPT dP\e2 is true, P is the desired pose, otherwise, proceed to the next step; 7. Calculate P ¼ P þ dP and go to step 2. The calculation formula of the Jacobian matrix in step 5 is as follows: 8 Ji;1 > > > > Ji;2 > > > > < Ji;3 Ji;4 > > > > Ji;5 > > > > : Ji;6

¼ 2lix ¼ 2liy ¼ 2liz ¼ 2ðp aiy lTi Rcol3 p aiz lTi Rcol2 Þ Rprow3 ai ðlix cos hz þ liy sin hz Þ ¼2 liz ðp aix cos hy þ p aiy sin hy sin hx þ p aiz sin hy cos hx Þ ¼ 2ðliy Rprow3 ai lix Rprow2 ai Þ

ð1:6Þ

where: li ¼ ½ lix liy liz T , the coordinate vector of the i-th pole; p ai , the coordinate vector of the i-th hinge point in the o xyz coordinate system on the motion platform; p aix , p aiy , p aiz , respectively present p ai in the coordinate vector in the o xyz coordinate T T system; Rcol2 ¼ ½ Jx Jy Jz ; Rcol3 ¼ ½ Kx Ky Kz ; Rrow1 ¼ ½ Ix Jx Kx ; Rrow2 ¼ ½ Iy Jy Ky Rrow3 ¼ ½ Iz Jz Kz ; The 6DOF parallel mechanism generally moves near the neutral position, so the initial value of the positive solution can be set to the middle position to ensure the convergence of the solution method. This method can ﬁnd the only feasible solution.

2 Simulation of 6DOF Parallel Robot Based on OpenGL The 6DOF parallel mirror platform is dynamically generated in real time according to the 3D coordinates provided by the inverse solution and the positive solution of the kinematic model. The platform pose is calculated by the inverse solution, and the pose parameters are not easy to ﬁnd. Therefore, the rotation matrix of the struct is obtained

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by using the quaternion, which is simpler and faster than the Euler angle. The angle / is between the new pose of the pole and the benchmark pole, the normal vector of the plane formed by the pole and the benchmark pole is the rotation axis a ¼ ½ a1 a2 a3 T , the normalized vector is: aT a ¼ a21 þ a22 þ a23 ¼ 1 The quaternion parameter is written as q ¼

ð2:1Þ

e , g2 þ e21 þ e22 þ e23 ¼ 1 and there g

are: 2 3 2 3 e1 a sinð/=2Þ / / 4 1 g ¼ cos ; e ¼ a sin ¼ a2 sinð/=2Þ 5 ¼ 4 e2 5 2 2 e3 a3 sinð/=2Þ

ð2:2Þ

Therefore the normalized rotation matrix based on quaternions is: 2

3 1 2ðe22 þ e23 Þ 2ðe1 e2 þ e3 gÞ 2ðe1 e3 e2 gÞ R ¼ 4 2ðe2 e1 e3 gÞ 1 2ðe23 þ e21 Þ 2ðe2 e3 þ e1 gÞ 5 2ðe3 e1 þ e2 gÞ 2ðe3 e2 e1 gÞ 1 2ðe21 þ e22 Þ

ð2:3Þ

According to the rotation matrix, the ﬁnal 3D simulation image rendering is realized by OpenGL’s viewport transformation.

3 Simulation Results After the 3D geometric model and motion model of the 6DOF parallel mirror platform are established, the human-computer interaction software platform and the 3D interface can be programmed with the OpenGL-based MFC framework to achieve precisely control of the platform motion. The flow chart of the simulation system is shown in (Fig. 3). Firstly, the target pose of the moving platform is input on the human-computer interaction interface. The current pose of the dynamic platform can be used to obtain the target pose of the six poles through the inverse solution algorithm, and the current pose of the six poles is updated, and then based on six The current pose of the struts is obtained by the positive solution algorithm to obtain the current pose of the moving platform, update the current pose of the moving platform, and ﬁnally realize the 3D simulation of the entire mirror platform (Figs. 4 and 5). The human-computer interaction interface and 3D simulation interface of the 6DOF parallel mirror platform control system are shown in the ﬁgure. When at the middle position, the platform height is 181.5941 mm, and the hinge coordinates of the static platform and the moving platform are

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Moving platform present pose

Inverse solution for moving platform pose

Update pose of six poles

Update moving platform pose

Positive solution for moving platform actual pose

3D simulation

Fig. 3. Simulation system control flow chart

Fig. 4. Simulation interface

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Fig. 5. Simulation interface of the new pose Chart 1 Six hinge coordinates of the static platform The static platform 1 2 3 4 5 6 X 143.4754 −5.4756 −137.9997 −137.9997 −5.4757 143.4754 Y 76.5128 162.5097 85.9969 −85.9969 −162.5097 −76.5128 Z 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Chart 2 Six hinge coordinates of the moving platform The moving platform X Y Z

1 2 3 4 5 6 101.3365 36.6631 −137.9997 −137.9997 36.6631 101.3365 181.5941 181.5941 181.5941 181.5941 181.5941 181.5941 100.8416 138.1808 37.3391 −37.3391 −138.1808 −100.8416

After moving 50 mm in the X direction and 10° around the V axis, the simulation interface is as shown in the ﬁgure. The simulation interface accurately reflects the new pose of the 6DOF parallel robot. By comparison, it can be seen that the two sets of data are basically consistent, and the kinematics model of the 6DOF parallel mirror platform is veriﬁed to be accurate.

4 Conclusion This paper mainly designs the kinematics model and establishes simulation system for the space mirrors 6DOF parallel robot. According to the kinematics characteristic and requirements of the space mirrors, the human-computer interaction is designed and simulation system modeling of the control system of the space mirrors 6DOF parallel robot is completed. The motion control algorithm is programmed based on the MFC framework and OpenGL technology. Finally the real-time simulation of the system was realized. The practice proves that the simulation platform effectively veriﬁes the

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correctness of the kinematics model of the 6DOF parallel robot. The real-time simulation platform is very important for validating the working principle, algorithm and working space of the space mirrors 6DOF parallel robot.

References 1. Fengchao L, Gang H et al (2017) Simulation of kinematics and dynamics of stewart platform for secondary mirror based on ADAMS. Res Explor Lab 36(2):107–112 2. Shuang T, Xiaoyong W et al (2015) Sensitivity analysis of position and pose adjustment of 6DOF parallel mechanism. Spacecraft Recovery Remote Sens 36(3):78–85 3. Yao R, Zhu WB, Qingge Y (2011) Dimension optimization design of the Stewart platform in FAST[C]. In: International conference on advanced design and manufacturing engineering. GuangZhou(CN), pp 2088–2091 4. QingLin W, Bing Q et al (2013) Secondary mirror control system design based on Stewart platform. Foreign Electronic Measur Technol 32(11):73–76

A Dictionary Learning-Based Oﬀ-Grid DOA Estimation Method Using Khatri-Rao Product Weijie Tan1,2(B) , Chenglin Zheng3 , Judong Li1 , Weiqiang Tan1 , and Chunguo Li4 1

2

School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China [email protected] School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China 3 School of Electronic Information, Wuhan University, Wuhan 430072, China [email protected] 4 School of Information Science and Engineering, Southeast University, Nanjing 210096, China [email protected]

Abstract. Grid mismatch is the main drawback in grid-based sparse representation. For DOA estimation, oﬀ-grid problem degrades the accuracy of angle estimation. In order to solve this problem, a dictionary learning-based oﬀ-grid DOA estimation method is proposed. Firstly, we calculate the sampling covariance matrix, then based on covariance matrix model, we formulate the DOA estimation as a sparse representation problem with Khatri-Rao product dictionary. In the proposed method, two stage iteration strategy is utilized to address the oﬀ-grid problem. In the ﬁrst stage, the coarse estimation is attained by the gridbased sparse DOA estimation; in the second stage, the dictionary perturbation parameter is learned based on gradient descent method for improving the accuracy of DOA estimation. Simulation results verify the eﬀectiveness of the proposed method. Keywords: Grid mismatch · Dictionary learning product · Gradient descent method

· Khatri-Rao

This work was supported in part by the National Undergraduate Training Program for Innovation and Entrepreneurship under Grant 201811078117 and the Natural Science Foundation of Guangdong Province of China under Grant 2018A030310338. c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 239–248, 2020 https://doi.org/10.1007/978-981-13-9409-6_29

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Introduction

Source localization using sensor arrays has played a fundamental role in many engineering applications such as communication, radar, sonar, seismology, smart home and public-safety. So it has received much attention in signal processing ﬁeld for many decades. Many powerful sparse DOA estimation algorithms have been proposed in recent years [1–3]. A great deal of them have focused on signals with sparse representations in ﬁnite discrete dictionaries [4], which provided that the grid is ﬁne enough such that every continuous parameter lies on (practically, close to) certain grid point, and described the continuous parameter by a set of discrete grid points. However, signals encountered in many actual applications are usually speciﬁed by parameter in a continuous domain, the parameters are almost surely not located exactly on the assumed grid and not perfectly matched the predeﬁned basis. This leads to a grid mismatch that results in a degradation of the recovery performance. To solve the grid mismatch problem, a great number of oﬀ-grid sparse methods have been proposed [6–16]. Interpolation is the basic strategy among these methods, which approximates the grid error by interpolating between grid points [6–9]. In continuous basis pursuit method (CBP) [6], a novel polar interpolation approach is proposed for leveraging the translation-invariant property of frequency-sparse signals in the frequency domain. In [7], oﬀ-grid sparse bayesian inference (OGSBI) method is presented which models the mismatch error as a mismatch parameter, ﬁts the grid mismatch to the observed data statistically, and estimates the parameter via an alternating descent algorithm. In [8,9], the sparse total least squares (STLS) method is proposed, STLS method can yield an maximum a posteriori (MAP) optimal estimate, but the obvious drawback is its unrealistic model of Gaussian distributed oﬀ-grid errors. In [10], a lowcomplexity simultaneous orthogonal matching pursuit least-squares (SOMP-LS) algorithm is proposed, which is an iterative alternating descent algorithm, but its performance appears to be questionable for closely-spaced sources. In [11], perturbed OMP method is proposed, which use the OMP framework to obtain a selected set of dictionary atoms, and exploit oﬀ-grid solver to jointly solve for the signal support and the oﬀ-grid perturbations. Based on the covariance matrix model, an oﬀ-grid 1 covariance matrix reconstruction approach (OGL1CMRA) is proposed in [13, 14]. This method can attain the close-form of the perturbation parameter. Based on the element domain, the researchers in [15,16] proposed a dictionary learning DOA estimation method for single snapshot case, which uses the manifold dictionary to learn the perturbation parameter. In this paper, motivated by [13–16], a dictionary learning-based oﬀ-grid DOA estimation is proposed for addressing the grid mismatch problem. Based on covariance matrix model, the DOA estimation is formulated as a sparse representation problem, which is using Khatri-Rao product dictionary. To approximate the oﬀ-grid error, two stage iteration strategy is utilized. In the ﬁrst stage, the grid-based sparse DOA estimation is used to attain the coarse estimation. In the second stage, the dictionary parameter is learning based on gradient descent

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method to improve the accuracy of DOA estimation. Simulation results demonstrate the eﬀectiveness of the proposed method. Notations: Matrix and vector are used by upper and lower case boldface, (·)∗ , (·)T , (·)H , (·)† represent a conjugate operator, transpose operator, conjugate operator, pseudo-reverse. det (·), diag (·) stand for a matrix determinant, diagonal matrix respectively. ◦, denote hadamard product, Khatri-Rao product respectively, and vec(·) denotes vectorization operations, that is, stacking the matrix row by row. ⊗ represents Kronecker product, · 1 , · 2 stand for the 1 norm and 2 norm. IM denote the M × M identify matrix.

2 2.1

Covariance-Based Model for Sparse DOA Estimation Array Model

We consider a uniform linear array (ULA) with M omnidirectional elements, which spacing d between adjacent elements are arranged with half-wavelength λ/2. We assume that K independent narrowband uncorrelated signals from θ1 , θ2 , . . . , θk impinge on this ULA. Then the received signal can be expressed as x(t) =

K

a(θk )sk (t) + n(t),

(1)

k=1

where a(θk ) = [1, e−j2πd/λ sin θk , . . . , e−j2π(M −1)d/λ sin θk ] denotes the steering vector. s(t) represents the transmitted signal. n(t) is a additional Gaussian white noise vector with the zero mean and the variance is σn2 . The covariance matrix can be given as R = E{x(t)xH (t)} = A(θθ )Rs AH (θθ ) + σn2 IM , where A(θθ ) = [a(θ1 ), a(θ2 ), . . . , a(θK )] is array manifold, Rs = denotes signal covariance matrix. 2.2

(2) 1 L

L t=1

s(t)sH (t)

Covariance-Based Sparse Representation Model

In practice, since the covariance matrix R is estimated by the ﬁnite snapshots, ˆ = 1 L x(t)xH (t) instead that is, we use the sampling covariance matrix R t=1 L of the covariance matrix R. There is the measurement error besides the noise ˜ = R + ΔR. Therefore, by the vectorization processing R ˜ − σ 2 IM , error, i.e. R n we can attain ˜r = vec(A(θθ )Rs AH (θθ ) + ΔR) = vec(A(θθ )Rs AH (θθ )) + ξ ,

(3)

˜ where the noise power σn2 can be estimated as the minimum eigenvalue of R. When the signals are uncorrelated, The problem (3) can be further expressed as ˜r = (A∗ (θθ ) A(θθ ))p + ξ = Bp + ξ ,

(4)

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where B(θθ ) = [a(θ1 )∗ ⊗ a(θ1 ), . . . , a(θK )∗ ⊗ a(θK )], p = [p1 , p2 , . . . , pk ]T is the signal power. In order to formulate the DOA estimation as a sparse representation problem, uniformly sampling the spatial domain angle attains a equal spacing grid set φ = [φ1 , φ2 , . . . , φN ], N K and the Khatri-rao product dic˜ φ) = [a(φ1 )∗ ⊗ a(φ1 ), . . . , a(φN )∗ ⊗ a(φN )] can be constructed. The tionary B(φ DOA estimation problem can be formulated the following sparse representation problem: ˜ p2 ≤ , p1 , s.t. ˜r − B˜ (5) min ˜ ˜ 0 p

˜ is the extension of p from θ to φ with non-zeros entries denoting the true where p source locations. The aim of constraint term is to ﬁtting the measurement error ξ , but the parameter is not easy to determinate. From [17], the measurement error ξ satisﬁes a complex Gaussian distribution, i.e. ξ ∼ CN (0, W), where ˜ − 12 ξ ∼ CN (0, I). Furthermore, it ˜ = 1 RT ⊗ R, then we can conclude that W W L can be deduced that ˜ − 12 ξ 2 ∼ χ2 (M 2 ). (6) W 2 Now, the sparse DOA estimation problem can be rewritten as: ˜ 12 (˜r − B˜ ˜ p)2 ≤ ε. min ˜ p1 , s.t. W ˜ 0 p

(7)

It is found that the regular parameter ε can be uniquely determined by χ2 (M 2 ). ˜ − 12 ξ 2 which can be determined χ2 distribution with degrees of freedom ε2 ≥ W 2 M 2 and 99% probability [2]. Generally, we assume that the DOAs of the signals are on the grid, but due to the continuous properties, this assumption is not always satisﬁed. To address this problem, we proposed a dictionary learningbased oﬀ-grid DOA estimation method.

3

Dictionary Learning-Based Oﬀ-Grid DOA Estimation Method

The proposed method includes two steps: In the ﬁrst step, the Khatri-Rao prod˜ is ﬁxed and sparse power vector p ˜ will be estimated by low comuct dictionary B plexity sparse recovery algorithms such as orthogonal matching pursuit (OMP). By calculating the maximum correlation between all atoms in the dictionary and the residual signal, the OMP estimates one atom iteratively. Therefore, it is guaranteed to yield a K-sparse representation after K iterations. In the second ˜ and then update the Khatri-Rao product dictiostep, we ﬁx the power vector p ˜ or equivalently the angle vector θ . To update the Khatri-Rao dictionary, nary B we propose to minimize the following cost function ˜ − 12 (˜r − B(φ)˜ ˜ p)22 . minW φ

(8)

The cost function deﬁned as ˜ − 12 (˜r − B(φ)˜ ˜ ˜ ˜ − 12 (˜r − B(φ)˜ Ξ(φ) (W p))H W p),

(9)

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1

˜ − 2 ˜r, C = W ˜ ˜ − 2 B(φ), Let y = W then the Eq. (9) can expressed as p), Ξ(φ) ((y − C(φ)˜ p))H (y − C(φ)˜

(10)

which can be calculated as follow: Ξ(φ) = (y − C(φ)˜ p)H (y − C(φ)˜ p) = yH y − yH C(φ)˜ p − (C(φ)˜ p)H y − (C(φ)˜ p)H (C(φ)˜ p)

(11)

= y y − 2Re{y C(φ)˜ p} − (C(φ)˜ p) C(φ)˜ p. H

H

H

Therefore, using the steepest decent method, we can iteratively estimate the true ˜ DOA by learning the Khatri-Rao product matrix B(φ). By diﬀerentiating the function Ξ(φ) respect to the value φ, the steepest descent iteration is φ i+1 = φ i − μφ Ξ(φ), where

∂(C(φ)˜ p) φ Ξ(φ) = 2Re ((C(φ)˜ , p )H − y H ) ∂φ

(12)

(13)

and ∂(C(φ)˜ p) ˜ − 12 ((D(φ φ) ◦ A∗ (φ φ)) A(φ φ) + A∗ (φ φ) (D∗ (φ φ) ◦ A(φ φ)))˜ =W p, (14) ∂φ φ) = j2π λd (0 : M − 1) cos(φ). Let where D(φ φ) = (D(φ φ) ◦ A∗ (φ φ)) A(φ φ) + A∗ (φ φ) (D∗ (φ φ) ◦ A(φ φ)), G(φ

(15)

It is found that the ﬁnal recursion for updating the estimated angle vector φ at iteration i + 1 is ˜ − 12 G(φ φ)˜ p)}, (16) φ i+1 = φ i − μRe{eH W where μ is the step size parameter, e = (C(φ)˜ p) − y is the error. Therefore, the overall dictionary learning-based algorithm for DOA estimation based on covariance matrix model is summarized in Algorithm 1.

4

Simulation and Analysis

In this section, we will present the simulation results to evaluate the estimation performance of our proposed method with comparison to several other stateof-the-art methods, including l1 -singular value decomposition (L1-SVD) [1], l1 -sparse representation of array covariance vectors (L1-SRACV) [2] and L1CMRA [3], OGL1CMRA [13] with Cramer-Rao lower bound(CRLB) [18]. In the following experiments, we consider a uniform linear array (ULA) with M = 8 elements. The signals are assumed to be mutually independent Gaussian distribution. The reference point is set at the left of the ULA. Unless otherwise stated, we use an uniform sampling grid from −90◦ to 90◦ with the step 2 degree.

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Algorithm 1 Dictionary learning-based DOA estimation method using Khatri-Rao Product. Input: Array output X ∈ CM ×N ˜ (0) ← 0N ×1 , φ (1) ← φ Initialization: p ˆ Calculate the sample covariance matrix: R 2 2 ˜ Preprocessing : ˜r = vec(R − σn IM ) , σn is the noise power. ˜ = 1 RT ⊗ R Calculate the prewhite matrix: W L for i = 1 to Itermax do ˜ i by the following optimization probFixed φ, update power parameter p lem ˜ 12 (˜r − B˜ ˜ p)2 ≤ ε min ˜ p1 , s.t. W ˜ 0 p

˜ i , update the angle φ i+1 by the following dictionary learning: Fixed p 1

˜ − 2 G(φ φ)˜ p)} φ i+1 = φ i − μRe{eH W end for ˆ←p ˜ (Itermax ) , Output: Source power estimation p Source direction parameter: θˆ ← φ (Itermax +1) .

In our algorithm, the maximum number of iterations is empirically set as 60, the ˆq < pq+1 − p step parameter μ = 10e−4 , the stopping criterion is deﬁned as ˆ −3 2 ˆ 10 . We calculate the noise power σn by the minimum eigenvalue of R, and the regular parameter ε is determined by using MATLAB function chi2inv (1 − γ, M 2 ), where γ is set to 10−4 . In L1-SVD, we set regular parameter λ = 0.575. The root mean square error (RMSE) is deﬁned as: Q 1 (q) θˆ − θ 22 , (17) RMSE = QK n=1 (q) where Q denotes the number of trials, θˆ and θ are the sets of the estimated and true directions of signals in the q trial, respectively. In the ﬁrst experiment, we compare the RMSE of these methods with respect to diﬀerent snapshots. we assume two signals impinge onto the 8 element ULA from [−5◦ + υ, 4◦ + υ], where υ is a random variable, uniformly chosen from the interval [− 2r , 2r ]. The SNR is set to 0 dB and L varies from 20 to 200. The simulation results are shown in Fig. 1, we can see that the curves of the proposed method coincides with the CRLB curve when the number of snapshots is larger than 120. And the performance of OGL1CMRA is better than L1CMRA, L1-SVD and L1-SRACV, the dictionary learning-based method has the best performance of DOA estimation among all methods. We have also compared the CPU time of these methods and show the results in Fig. 2. Since the proposed method and OGL1CMRA are based on coarse estimation to involve the iteration

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4 L1-SVD L1-SRACV L1CMRA OGL1CMRA Dictionary Learning Method CRB

3.5 3 2.5 2 1.5 1 0.5 0

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Fig. 1. The estimated RMSE’s versus the snapshots in the ﬁrst experiment for the ﬁxed SNR = 0 dB. 6

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2

1

0

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Fig. 2. The estimated time’s versus the snapshots in the ﬁrst experiment for the ﬁxed SNR = 0 dB.

procedure, more computations are required. By comparing in Fig. 2, we can see that the computations of the proposed method is the same as the L1CMRA, OGL1CMRA, in other words, the main computation is to attain the coarse estimation, although the estimation performance is better than them, and we can ﬁnd the time-consuming is less than L1-SVD, and L1-SRACV. In the second experiment, we compare the RMSE of these methods with respect to diﬀerent SNR. We repeat the previous simulation except that the number of snapshot is set to 100 and SNR varies from −9 to 15 dB. From Fig. 3,

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Fig. 3. The estimated RMSE’s versus the SNR in the second experiment for the ﬁxed L = 100. L1-SVD L1-SRACV L1CMRA OGL1CMRA Dictionary Learning Method

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Fig. 4. The estimated RMSE’s versus grid interval for the ﬁxed L = 100, SNR = 5 dB.

we can observe that the dictionary learning method have the best estimation performance when the SNR is more than −4 dB, and the performance curve is consistent with the CRLB when SNR is larger than 0 dB. Among all methods, the OGL1CMRA show the suboptimal performance, which uses the one order Taylor expansion to reduce the estimation error, however, it inevitable introduces error without high order term. The proposed method utilizes the gradient descent method to learning the perturbation parameter, the estimation performance is better than OGL1CMRA. In Fig. 4, the relationship between RMSE and grid size

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is given. It can be seen that the proposed algorithm shows the best estimation performance under diﬀerent grid sizes.

5

Conclusion

In this paper, a dictionary learning-based oﬀ-grid DOA estimation is proposed in order to solve oﬀ-grid DOA problem, which is based on covariance matrix model, and formulates the DOA estimation as a sparse representation problem with Khatri-Rao product dictionary. Two stage iteration strategy is utilized to approximate the oﬀ-grid error in the method, the coarse estimation is achieved by the grid-based sparse DOA estimation method, and the Khatri-Rao product dictionary perturbation parameter is learned by gradient descent method. The accuracy of DOA estimation can be improved by alternating iteration. Simulation results demonstrate the eﬀectiveness of the proposed method compare with state-of-the-art methods.

References 1. Malioutov D, Cetin M, Willsky AS (2005) A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 53:3010–3022 2. Yin J, Chen T (2006) Direction-of-arrival estimation using a sparse representation of array covariance vectors. IEEE Trans Signal Process 59:4489–4493 3. Wu X, Zhu WP, Yan J (2016) Direction-of-arrival estimation based on Toeplitz covariance matrix reconstruction. In: 2016 IEEE international conference on acoustics, speech and signal processing. IEEE Press, Shanghai, pp 3071–3075 4. Tan W, Feng X, Ye X et al (2018) Direction-of-arrival of strictly non-circular sources based on weighted mixed-norm minimization. EURASIP J Wirel Commun Netw 225 5. Bernhardt S, Boyer R, Marcos S et al (2016) Compressed sensing with basis mismatch: performance bounds and sparse-based estimator. IEEE Trans Signal Process 64:3483–3494 6. Ekanadham C, Tranchina D, Simoncelli EP (2011) Recovery of sparse translationinvariant signals with continuous basis pursuit. IEEE Trans Signal Process 59:4735–4744 7. Yang Z, Xie L, Zhang C (2013) Oﬀ-grid direction of arrival estimation using sparse Bayesian inference. IEEE Trans Signal Process 61:38–43 8. Zhu H, Leus G, Giannakis GB (2011) Sparsity-cognizant total least-squares for perturbed compressive sampling. IEEE Trans Signal Process 59:2002–2016 9. Jagannath R, Leus G, Pribi´c R (2012) Grid matching for sparse signal recovery in compressive sensing. In: 2012 9th European radar conference. Amsterdam, pp 111–114 10. Gretsistas A, Plumbley MD (2012) An alternating descent algorithm for the oﬀgrid DOA estimation problem with sparsity constraints. In: Proceedings of the 20th European signal processing conference. Bucharest, pp 874–878 11. Teke O, Gurbuz AC, Arikan O (2013) Perturbed orthogonal matching pursuit. IEEE Trans Signal Process 61:6220–6231

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12. Camlica S, Yetik IS, Arikan O (2019) Sparsity based oﬀ-grid blind sensor calibration. Digital Signal Process 84:80–92 13. Wu X, Zhu WP, Yan J et al (2018) Two sparse-based methods for oﬀ-grid directionof-arrival estimation. Signal Process 142:87–95 14. Zhang Z, Wu X, Li C et al (2019) An p -norm based method for oﬀ-grid DOA estimation. Circuits Syst Signal Process 38:904–917 15. Zamani H, Zayyani H, Marvasti F (2016) An iterative dictionary learning-based algorithm for DOA estimation. IEEE Commun Lett 20:1784–1787 16. Tan W, Feng X, Tan W et al (2018) An iterative adaptive dictionary learning approach for multiple snapshot DOA estimation. In: 2018 14th IEEE international conference on signal processing (ICSP). Beijing, pp 214–219 17. Tan W, Feng X (2019) Covariance matrix reconstruction for direction ﬁnding with nested arrays using iterative reweighted nuclear norm minimization. Int J Antenna Propag 18. Stoica P, Nehorai A (1989) Music, maximum likelihood, and Cramer-Rao bound. IEEE Trans Acoust Speech Signal Process 37:720–741

Radar Adaptive Sidelobe Cancellation Technique Based on Spatial Filtering Yumeng Zhang(&), Jinliang Dong, and Huifang Dong Nanjing Research Institute of Electronics Technology, Nanjing, China [email protected]

Abstract. The electromagnetic environment of radar operation is increasingly complex, and active interference will have a great impact on radar performance. Side-lobe cancellation technology is an effective means to eliminate interference by auxiliary antennas. This paper introduces an adaptive beamforming algorithm to form the cancellation weight based on the secondary antenna. The weight convergence speed of several algorithms is analyzed, and the cancellation ability is analyzed, and a normalized least mean square algorithm is proposed. Keywords: Sidelobe cancellation mean square algorithm

Adaptive sidelobe cancellation Least

1 Introduction The space electromagnetic environment under the process of informationization has become increasingly complex, and the struggle for electromagnetic space has been unprecedentedly intensiﬁed, which has had a profound impact on military activities. The interference of ground clutter and various active interferences bring continuous challenges to the development of radar systems at this stage [1]. The sharp deterioration of the electromagnetic environment in which radars operate is a serious challenge for modern radar systems. In order to extract targets from strong ground clutter and interference, the radar also has anti-resistance such as adaptive interference suppression and frequency agility to obtain lower antenna side lobes, low intercept rate performance and high maneuverability. In order to adapt to the complex and varied application environment, the radar system must have higher mobility and flexibility, and lower development and maintenance costs [2]. Therefore, a new radar anti-jamming technology that can well balance the above requirements is needed. It is a good choice regardless of the anti-jamming effect of the side-lobe cancellation technology or its implementation cost. The function of the sidelobe cancellation system is to cancel the sidelobe interference. It sets a certain number of auxiliary antennas around the main antenna of the radar to form an adaptive array with the main antenna. The adaptive weighting of the auxiliary array makes the synthesis of the entire sidelobe cancellation system. The zero point of the receiving pattern adaptively aligns the interference direction to achieve the purpose of suppressing interference. In the sidelobe cancellation system, the weighting coefﬁcient of the main antenna is always 1, and the weighting coefﬁcient of the © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 249–258, 2020 https://doi.org/10.1007/978-981-13-9409-6_30

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auxiliary array is determined by the adaptive algorithm. Therefore, the sidelobe cancellation system is a special case of the adaptive beamforming system. The adaptive algorithm used in the radar sidelobe cancellation system can be divided into open-loop algorithm and closed-loop algorithm. The open-loop algorithm has a large amount of computation and engineering implementation is difﬁcult. This paper studies the closed-loop adaptive algorithm. The closed-loop algorithm is mainly based on the Wiener ﬁltering algorithm with minimum gradient descent. The optimal solution is obtained according to the selected performance surface function. The adaptive algorithm obtained by this principle is the least mean square algorithm (LMS). However, its convergence speed and error characteristics are difﬁcult to balance, and high convergence speed will bring about a large steady-state error. The sampling matrix inversion algorithm (SMI) extended by this algorithm can improve the convergence speed and the contradiction of steady state error, but the amount of calculation becomes larger. In addition, considering the constraint of the dispersion degree of the correlation matrix caused by the gradient steepest descent principle adopted by LM and SMI algorithm, this paper uses the conjugate gradient method (CGM) to improve. Each iteration of the orthogonal path, constantly updated to seek the optimal solution. This paper proposes a normalized LMS algorithm to improve the contradiction between the convergence speed and steady state error of a typical LMS algorithm.

2 Radar Sidelobe Cancellation Technology The active interference of the radar can be accessed not only from the main lobe of the antenna but also from the side lobes of the antenna. One of the ways to deal with cochannel interference from side lobes into the receiver is to use very low side lobes. However, the development of low sidelobe antennas is extremely difﬁcult and costly, and only the newly developed radar antennas have lower sidelobe levels. Moreover, with the advancement of interference technology, the effective power of interference increases continuously, and it is difﬁcult to effectively suppress strong sidelobe interference only by the extremely low sidelobe antenna. The most effective way to deal with sidelobe interference is the sidelobe adaptive cancellation. The pattern of the antenna has spatial ﬁltering characteristics, that is, “space selection”. The main lobe is equivalent to “passband”, and the side lobes are equivalent to “stopband”. If the auxiliary antenna is additionally added, the signal it receives is made. The sum is weighted to form a new spatial ﬁltering characteristic, further eliminating the interference signal received by the side lobes of the main antenna [3]. The adaptive sidelobe cancellation achieves a nulling at the interference angle of the sidelobe position to suppress strong interference signals entering the antenna by the side lobes. Generally, an adaptive canceller is composed of a high-gain radar main antenna and a plurality of low-gain auxiliary antennas (the gain of the auxiliary antenna is equivalent to the ﬁrst side lobe gain of the main antenna), and the adaptive processor is based on the main and auxiliary antennas. The received signal calculates a set of weight coefﬁcients, adjusts the amplitude and phase of the auxiliary antenna, and adaptively forms a zero point in the active interference direction to achieve the purpose of suppressing active interference. The sidelobe cancellation schematic is shown in the Fig. 1.

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d 1

N

2

s0 + i0

s+i x e

Fig. 1. Schematic diagram of sidelobe cancellation

In the schematic diagram, k represents the moment, and dðkÞ represents the main lobe signal, which includes the useful signal s0 ðkÞ and the interference signal i0 ðkÞ received by the radar main lobe [4]. For N auxiliary antennas, the signal received by the auxiliary antenna is an N 1 dimensional matrix xðkÞ, where the useful signal and the interference signal are respectively sðkÞ and iðkÞ, adaptive side lobes The weight vector obtained by the cancellation algorithm can be expressed as N 1 dimensional matrices xðkÞ, the auxiliary antenna output eðkÞ in the cancellation system can be expressed as: eðkÞ ¼ dðkÞ xH ðkÞxðkÞ

ð1Þ

since the mean value of the useful signal is zero, that is, eðkÞ is the cancellation residual, and the representation pair Ability to dissipate.

3 Adaptive Cancellation Algorithm 3.1

Least Mean Square Algorithm

The purpose of the least mean square algorithm is to minimize the square of the average error. Through algebraic calculation, multiple iterations are used to obtain the convergence coefﬁcient of the antenna array to achieve the desired antenna array performance [5]. The mean square error expression is: jeðkÞj2 ¼ jdðkÞj2 2dðkÞxH xðkÞ þ xH ðkÞxðkÞxH ðkÞxðkÞ

ð2Þ

For the quadric surface formed by the objective function, the iterative relationship of weights is obtained through its performance function, and the optimal search based on gradient information is realized. We use the steepest descent method (the opposite direction of the gradient) to iteratively obtain the gradient, and then derive the weight change relationship [6]. The recursive relationship of weights is:

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1 xðk þ 1Þ ¼ xðkÞ þ l½rðkÞ 2

ð3Þ

xðk þ 1Þ ¼ xðkÞ l½Rxx x r ¼ wðkÞ þ le ðkÞxðkÞ

ð4Þ

When the gradient vector is zero, that is, when the Wiener solution is reached, the following relationship is obtained: E½eðkÞxðkÞ ¼ 0

ð5Þ

The implementation steps of the least mean square algorithm are: (1) Weight initial setting, vector xðkÞ is initially N 1 all zero matrix (2) Deﬁne the auxiliary antenna to receive the signal: xðkÞ ¼ vS sðkÞ þ vS iðkÞ þ n

ð6Þ

Vs stands for N 1-dimensional steering vector and n stands for noise. (3) Deﬁne the auxiliary antenna output signal: yðkÞ ¼ wH ðkÞxðkÞ (4) Weight update: xðk þ 1Þ ¼ xðkÞ l½Rxx x r ¼ wðkÞ þ le ðkÞxðkÞ 3.2

Sampling Matrix Inversion Algorithm

A signiﬁcant disadvantage of the least mean square algorithm is that it must undergo multiple iterations before reaching a stable convergence. We use the sampling matrix inversion algorithm to perform time-correlated estimation of the K-sampled array matrix. It does not require iterative calculations and is an adaptive algorithm based on the Maximum Signal to Interference and Noise Ratio (SINR) criterion [7]. For this block adaptation method, the kth block within the K samples is sampled: XK ðkÞ. 2

x1 ð1 þ kKÞ XK ðkÞ ¼ 4 x2 ð1 þ kKÞ xM ð1 þ kKÞ

3 x1 ð2 þ kKÞ x1 ðK þ kKÞ x2 ð2 þ kKÞ x2 ðK þ kKÞ 5 xM ð2 þ kKÞ xM ðK þ kKÞ

ð7Þ

The implementation steps of the sampling matrix inversion algorithm are: (1) Weight initial setting, vector xðkÞ is initially N 1 all zero matrix (2) Deﬁne the auxiliary antenna to receive the signal: xðkÞ ¼ vS sðkÞ þ vS iðkÞ þ n Vs stands for N 1-dimensional steering vector and n stands for noise.

ð8Þ

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(3) Calculating the sampling covariance matrix: Rxx ðkÞ ¼

1 XK ðkÞXKH ðkÞ K

ð9Þ

(4) Computational correlation matrix: r¼

1 d ðkÞXK ðkÞ K

ð10Þ

(5) Calculate the weight vector: 1 H xSMI ðkÞ ¼ R1 xx ðkÞrðkÞ ¼ ½XK ðkÞXK ðkÞ d ðkÞXK ðkÞ

3.3

ð11Þ

Conjugate Gradient Algorithm

In view of the convergence of the correlation matrix dispersion degree on the convergence speed caused by the steepest descent method in the previous algorithms, we use the conjugate gradient method to improve. The orthogonal path of each iteration is continuously updated to seek the optimal solution, because the orthogonal search direction of the CGM algorithm converges the fastest [8]. The goal of the CGM algorithm is to minimize the quadratic cost function by multiple iterations: 1 JðxÞ ¼ xH Ax d H x 2

ð12Þ

A is a K N-dimensional matrix and represents K-sampling of the N-element auxiliary antenna. The gradient value of the cost function is: rJðxÞ ¼ Ax d

ð13Þ

The vector is updated by deﬁning the form of the residual to reduce the number of iterations: rð1Þ ¼ J 0 ðxð1ÞÞ ¼ d Axð1Þ

ð14Þ

Deﬁne the conjugate direction of the iteration by the residual: Dð1Þ ¼ AH rð1Þ

ð15Þ

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The weight iteration relationship is: xðk þ 1Þ ¼ xðkÞ lðkÞDðkÞ

ð16Þ

The step size is selected as: lðkÞ ¼

r H ðkÞAAH rðkÞ DH ðkÞAH ADðkÞ

ð17Þ

The update of the residual vector and the direction vector can be expressed as: rðk þ 1Þ ¼ rðkÞ þ lðkÞADðkÞ

ð18Þ

Dðk þ 1Þ ¼ AH rðk þ 1Þ aðkÞDðkÞ

ð19Þ

aðkÞ ¼

3.4

r H ðk þ 1ÞAAH rðk þ 1Þ r H ðk þ 1ÞAAH rðkÞ

ð20Þ

Normalized Least Mean Square Algorithm

Normalized LMS (NLMS) is an improved algorithm based on the typical LMS algorithm, which aims to avoid the interference caused by gradient noise ampliﬁcation, and adaptively adjust the tracking step size to make the tracking effect, The iterative speed and error variation is better than the typical LMS algorithm with a constant step size. The basic idea is to give a larger step size in the tracking phase, so that the signal converges faster, but after convergence, in order to prevent the steady-state error caused by the excessive step size, the step size is adaptively adjusted through the whole process to achieve fast convergence. Stable after convergence [9]. The basic idea is to adjust the step size of the algorithm according to the input signal. The input signal is proportional to the steady-state error, and the step size is inversely proportional to the steady-state error. The normalized LMS algorithm normalizes the step size by the square norm of the input signal to obtain the step size that changes with the signal to improve the performance of the LMS algorithm [10]. The variable step size LMS algorithm step size can be expressed as: xðk þ 1Þ ¼ xðkÞ þ lðkÞe ðkÞxðkÞ

ð21Þ

In order to achieve fast convergence, it is necessary to select the step value appropriately, reduce the instantaneous square error, and use the instantaneous square error as a simple estimate of the mean square error MSE, which is also the basic idea of the LMS algorithm. In order to speed up the convergence, it is appropriate to minimize the squared error, obtain the partial derivative of the variable coefﬁcient, and make it zero, and ﬁnd:

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lðkÞ ¼

1 xT ðkÞxðkÞ

255

ð22Þ

The resulting step value may cause a negative value of the instantaneous error variation. In order to control the offset, considering the derivative of the instantaneous square error is not equal to the mean square error MSE derivative value, the normalized LMS algorithm is modiﬁed as follows: xðk þ 1Þ ¼ xðkÞ þ

l c þ xT ðkÞxðkÞ

eðkÞxðkÞ

ð23Þ

where l is called a ﬁxed convergence factor and its purpose is to control the amount of offset. The parameter c is set to avoid the xT ðkÞxðkÞ is too small and the step value is too large.

4 Simulation and Performance Analysis The simulation analysis in this paper is based on the linear arrangement of 8 auxiliary antennas. The array element spacing is 0:5k, and the desired signal arrival angle is 0°. The interference signal has a wave azimuth angle of 30° and a useful signal mean of zero. The simulation analysis analyzes the convergence speed and weight stability of LMS algorithm, SMI algorithm and CGM algorithm. Finally, the normalized LMS algorithm proposed by the simulation compares the convergence performance of typical LMS algorithm (Fig. 2).

Fig. 2. Algorithm cancellation comparison chart

The convergence of the LMS is achieved through continuous iteration, but the convergence speed is slower, and the offset is also large after convergence. The SMI

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algorithm performs time-correlation estimation through the antenna array correlation matrix of the sampling point to obtain the optimal weight and direction graph. It does not need to be iterated, so the algorithm is fast, but the inverse operation is performed on a large amount of data, and the hardware implementation is very complicated. The algorithm performance of CGM algorithm is very superior, with fast convergence speed and high stability. However, the algorithm is very complicated due to the iterative update of weights by conjugate gradient method. The contradiction between the convergence speed and the steady-state error, the steps of 0.1, 5e–3 and 2e–3 are selected for comparison. Through Fig. 3, we can observe that the step value of 0.1 has the fastest convergence rate, but the convergence value is very obvious at the convergence value after convergence. The step value 2e–3 has the slowest convergence rate and is stable after convergence. The step value 5e–3 convergence speed and stability are in between.

Fig. 3. Weight magnitude iteration graph

In the Fig. 4, the normalized LMS algorithm dynamically changes the step size in the weight update formula. Because the ﬁxed step value is too large, the convergence speed is fast but there is a large steady-state error value after convergence, and the step value is selected to be small. It is stable after convergence but the convergence speed is very slow. The improved algorithm performs normalization calculation based on the error value and signal value of the current point. As the iteration proceeds, the step size in the weight update formula is changed. In the early iteration, the larger step size is used to increase the convergence speed. As the convergence continues, the normalized step value becomes smaller to maintain a small steady state error.

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Fig. 4. Weight convergence speed characteristic diagram

5 Conclusion In this paper, we study the radar sidelobe cancellation technology for active interference and analyze the principle of sidelobe cancellation. An adaptive cancellation algorithm based on auxiliary antenna is introduced to analyze the performance characteristics of several adaptive algorithms. LMS algorithm with excellent performance and complexity is improved. NLMS algorithm is proposed to verify the normalization. LMS algorithm can solve the contradiction between convergence speed and steady state error.

References 1. Kulpa JS, Maslikowski L (2017) Filter-based design of noise radar waveform with reduced side-lobes. IEEE Trans Aerosp Electron Syst 1–1 2. Pengliang Y, Chenjiang G, Qi Z et al (2018) Sidelobe suppression with constraint for MIMO radar via chaotic whale optimisation. Electron Lett 54(5):311–313 3. Benesty J, Cohen I, Chen J (2017) Adaptive beamforming. Fundamentals of signal enhancement and array signal processing. Wiley, Singapore 4. Sibei C, Qingjun Z, Mingming B et al (2018) An improved adaptive received beamforming for nested frequency offset and nested array FDA-MIMO Radar. Sensors 18(2):520 5. Elisa G, Piotr S, Maria-Pilar JA et al (2018) Recent advances in array antenna and array signal processing for radar. Int J Antennas Propag 2018:1–2 6. Cheng S, Wei Y, Chen Y et al (2017) A universal modiﬁed LMS algorithm with iteration order hybrid switching. ISA Trans 67(Complete):67–75 7. Horowitz L, Blatt H, Brodsky WG et al (1979) Controlling adaptive antenna arrays with the sample matrix inversion algorithm. Aerosp Electron Syst IEEE Trans AES-15(6):840–848 8. Nazareth JL (2009) Conjugate gradient method. Wiley Interdisc Rev Comput Stat 1(3):348– 353

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9. Jo SE, Kim SW (2005) Consistent normalized least mean square ﬁltering with noisy data matrix. IEEE Trans Signal Process 53(6):2112–2123 10. Sahu R, Mohan MR, Sharma MS (2013) Performance analysis of LMS Adaptive beamforming algorithm

On the Spectral Eﬃciency of Multiuser Massive MIMO with Zero-Forcing Precoding Chenglin Zheng1(B) , Weijie Tan1,2 , and Yazhen Chen2 1 2

School of Electronic Information, Wuhan University, Wuhan 430072, China [email protected], [email protected] School of Computer Science, Guangzhou University, Guangzhou 510006, China [email protected]

Abstract. This paper investigates the spectral eﬃciency (SE) of downlink massive MIMO systems, where we consider the Ricean fading channels and utilize the zero forcing precoder at the base state. An exact expression for the SE is derived and the tight lower and upper bounds are presented by utilizing the modiﬁed Jensen’s inequality. Our results show that as the number of transmit antennas grows to inﬁnite or in the high signal-to-noise ratios regime, the lower and upper bound coincide, which are approximately equal to the exact expression for the spectral eﬃciency of system. In addition, we reduce the Ricean fading channels to the Rayleigh fading case, a tractable lower bound of SE is obtained, which is shown that our results cover a series of previous works as special cases. Finally, numerical results are presented to validate the theoretical analysis. Keywords: Spectral eﬃciency · Zero-forcing precoding MIMO · Ricean fading · Space-division multiple-access

1

· Massive

Introduction

With the rapid development of smart terminals and their applications, the need for high speed services explosive increases year by year. In order to meet the need, massive multiple-input multiple-output (MIMO) has recently attracted a lot of interest in the future wireless communications [1], which is regarded as a promising technique for the ﬁfth-generation communication systems. Some literatures have been theoretically demonstrated that massive MIMO systems enable signiﬁcantly increase the achievable sum-rate of cellular communication systems, while possibly reducing system energy consumption [2–4]. This work was supported by the National Undergraduate Training Program for Innovation and Entrepreneurship and the Natural Science Foundation of Guangdong Province of China under Grant 2018A030310338. c Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 259–267, 2020 https://doi.org/10.1007/978-981-13-9409-6_31

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To further maximize the achievable sum-rate, the massive MIMO systems with diﬀerent precoding or detection schemes have been investigated broadly [3– 7]. In particularly, in [3], massive MIMO systems using simple linear algorithms such as the maximum ratio combining (MRC) for the uplink and the maximum ratio transmission (MRT) for the downlink were proposed. It was shown that the eﬀect of fast fading will vanish when the BS deploys very large antenna arrays while simultaneously serving multiple users. Furthermore, the authors in [4–6] studied uplink performance with MRC, zero forcing (ZF) and minimum mean square error ﬁlter for massive MIMO. The lower bounds on the achievable sum-rate for massive MIMO were derived in [4]. In [5,6], some novel upper and lower bounds on the achievable sum-rate of point-to-point MIMO systems with ZF receivers was performed. In addition, the rate analysis of massive MIMO systems with MRT and ZF precoders was performed, some closed-form formulas for the achievable rate were derived in the high and low signal-to-noise ratios (SNR) regimes. The authors in [7] investigated the achievable rate of downlink massive MIMO systems with MRT and ZF precoders, but did not given the exact expression. To the best of our knowledge, there are currently few closedform analytical results on the achievable sum-rate of massive MIMO systems, and the analytical results of [3–7] were limited to independent and identically distributed (i.i.d.) Rayleigh fading channels or point-to-point MIMO systems, respectively, while the practically relevant case of massive MIMO systems in Ricean fading channels remains still an open problem. Motivated by this fact, our works are focused on the spectral eﬃciency for multiuser massive MIMO systems with ZF precoder. Our work diﬀerentiates from the previous literature results in the following aspects. A lower bound on the spectral eﬃciency is obtained by considering Ricean fading channels. And then we further reduce this model to Rayleigh fading channels. We derive an exact closed-form expression for the spectral eﬃciency of systems in Rayleigh fading channels and present tractable upper and lower bounds, which are shown to be much tighter than the results in [7] for arbitrary SNR and is shown that our results cover a series of previous works as special cases. Finally, numerical results are presented to validate the theoretical analysis.

2

System Model

We consider a downlink transmission in massive MIMO system, where the BS is equipped with Nt transmit antennas and transmits simultaneously to M users with single-antenna (Nt ≥ M ). Assume that the BS uses linear precoding techniques to process the signal before transmitting to all users. This requires knowledge of CSI at the BS. The received vector for M user can be written as √ (1) r = ρHWs + n, where ρ is the average SNR of system ,W denotes the Nt × M precoding matrix, n represents the vector of additive white zero-mean Gaussian noise, and H denotes the M × Nt the fast fading channel matrix between the BS and the

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M users. The Ricean channel is considered in this paper, which consists of a deterministic component corresponding to a line-of-sight signal and a Rayleigh distributed random component. The Ricean K-factor represents the ratio of the power of the deterministic component to the power of the scattered components. The Ricean channel matrix H can be written as [8] 1/2 1/2 −1 ¯ + (Ξ + INt )−1 H Hw , H = Ξ(Ξ + INt )

(2)

where Ξ is the Nt × Nt diagonal matrix with [Ξ]kk = Kk , which denotes Ricean K-factor of the K-th user, Hw denotes the random component, whose entries are i.i.d. complex Gaussian random variables with zero-mean and variance one, ¯ denotes the channel mean matrix, which can be expressed as and H ¯2 , . . . , h ¯M . ¯1 , h ¯ = h (3) H Assuming massive MIMO system of a uniform linear array (ULA) at the BS, the channel mean vector of the k-th user is given by ¯ k = 1 ejk0 d cos(ϕk ) . . . ej(Nt −1)k0 d cos(ϕk ) , (4) h where k0 = 2π/λ, λ is the carrier wavelength, d denotes the inter-antenna spacing, and ϕk is the angle of departure for the k-th user. To facilitate our analysis, the noise variance and the large-scale fading are assumed to be constant one since the large-scale fading can be known a priori. By employing zero-forcing (ZF) precoder at the BS, W can be written as W = HH (HHH )−1 .

(5)

In the following section, we circumvent this problem by deriving a close-form expression and tractable lower and upper bounds on the spectral eﬃciency of massive MIMO systems with ZF precoder.

3

Spectral Eﬃciency Analysis of System

In this section, we derive an exact closed-form expression for the spectral eﬃciency and present tight upper and lower bounds by using ZF precoder. 3.1

Spectral Eﬃciency Analysis in Ricean Fading

From (1), the spectral eﬃciency of the k-th user by using ZF precoder can be given as [7] ⎧ ⎛ ⎞⎫ ⎨ ⎬ ρ ⎠ , RkZF = E log2 ⎝1 + (6) −1 ⎩ ⎭ (HH H) kk

where the evaluation of the expectation requires all channel ergodic realizations of channel H.

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Theorem 1. For Ricean fading channels, the lower bound on the spectral eﬃciency is given by Kk Nt + (Nt − K) ZF . (7) = log2 1 + ρ Rk,lower Kk + 1 Proof. We begin with presenting the Jensen’s inequality that holds that E{X} X E log 1 + Y log 1 + E{Y } . With the aid of the inequality, we can derive the lower bound on the spectral eﬃciency of the k-th user as ⎛ ⎞ 1 ZF ⎠ . Rk,lower = log ⎝1 + (8) −1 E (HH H) kk

For the sake of simplicity, we let −1 H −1 = E H H HH H . Yk = E kk

(9)

kk

Substituting (3) into (9), along with some basic manipulations, which can be further simpliﬁed as −1 1 Kk ¯ H ¯ H H H +E H Hw Yk = E . (10) Kk + 1 Kk + 1 w

¯ HH ¯ = Nt and E trace HH Due to E H w Hw Yk =

−1

Nt − M K k Nt + Kk + 1 Kk + 1

kk

=

M Nt −M .

Thus, we obtain

−1 .

(11)

Substituting (11) into (8) yields the desired result. ZF depends on the SNR, the number of From Theorem 1, we know that Rk,lower transmit antennas, the number of users and Ricean K-factor. We now investigate the exact expression of spectral eﬃciency under the Rayleigh fading channels in the following subsection.

3.2

Exact Expression of the Spectral Eﬃciency

Theorem 2. For i.i.d. Rayleigh fading channels, the exact analytical expression of RkZF by using ZF precoder is given by 1 Nt −M +1 1 , (12) Eh RkZF = log2 (e) e ρ h=1 ρ where Eh (·) denotes the exponential integral function of order h.

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Proof. For the sake of simplicity, we start by deﬁning as 1

Xk =

(HH H)

−1

.

(13)

kk

We rewrite (6) as RkZF = log2 (e) E {ln (1 + ρXk )} .

(14)

RkZF

We shall begin by studying the evaluation of according to the following expression ∞ ZF (15) Rk = log2 (e) ln (1 + ρxk )p (xk ) dxk . 0

According to random matrix theory, when the entries of small-scale fading H are i.i.d. Rayleigh random variances, the probability density function (p.d.f.) of Xk is given by [9] e−xk xNt −M . p (xk ) = (16) (Nt − M )! k Substituting (16) into (15) and applying the integration identity [10] ∞

ln (1 + aλ)λq−1 e−bλ dy = (q − 1)!eb/a b−q

q h=1

0

Eh

b . a

(17)

We can obtain the exact expression as (12) after some basic manipulations. This completes the proof. From Theorem 2, we can draw an interesting conclusion that RkZF is concerned with the SNR and the number of transmit antennas. We now study the bound on spectral eﬃciency in the following subsection. 3.3

Tight Bounds on the Spectral Eﬃciency

Theorem 3. For i.i.d. Rayleigh fading channels, the exact analytical expression of RkZF by utilizing modiﬁed Jensen’s inequality can be bounded as Rlower ≤ RkZF ≤ Rupper ,

(18)

where

Rlower log2 (e) ψ (Nt − M + 1) + log2 (e) ln γ + e−ψ(Nt −M +1)

and

Rupper log2 (e) ψ (Nt − M + 1) + log2 (e) ln γ +

1 (Nt − M )

(19)

.

(20)

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Proof. We start by re-expressing RkSE in (14) as follows 1 RkZF = log2 (e) E {ln Xk } + E ln ρ + . Xk

(21)

In order to evaluate the ﬁrst term in (21), the required expectation of ln (Xk ) can be calculated as ∞ E {ln (Xk )} =

ln xk p (xk ) dxk .

(22)

0

Substituting the p.d.f. of Xk in (16) into (22) and applying the integration identity [10] ∞ 1 λa−1 e−bλ ln λdλ = a Γ (a) [ψ (a) − ln (b)] . (23) b 0 After some basic manipulations, the average log function can be evaluated as E {ln (Xk )} = ψ (Nt − M + 1) .

(24)

With the help of the Jensen’s inequality, the second term of the log function in (21) can be upper and lower bounded as 1 1 ≤ E ln ρ + ≤ ln ρ + E . (25) ln ρ + e−E{ln Xk } Xk Xk In order to evaluate the right-hand side term in (25), We shall begin by studying the average value of 1/Xk , which can be calculated as E

1 Xk

∞ = 0

1 p (xk ) dxk . xk

(26) ∞

Substituting (16) into (26) and applying the integration identity

λa e−bλ dλ =

0

a!b−a−1 . After some basic manipulations, the required expectation of 1/Xk can be calculated as 1 1 . (27) = E Xk (Nt − M ) Substituting the results into (25) and combining it with (21) yields the result.

4

Numerical Results

This section provides numerical results to conﬁrm our theoretical analysis. In our simulations, the channel mean vectors between the users are orthogonal to

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Monte−Carlo simulation Analytical Analytical Lower Bound Analytical Upper Bound

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Kk=15dB

K = −∞ k

25 20 15 10

0

5

10

15

20

25

30

SNR [dB]

Fig. 1. The achievable sum-rate versus SNR for diﬀerent Ricean K-factor cases.

Achievable sum−rate [bit/s/Hz]

70

Monte−Carlo Simulation Analytical

65 SNR=15dB

60

55 SNR=10dB

50

45 50

100

150

200

250

300

350

400

450

500

The number of transmit antenna [ Nt ]

Fig. 2. Achievable sum-rate versus the number of transmitter antennas.

each other. We assume that the number of users is set to M = 3, the number of transmit antennas is N t = 30 and the inter-antenna spacing is d = λ/2. For the sake of simplicity, every user has the same Ricean K-factor (i.e. K k = M, ∀ k ). In Fig. 1, results are provided for Monte Carlo simulation and the exact analytical results in (12) are compared against the lower and upper bounds, shown in (19) and (20), respectively. Clearly, the lower and upper bounds remain suﬃciently tight with the these results across the entire SNR regime. Furthermore, it can be found that the exact analytical for the achievable sum-rate and the lower bounds, as well as upper bound exactly coincide in the high-SNR regime and the larger transmit antennas. In addition, the analytical results are presented in

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Achievable sum−rate [bit/s/Hz]

SNR=10dB

24

23

Monte−Carlo Simulation Analytical

22

21 SNR=5dB

20

19

0

5

10

15

20

25

30

Ricean K−factor [ dB ]

Fig. 3. Achievable sum-rate versus the Ricean K-factor.

Theorem 3. We can see that the achievable sum-rate almost no increase for Ricean K-factor K k = 15 dB. This phenomenon is anticipated since for the case of ZF procoder with perfect CSI, the systems experience no inter-user interference and no estimation-error. Figure 2 shows the Monte-Carlo simulations are compared against their corresponding analytical approximations in (7). Results are present for diﬀerent SNR scenarios ρ = 15 dB and ρ = 10 dB, respectively. We see a precise agreement between the simulation results and our analytical results. For the ZF precoder, the achievable SE increases as the number of transmit antennas, which consistents with the previous results in [7]. In addition, we can also notice that the achievable sum-rate with a high SNR is much larger that the small one, which indicates that a high-SNR does dramatically beneﬁt the achievable sum-rate. We study now the the achievable sum-rate varies with the Ricean K-factor in Fig. 3. Results are present for diﬀerent SNR regimes ρ = 10 dB and ρ = 5 dB, respectively. We ﬁnd that the achievable SE has a little change as the Ricean K-factor increases for the reason that the systems experience no interuser interference and no estimation-error with the ZF precoder, on the other ¯ whose hand, as Ricean K-factor, the channel matrix becomes identical to H, singular values have a large spread. Meanwhile, we also notice that the achievable sum-rate with a high SNR is much larger that the small one in the same Ricean K-factor scenario.

5

Conclusion

In this paper, we investigated the spectral eﬃciency for the downlink multiuser massive MIMO systems by utilizing ZF precoding. An exact expression of the spectral eﬃciency was derived and the tight lower and upper bounds are derived.

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Analytical results showed that the lower and upper bounds are approximately equal to the exact expression of the achievable ergodic sum-rate in the high-SNR regime or with larger transmit antennas. In addition, a tractable lower bound of spectral eﬃciency was derived in Ricean fading channels, which are shown that our results covered a series of previous works as special cases.

References 1. Boccardi F, Heath RW, Lozano A, Marzetta TL, Popovski P (2014) Five disruptive technology directions for 5G. IEEE Commun Mag 52(2):74–80 2. Rusek F, Persson D, Lau BK, Larsson EG et al (2013) Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process Mag 30(1):40–60 3. Marzetta TL (2010) Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wirel Commun 9(11):3590–3600 4. Ngo HQ, Larsson EG, Marzetta TL (2013) Energy and spectral eﬃciency of very large multiuser MIMO systems. IEEE Trans Commun 61(4):1436–1449 5. McKay MR, Collings IB, Tulino AM (2010) Achievable sum rate of MIMO MMSE receivers: a general analytic framework. IEEE Trans Inf Theory 56(1):396–410 6. Matthaiou M, Zhong CJ, Ratnarajah T (2011) Novel generic bounds on the sum rate of MIMO ZF receivers. IEEE Trans Signal Process 59(9):4341–4355 7. Yang H, Marzetta TL (2013) Performance of conjugate and zero-forcing beamforming in large-scale antenna systems. IEEE J Sel Areas Commun 31(2):172–179 8. Jin S, Gao XQ, You XH (2007) On the ergodic capacity of rank-1 Ricean fading MIMO channels. IEEE Trans Inf Theory 53(2):502–517 9. Grant A (2002) Rayleigh fading multi-antenna channels. EURASIP J Appl Signal Process 2002(3):316–329 10. Alfano G, Lozano A, Tulino AM, Verd´ u S (2004) Mutual information and eigenvalue distribution of MIMO Ricean channels. In: Proceedings IEEE ISIT 11. Gradshteyn IS, Ryzhik IM (2007) Table of integrals, series, and products, 7th edn. Academic 12. Abramowitz M, Stegun IA (1974) Handbook of mathematical functions. Dover, New York

A Signal Sorting Algorithm Based on LOF De-Noised Clustering Zhenyuan Ji(&), Yan Bu, and Yun Zhang Harbin Institute of Technology, Institute of Electronicsn, Harbin, China [email protected]

Abstract. In this paper, an algorithm for removing outliers is proposed for low SNR signals. Firstly, the coarse separation of signals is performed by using the isolated point removal algorithm based on Euclidean distance, and then the coarsely separated data is ﬁnely separated by the LOF algorithm based on density detection. The remaining signal data after ﬁne separation is clustered. Through simulation analysis, the algorithm can remove all isolated points at the cost of useful signal loss at low SNR, and the residual signal clustering effect is better. Keywords: Outlier removal

LOF Clustering

1 Introduction Radar signal sorting is a vital part of the electronic reconnaissance system and is essential in electronic countermeasures. With the emergence and use of modern new system radars, the traditional radar signal sorting method based on inter-pulse parameter PRI has not adapted to the current use requirements [1]. In recent years, clustering algorithms have been used more and more in radar signal sorting in complex environments [2, 3]. K-means clustering algorithm is widely used in recent years. The initial clustering center and the number of clusters for the K-means clustering algorithm need to be artiﬁcially set. Zhang [4] uses the data ﬁeld potential function to automatically obtain the clustering center and the number of clusters of data, but the method is only applicable to the receiving letter. For signals with high noise, for signals with low signal-to-noise, the noise isolation point needs to be removed. The traditional method of removing noise isolated points is the distance-based isolated point removal algorithm [5], but this algorithm removes a large amount of signal data while removing all noise isolated points, and there are cases where isolated points are not completely cleared. In this paper, an algorithm combining the Euclidean distance-based outlier removal with density-based outlier detection is proposed. The algorithm uses the distance-based outlier removal algorithm to ﬁrst coarsely separate the signal data, and uses the density-based outlier detection algorithm LOF for ﬁne separation of the coarsely separated data [6]. The algorithm removes all noise isolated points at the expense of less loss of signal.

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 268–275, 2020 https://doi.org/10.1007/978-981-13-9409-6_32

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2 Data Standardization The actual types of radar signals received by different types are on different orders of magnitude, because the distance between parameters with large orders of magnitude is much larger than the distance between parameters with small orders of magnitude. In order to make the different types of parameters comparable, signal parameters are needed. Standardized processing. The parameter data is normalized according to (1). xi ð j Þ 0 ¼

xi ð jÞ minðxi ð jÞÞ maxðxi ð jÞÞ minðxi ð jÞÞ

ð1Þ

i ¼ 1; 2. . .M, M is the number of signals, j ¼ 1; 2. . .N N is the signal dimension. That is the number of signal parameter types. xi ð jÞ is the signal sample of the i-th row and jth column, x0i ð jÞ is a standardized processed signal sample. At this time, different types of signal parameters are all on the same order of magnitude and are comparable.

3 Outlier Removal Algorithm 3.1

Isolated Point Removal Based on Euclidean Distance

The K-means clustering algorithm is sensitive to isolated points in signal parameters, so it is necessary to remove isolated points. Set the standardized data to form a data set, Calculate the distance between each parameter in P and other parameters, Calculate the Euclidean distance between each parameter using (2). vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u M uX 2 xip xjp di;j ¼ t

ð2Þ

p¼1

1 i; j N, The resulting di;j forms an N N-dimensional matrix F, Sum each line of F to get fi i ¼ 1; 2. . .N. fi is the sum of the distances between xi and other parameters, the average distance is shown as (3). f ¼

N X

fi =N

ð3Þ

i¼1

Comparing each row of fi with the average distance f , if the parameter larger than f is removed into the set A, the isolated points may not be completely separated. Therefore, the appropriate weighting coefﬁcient m is selected to weight. f and remove the parameters greater than m f into the set A, and the coarse separation of isolated points is completed. LOF is a density-based outlier detection algorithm that describes the degree of isolation of each parameter and assigns each parameter indicator to indicate its degree

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of isolation. The larger the LOF value of the parameter, the higher the degree of isolation and the greater the likelihood of isolated points. Firstly, the related deﬁnition of LOF algorithm is introduced. It is assumed that the data separated by coarse separation constitutes a data set Q, and q is the data object in Q: 1. d ðq; oÞ represents the Euclidean distance between the two objects q and o. 2. The k-th distance of q is the distance between the object q and the object o close to its k-th, which is denoted as k distðqÞ, and the condition that the object o should satisfy is: • There are at least k objects o0 2 Q fqg in the set Q except q, which satisﬁes d ðq; o0 Þ d ðq; oÞ. • There are at most k 1 objects o0 2 Q fqg in the set Q except q, which satisﬁe d ðq; o0 Þ\d ðq; oÞ. 3. The k-th distance neighborhood Nk ðqÞ of q, which is the set of all objects within the k-th distance of q including the k-th distance. 4. The k-th reachable distance from object o to object q is shown as (4). reach distðo; qÞ ¼ maxfdistðoÞ; d ðq; oÞg

ð4Þ

5. The local reachable density of q is shown as (5). P reach distðq; oÞ lrdk ðqÞ ¼ 1=

o2Nk ðqÞ

jNk ðqÞj

ð5Þ

Represents the reciprocal of the average reachable distance of objects in the k-th neighborhood of q to q. 6. LOF is shown as (6). P LOFk ðqÞ ¼

o2Nk ðqÞ

lrdk ðoÞ lrdk ðqÞ

jNk ðqÞj

ð6Þ

Indicates the average of the ratio of the local reachable density of each object in Nk ðqÞ to the local reachable density of object q, and characterizes the degree of isolation of object q within its k neighborhood. The larger the value, the higher the degree of isolation, the more likely it is isolated point.

4 K-Means Clustering Algorithm Based on Data Field 4.1

Data Field

Data interacts with other data through the data ﬁeld, and its influence function is a ﬁeld strength function, which is shown as (7).

A Signal Sorting Algorithm Based on LOF De-Noised Clustering

fy ð xÞ ¼ qe

d 2 ðx;yÞ 2r2

271

ð7Þ

r is the radiation factor, and q is the amount of data reflecting the data points, generally 1, and d ðx; yÞ is the Euclidean distance. The potential function of the data ﬁeld can be obtained from the ﬁeld strength function is shown as (8). M M d 2 ðxi ;xj Þ X X f xj ð xi Þ ¼ e 2r2 F xj ¼ i¼1

ð8Þ

i¼1

j ¼ 1; 2. . .M, M is the number of data. It can be seen that the larger the distance, the smaller the potential value, and the denser the regional data is. 4.2

Determination of the Initial Cluster Center

The line with the same potential value in the data ﬁeld is the equipotential line, and the center surrounded by the equipotential line is called the potential center Fmax . The potential center is the maximum value of the potential value in the local range. The number of clusters and the initial cluster center can be determined by the potential center, but the potential value does not necessarily coincide with the data sample. Therefore, the sample data closest to the potential core should be selected as the initial. Cluster center is shown as (9). d ¼ min d ðFmax ; QðqÞÞ q2Q

ð9Þ

Select the q object position when d reaches the minimum value as the initial cluster center. 4.3

K-Means Algorithm

If the sample is closest to the type of initial cluster center obtained from the data ﬁeld, the sample is classiﬁed into this category, so that the similarity within the category is higher, and the similarity between the categories is lower, and the cluster center is updated until When the sum of the squares of the errors of all samples and the mean of each class converges, the cluster center no longer transforms. The sum of squared errors is shown as (10). JN ¼

N X X x mj j¼1 x2Qi

mj is the mean of the j-th sample set Qi . The LOF de-noised clustering algorithm flow chart is shown in Fig. 1.

ð10Þ

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Calculate the distance between all data objects to form a matrix

Choose the value of k

Determine the number of categories N and the initial cluster center

Calculate k − dist ( q ) q ∈Q

Calculate the sum of each row and get f i

Calculate N

f = ∑ fi N i =1

Calculate the reach of o and q reach − dist ( q, o ) Determine the category of each sample Calculate the local reachable density of q lrd k ( q ) =

1

∑

o∈N k ( q )

Data larger than m f is moved into Q

Calculate the distance of the sample to the cluster center

Calculate N k ( q )

reach − dist ( q, o ) Nk ( q )

Calculate the mean of each type of sample Calculate the LOF of q LOFk ( q ) =

∑

o∈N k ( q )

lrd k ( o ) lrd k ( q )

Nk ( q )

Separate the object corresponding to the first l LOF value

Standardize the data after removing isolated points

mi =

1 Ni

∑x

x ∈ Xi

k

J k = ∑ ∑ x − mi i =1 x∈X i

Update cluster center

Convergence

Output sorting sample

Fig. 1. The LOF de-noised clustering algorithm

5 Simulation Analysis Simulate the radar signal parameters of complex systems, as shown in Table 1. Table 1. Simulation parameter Source type 1 2 3

RF/MHz 3100–3145 2800–3220 3270–3540

PW/us 8.3–15.6 15–18.2 18–19.75

DOA/° 34.5–38 39.45–40.65 37–40

Number of pulses 300 300 400

Adding an isolated point to the signal data stream, and the obtained data map is shown in Fig. 2.

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Fig. 2. Original distribution of data

The signal is subjected to coarse separation based on the Euclidean distance-based isolated point removal, and the obtained data map is shown in Fig. 3.

Fig. 3. Roughly distributed data distribution

The coarsely separated data is ﬁnely separated, that is, the LOF algorithm is used for detection, the obtained LOF value and the separated data map are as shown in Fig. 4.

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(a) The value of the LOF

(b) Three-dimensional distribution of data after fine separation

Fig. 4. Data distribution after ﬁne separation

The initial cluster center is determined by the data ﬁeld potential function, as shown in Fig. 5. According to Fig. 5, the initial clustering center and the number of clusters were obtained, and then K-means clustering was performed. The ﬁnal sorting accuracy was 99%.

(a) Equipotential distribution of RF and PW

(b) Equipotential distribution of RF and DOA

(c) Equipotential distribution of PW and DOA

Fig. 5. Signal parameter equipotential line distribution

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It can be seen from the experimental results that combining the outlier removal algorithm based on Euclidean distance with the LOF detection algorithm can correctly separate all isolated points at the cost of losing less useful signals, making the ﬁnal signal clustering sorting more accurate.

6 Conclusion In this paper, an isolated point removal algorithm is proposed for low SNR signals. Firstly, the isolated point removal algorithm based on Euclidean distance is used for coarse separation. Then the LOF algorithm based on density detection is used to ﬁnely separate the data. Finally, the signals are clustered and sorted. According to the simulation analysis, the algorithm can remove all the isolated points at the cost of losing less useful signals, and then use the K-means algorithm based on the data ﬁeld to have better sorting of the signals. Acknowledgements. This work was supported by the National Natural Science Foundation of China 61201304 and 61201308. It Thanks for the Key Laboratory of Marine Environmental Monitoring and Information Processing, Ministry of Industry and Information Technology.

References 1. Mei G (2011) Radar signal sorting algorithm on intensive signal condition. Harbin Engineering University, Harbin 2. Zhang W (2004) The application of clustering method in radar signal sorting. Radar Sci Technol 2:219–223 3. Zhu Z (2005) The clustering method of radar signals. Electron Countermeasures 6:6–10 4. Zhang R, Xia H (2015) Radar signal sorting algorithm of a new k-means Clustering Modern Def Technol 6:136–141 5. Chawla S, Sun P (2006) SLOM: a new measure for local spatial outliers. Knowl Information Syst 9(4):412–429 6. Dutta H, Giannella C, Borne K et al (2007) Distributed top-k outlier detection in astronomy catalogs using the demac system. In: Proceedings of 7th SIAM international conference on data mining

Design of a Small-Angle Reflector for Shadowless Illumination Guangzhen Wang(&) Foundation Department, Tangshan University, Tangshan 063000, China [email protected]

Abstract. The LED reflector of whole-reflection shadowless illumination was designed by flux compensation method. The theory of the Geometric optics and the Non-imaging optics were used in the design process of the reflectors. Based on LED’s characteristics, this reflector can achieve one uniform illumination spot at 1 m whose diameter is 200 mm. The illuminance is greater than 100,000 lx. The shadowless rate is also studied if there are occlusions. This reflector can meet the special requirements of shadowless lighting or signal transmission and coupling. Keywords: Light-emitting diodes

Illumination design Signal transmission

1 Introduction With the development of LED packaging technology, LED’s color uniformity and heat-spreading [1] have been greatly improved. HSIAO-WEN LEE proposed an advanced, ultra-thin, flexible light-emitting diodes LED package technique. Different types of micro-lenses were applied to different lighting regions to investigate their lighting effects [2]. And multiple colored LEDs [3] is used more and more popularly for it’s high optical flux [4, 5]. The large-size LED chip arrays can be used in shadowless lamp but need corresponding reflective or refractive devices to improve lighting performance. This belongs to the scope of small-angle lighting. In general, small-angle uniform illumination is achieved using PMMA lens. Because of converging light of large angle, TIR lens is the most suitable lens type. Shadowless lamp is an important equipment in the operating room or signal transmission. The LED light source has less heat compared to the traditional light source. It also has no UV radiation and long life. The majority of LED shadowless lamps are lens-type, whose design and manufacturing process are relatively more complex. This lighting’s design belongs to non-imaging optics [6]. The current design methods include free-form surface method, optimization method and other method. The free-form surface method is the most popular design method now because of its accurate design and high efﬁciency. This device must meet a variety of technical requirements at the same time and overcome many technical difﬁculties. The key is to distribute the total flux to the designated site. The illumination uniformity, shadowless rate and the illumination depth reach to the same or even higher levels. The theory of flux compensation and geometric optics are used in this paper to design LED shadowless lamp’s reflector. The surface of the reflector is obtained by the curve’s rotation. The total reflected flux from LED is divided into two parts, which are © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 276–283, 2020 https://doi.org/10.1007/978-981-13-9409-6_33

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reflected to the operative site in two ways. One part is the non-cross-reflection, the other part is cross-reflection. The purpose of these two ways is eliminating shadows.

2 Design and Simulation Because the illuminated site is circular, two-dimensional design is used here. Design ideas is shown in Fig. 1, which represents half of the reflector’s surface.

Fig. 1. Light rays’ paths form LED source.

In Fig. 1, the total flux from the LED is projected into the site in three parts. One part is the directly-incident part expressed by /1 (0–1). The second part of the flux is expressed by /23 (2–3), which is reflected to the site in the non-cross-reflection way. The third part is expressed by /34 (3–4), which is reflected to the surgical site in the cross-reflection way. Letter h is the vertical distance from LED to the operative site. Parameter R1 is the radius of the site. Spherical coordinate system shown in Fig. 2 is established for LED, the total light flux and /23 are shown in Eq. (1), respectively. Z /T ¼

Z IdX ¼

Zp=2 I0 cos h sin hdhdu ¼

pI0 sin2 h ¼ pI0

ð1Þ

0

/23 ¼ pI0 ðsin2 h3 sin2 h2 Þ

ð2Þ

where, I0 is the light intensity at the direction h ¼ 0. Therefore, the flux reflected by the reflector is /r ¼ / /2 ¼ pI0 cos2 h2

ð3Þ

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Ideally, if /1 and /r all reach to the operative site and produce an uniform illumination, the average illuminance is shown in Eq. (4). Eave ¼ /=pR21 ¼ I0 ðsin2 h1 þ cos2 h2 Þ=R21

ð4Þ

If h 2 ½0; h1 , take dh as an inﬁnitesimal section, it is get Eqs. (5) and (6). EðRÞ2pRdR ¼ 2pI0 cos h sin hdh

ð5Þ

EðRÞ ¼ I0 cos4 h=h2

ð6Þ

So the compensate illuminance of reflector is Eq. (7). Ecom ¼ Eave Edir

ð7Þ

And the compensate illuminance of /23 is: E23 ðhÞ ¼

ðsin 2 h3 sin2 h2 Þ Ecom ðsin2 h1 þ cos2 h2 Þ

ð8Þ

The calculation idea is shown in Fig. 2, in which Pm and Pm+1 are the arbitrary adjacent points. Parameter I and Nm are the light intensity and the normal vector at point Pm, respectively. According to the edge-ray theory, the two rays coming from the edge of LED must go to the edge of illuminated (or operative) site.

Fig. 2. Calculation of the reflected surface points.

The reflector surface’s coordinates and illuminated surface’s coordinates can be written as ðr cos h; r sin hÞ and ðh; h tan h0 Þ. The reflector is divided into many equal parts, it is get Eq. (9) taking flux d/23 as an inﬁnitesimal section.

Design of a Small-Angle Reflector for Shadowless Illumination

du23 ¼ pI0 ðsin2 hm þ 1 sin2 hm Þ ¼ ph2 E23 ðhÞðtan2 h0m tan2 h0m þ 1 Þ

279

ð9Þ

Set an initial point P1, all the coordinates of the target points can be calculated using Eqs. (8) and (9). The Law of Reflection (10) indicates the relationship between normal vector and tangent vector [6]: ND¼0

ð10Þ

where, D is the value of that Pm+1 vector minuses Pm vector. Coordinates of all points in the curve 2–3 can be get. Similarly, the coordinates of all points in the curve 3–4 surface can be calculated. rfxði; nÞ ¼ i 2ði nÞn

ð11Þ

For example, an reflector was designed using above theory. The proﬁle of the reflector is shown in Fig. 3a. And the simulation illumination system is shown in Fig. 3b. The ideal light efﬁciency of the reflector can be written as Eq. (12). g ¼ ð/1 þ /r Þ=/ ¼ sin2 h1 þ cos h22

ð12Þ

Here, h1 ¼ a tanð1=10Þ and h2 ¼ a tanð1=2Þ.

Fig. 3. Reflector proﬁle and lighting system.

In the simulation, the illuminated site is replaced by a receiver which is an absorber. Ray tracing 2000 thousand light rays using Monte Carlo method. The light rays’ distribution is shown in Fig. 4 and not all the light rays are drawn. It is seen that the light is control to the small spot in order.

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Fig. 4. Reflector proﬁle and lighting system.

In the simulation, a LED chip array of 5 mm 5 mm is used with 4500 lm. Ignoring absorption and scattering loss, the reflector controls light to the site well. The illuminance distribution is shown in Fig. 5.

Fig. 5. Illumination distribution of the illuminated site.

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Figure 5a is a raster chart and 3723 lm light rays go to this receiver. That is to say, the light efﬁciency is g ¼ 3723=4500 ¼ 82:7% . Figure 5b is a line chart which shows the illumination uniformity is 95%. The illumination uniformity is deﬁned as the ratio of minimum illumination to average illumination. Moreover, it can be seen that the illuminance is uniform and the spot’s edge is clear. If D50 and D10 refer to the spot diameters whose illuminances are 50% and 10% of the speciﬁed central illuminace, the value of D50 =D10 90%. Intensity distribution is shown in Fig. 6.

Fig. 6. Line chart of illumination and light intensity.

It can be seen from Fig. 6, the light is well controlled in the range of speciﬁed angle. The maximum intensity is 12,500 cd. The measurement of illuminance along the central optical axis is carried out. The result shows illumination depth is 700 mm. The energy distribution correspond s to radius and degree are shown in Fig. 7a and b, respectively.

Fig. 7. Energy distribution corresponding to radius and degree.

From Fig. 7, it shows the control ability of the reflector. Almost all the energy is limited in the circle with radius 150 mm and 40°.

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In order to verify the effect of the LED reflector for shadowless lamp, the remaining illuminance is studied when a occlusion block light in the illumination system shown in Fig. 8.

Fig. 8. A system diagram with occlusion of one head and two heads.

The property of the occlusion is an absorber. Figure 9 displays the change of illuminance distribution in the illuminated site. Because of the occlusion, the illuminance reduced slightly.

Fig. 9. Illumination distribution with one occlusion.

The residual illumination is about 100,000 lx which is also more than 40,000 lx. The shadowless rate is g2 ¼ 100000=120000 ¼ 83%. So there is not a big impact on the illuminance because of the cross-reflection flux. A similar result from the

Design of a Small-Angle Reflector for Shadowless Illumination

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Fig. 10. Illuminance distribution with two occlusions.

simulation of two occlusions shown in Fig. 10. It also has high shadowless rate and this reflector is a good element in illumination and signal transmission.

3 Conclusion In this paper, a reflector of shadowless lamp is designed for the current high-power LED array by means of energy supplement. Shadowless lamp’s reflector must achieve small-angle illumination. The traditional shadowless lamp is a lens array. It uses a number of LEDs tilting an angle to achieve a shadowless effect. Compared to the traditional shadowless lamp, the whole-reflection shadowless lamp is relatively simple and greatly reduce the cost. This designed reflector controls the cross-ray and noncross-ray to the illuminated site to achieve a shadowless effect. It is proved by simulation that the reflector can meet the requirements of shadowless lamp illumination. Because it has high energy utilization and illumination uniformity, it will have important applications in LED lighting or signal transmission and coupling.

References 1. Tsai PY, Huang HK, Sung CM, Kan MC, Wang YH (2016) High-power led chip-on-board packages with diamond-like carbon heat-spreading layers. J Disp Technol 12(4):357–361 2. Lee HW, Lin BS (2015) Micro-lens array design on a flexible light-emitting diode package for indoor lighting. Appl Optics 54(28):E210–E215 3. Ying SP, Lin CY, Ni CC (2015) Improving the color uniformity of multiple colored lightemitting diodes using a periodic microstructure surface. Appl Optics 54(28):E75–E79 4. Chung SC, Ho PC, Li DR, Lee TX, Yang TH, Sun CC (2015) Effect of chip spacing on light extraction for light-emitting diode array. Optics Express 23(11):A640–A649 5. Kim Y, Kim S, Iqbal F, Yie H, Kim H (2015) Effect of transmittance on luminescence properties of phosphor-in-glass for LED packaging. Optics Express 23(3):A43–A50 6. Wang G, Wang L, Wang D et al (2011) Secondary optical lens designed in the method of source-target mapping. Appl Opt 50:4031–4036

Anti-interference Communication Algorithm Based on Wideband Spectrum Sensing Minti Liu, Chunling Liu, Ran Zhang, and Yuanming Ding(&) Key Laboratory of Communication and Network, Dalian University, Dalian, Liaoning 116622, China {liuchunling,dingyuanming}@dlu.edu.cn, [email protected]

Abstract. It is difﬁcult to analyze and detect wideband Chirp interference signals, since the existing algorithms are constrained by hardware performance. Aiming at this problem, an anti-interference communication algorithm based on wideband spectrum sensing is proposed. Firstly, the signal is represented as sparse signal by discrete fractional Fourier transform (DFRFT), and Gaussian observation matrix is applied to measure the sparse signal. Then, the signal reconstruction is realized under the Bayesian framework. Finally, the frequency domain information entropy is utilized to make spectrum judgment of the signal, and non-interference frequency band is used for communication, so as to ensure safe and reliable transmission of information. The simulation results demonstrate that, in the case of less measurement data and low signal-to-noise ratio (SNR), the proposed algorithm achieves higher accuracy of signal reconstruction and better detection performance compared with the Bayes compressive sensing energy detection algorithm. Keywords: Anti-interference Entropy

Spectrum sensing Compressed sensing

1 Introduction Cognitive Radio (CR) is a kind of intelligent radio technology [1]. Through real-time spectrum sensing, CR nodes detect non-interference frequency bands for communication and realize safe and reliable transmission of information. Among numerous spectrum detection algorithms, energy detection (ED) algorithm is the most widely used because of its simple implementation and low cost [2]. However, ED algorithm is susceptible to the noise uncertainty, and the detection performance will drop sharply or even fail under low signal-to-noise ratio. Besides, the spectrum detection algorithm based on frequency domain information entropy is unrelated to signal power, which possesses good noise robustness [3]. In recent researches, the application of compressed sensing theory to wideband spectrum detection has become a hotspot for improving spectrum utilization [4]. Wideband spectrum detection algorithm based on compressed sensing technology, can achieve fast and efﬁcient wideband spectrum detection by down sampling. However, this algorithm uses the match pursuit (MP) reconstruction algorithm, which is sensitive © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 284–293, 2020 https://doi.org/10.1007/978-981-13-9409-6_34

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to measurement uncertainty, thus the signal reconstruction accuracy is low. Bayesian compressive sensing algorithm (BCS) is applied to wideband spectrum sensing, which can solve the uncertainty of measurement process by setting the prior of sparse signal and measurements noisy to reduce reconstruction error, and enable to recover the underlying sparse signal exactly [5]. In this research, an anti-interference communication algorithm based on wideband spectrum sensing is proposed. Our motivation to anti-interference communication is mainly from the perspective of avoiding interference frequency, and the related research is about spectrum sensing. The rest of the paper is organized as follows. The system model is described in Sect. 2, where the Chirp interference signal is represented by DFRFT basis. Wideband spectrum sensing based on Bayesian compressed sensing and entropy is then presented in Sect. 3. Section 4 analyzes the performance evaluation of the proposed algorithms compared with existing study. Finally, Sect. 5 concludes the whole research.

2 System Model Supposing the communication channel is a Gaussian channel, the interference in the environment is mainly Chirp interference signal, and this signal xW ðtÞ is composed of W components Chirp, which are sampled at Dt time intervals: yðnÞ ¼ xW ðnÞ þ eðnÞ

ð1Þ

where n ¼ 0; 1; . . .; L 1, eðnÞ is Gaussian noise signal, yðnÞ is received signal. According to compressed sensing theory, ﬁrst the signal is sparsely expressed. In this paper, the decomposition-type discrete fractional Fourier transform (DFRFT) algorithm proposed by Ozaktas [6] is adopted to perform L-point DFRFT transformation on the signal, which can be expressed as: Y ðc; mÞ ¼ zðc; mÞ þ eðc; mÞ

ð2Þ

The parameter c 2 ½p; p is the rotation angle of DFRFT, m is the fractional sampling point number, zðc; mÞ is denoted the DFRFT of the multi-component Chirp interference signal, and eðc; mÞ is the DFRFT of the noise signal. The subscripts are omitted for briefly in the upcoming sections.

3 Proposed Algorithm 3.1

Wideband Spectrum Sensing Algorithm Based on Compressed Sensing

Based on Bayesian compressed sensing theory [7], the Gaussian observation of the signal can be expressed by the following mathematical model:

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g ¼ Uy ¼ UF ðz þ eÞ ¼ z þ E

ð3Þ

where ¼ UF is a holographic dictionary, g 2 RM is observation signal, z 2 RM is the DFRFT coefﬁcient of the Chirp interference signal, F is the DFRFT transform basis matrix, U 2 RML is the Gaussian observation matrix, and E is the measure noise vector. According to the observed data vector g, its Gaussian likelihood model can be expressed as: b ð4Þ pðgjz; bÞ ¼ ð2p=bÞM=2 exp kg zk22 2 Based on Bayes hierarchical model, the prior distribution setting of sparse vector z can be donated by: L Y N zj 0; a1 pðzjaÞ ¼ ð5Þ j j¼1

is where zj is the jth DFRFT coefﬁcient, and aj is the jth element of the a. N zj 0; a1 j a Gaussian probability density function with a mean of 0 and an precision of aj . To promote the scarcity of z, set gamma priors for hyper parameters: pðbja; bÞ ¼ Gaðbja; bÞ ¼

pðajc; d Þ ¼

L Y

ba ða1Þ b expðbbÞ CðaÞ

ð6Þ

Ga aj jc; d

ð7Þ

j¼1

With the obtained observation vector, the Bayesian theorem can be used to derive the posterior probability density function of the hyper parameters. Since the integral item about the hyper parametric a vector is L-dimensional, in order to reduce computational complexity, the hyper parameter is estimated by maximizing the posterior probability density function to obtain:

aMAP ; bMAP ¼ arg maxðlog pðbja; bÞÞ þ log pðajc; d Þ a;b Z þ log dzpðgjz;bÞpðzjaÞ

ð8Þ

For simplicity, assuming a ¼ b ¼ c ¼ d ¼ 0, under the deﬁnition of (8), the maximum likelihood estimation of sum can be simpliﬁed as:

ML

a

;b

ML

Z ¼ arg max log a;b

dzpðgjz;bÞpðzjaÞ

ð9Þ

After estimating a and b, with Bayes’ theorem, Eqs. (4) and (5) can be used to obtain the maximum posterior probability density function for sparse vectors, as:

Anti-interference Communication Algorithm Based on Wideband

pðzjg;a;bÞ ¼ R

pðgjz; bÞpðzjaÞ ¼ N ðzjl;RÞ dzpðgjz;bÞpðzjaÞ

287

ð10Þ

Among them, the mean and variance are represented as l ¼ bR¤ T g and R ¼ 1 b¤ T ¤ þ A from the hyper parameter, respectively. The estimated value of the sparse vector z is l, and A ¼ diagða1 ; a2 ; . . .; aL Þ. From above analyzing, the estimation of sparse vector z can be changed to estimation of hyper parameters a and b. For accelerating computation efﬁciency, the more efﬁcient relevance vector machine (RVM) algorithm [8] is applied to reconstruct signal. The values of a and b can be determined by the maximum likelihood method. By means of marginalizing the sparse coefﬁcients z, the logarithm likelihood function of hyper parameters can be expressed as:

Z Lða; bÞ ¼ logðgja; bÞ ¼ log

pðgjz;bÞpðzjaÞ dz

1 ¼ M log 2p þ logjCj þ gC1 g 2

ð11Þ

where C ¼ b1 I þ ¤ A1 ¤ T , and we set derivative of logarithm likelihood function as zero, then the point estimation of a and b can be obtained by: . 8 new 2 > a ¼ s j lj ðj ¼ 1; 2; . . .; LÞ > > < j XL

. new

gj Tlj 2 L ¼ s 1=b j > 2 j¼1 > > : sj ¼ 1 aj Rj

ð12Þ

where lj is the jth component of the posterior mean l, gj is the jth component of the observation vector g. sj measures the corresponding effect of z determined by the observed data, and Rj is the jth diagonal element of the a posteriori covariance R. Each hyper parameter corresponds to one sparse vector. It is found that most of the hyper parameters tend to inﬁnity. They are invalid for the sparse vector to be mapped to the observation vector, that is, the sparse vector is automatically obtained. After the sparse vector estimation being obtained, the original signal can be obtained by inverse transform. 3.2

Spectrum Decision Algorithm Based on Frequency Domain Entropy

The energy decision-based spectrum decision method is modeled as a binary hypothesis test problem. However, it is related to signal power, which is seriously affected by noise uncertainty. The detection performance is poor under low SNR. To solve above problems, the entropy-based spectrum decision algorithm [9] is applied. The result of z is recovered by the compressed sensing signal, and the detection technique is used to make a ﬁnal decision on the spectrum occupancy of the signal. Based on the concept of frequency domain entropy in information theory, after

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compressed sensing being recovered, the original received signal vector can be obtained, and the L-point discrete Fourier transform DFT (corresponding to c ¼ p=2) by Eq. (2) can be deﬁned as: Y ðmÞ ¼ X ðmÞ þ eðmÞ; m ¼ 0; 1; . . .; L 1

ð13Þ

where Y ðmÞ; X ðmÞ and eðmÞ are the spectrum of the received signal, the interference signal and the noise signal, respectively. Since the amplitude value of the signal spectrum is random, it can be recorded as a random variable R, and the measured signal is represented by estimating its probability density function. Therefore, signal detection based on frequency domain information entropy (FDE) can be modeled as: HJ0 versus HJ1

ð14Þ

Under the assumption Hjk , the state space dimension is the information entropy of the random variable of J (probability space dimension). To reduce the computational complexity, the histogram method is used to estimate the probability of each state. According to the number of values (state number), random variable R can be divided into J bins. The size of each bin is b ¼ ðRmax Rmin Þ=J, which indicates the number of P frequency points in the ith bin, and the total number is Ji¼1 ni ¼ L. Therefore, the probability of frequency appearing in the ith bin is pi ¼ ni =L, then the information entropy of the signal and the corresponding statistics can be represented as: T ðRÞ ¼ HJ ðRÞ ¼

J X ni i¼1

L

log2

ni L

ð15Þ

From the above analysis, the entropy-based spectrum decision algorithm can be modiﬁed as:

H0 : H ð R Þ [ k H1 : H ð R Þ k

ð16Þ

where, the threshold deﬁnition [10] is: k ¼ HJ þ Q1 1 Pfa b1=2

ð17Þ

Among them, HJ ¼ ln

b1 pﬃﬃﬃ þ 21 d þ 1 2b L

ð18Þ

Equation (18) is the theoretical noise entropy, where d is the Euler-Mascheroni constant, Q1 ðÞ is the inverse of the Q function, b is the noise accuracy (reciprocal of the variance) of H under H0 , and Pfa is the theoretical false alarm probability by Neyman-Pearson criterion. The probability of detection and false alarm probability is deﬁned respectively as:

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d ¼ LD =LT P

ð19Þ

fa ¼ LFA =LT P

ð20Þ

Among them, LT is the total number of detections, LD is the correct number of detections, and LFA is the number of false alarms.

4 Simulation Results 4.1

Chirp Interference Signal Sparse Representation

The Chirp signal parameters are set as follows: snapshot number L ¼ 1024, signal pulse width and sampling frequency is set to T ¼ 16 ls and fs ¼ 64 MHz, respectively. The signals bandwidth B ¼ ½B1 ; B2 ; B3 ; B4 ; B5 ; B6 ¼ ½50; 55; 60; 65; 70; 75ðMHzÞ, and 3:75; modulation frequency rate k ¼ ½k1 ; k2 ; k3 ; k4 ; k5 ; k6 ¼ ½3:125; 3:438; 4:063; 4:375; 4:688ðMHz/lsÞ, according to the angle of ration c ¼ arc cot kL=fs2 , then we can obtain acquisition c ¼ ½c1 ; c2 ; c3 ; c4 ;c5 ; c6 ¼ ½0:908; 0:861; 0:818; 0:778; 0:741; 0:706 ðradÞ. Figure 1a, b demonstrate that the signal noise value in the fractional domain is much smaller than the peak value of Chirp signal spectrum. There are only a few positions where obvious peaks appear, while most of the positions are small values. It shows good scarcity characteristics, thus the signal can be subsequent processed using compressed sensing technology.

(a)

(b)

Fig. 1. DFRFT of multi-component Chirp signal. a DFRFT of multi-component Chirp signal. b Chirp signal’s DFRFT amplitude on ðc; mÞ plane

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Signal Reconstruction Performance by Different Algorithms

To verify the signal reconstruction effect, the reconstruction accuracy is deﬁned as: Accuracy , 1 kz ^zk2 kzk2 . In the formula, z is the original signal, and ^z is the recovery signal. Monte Carlo cycle of simulation is set to 500 times.

Amplitude

40 20 0

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(b) Reconstruction with MP, M = 50

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40 20 0

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(c) Reconstruction with BCS, M = 50

Fig. 2. Reconstruction of signal of length L = 1024. a Original sparse signal. b Reconstruction signals by MP. c Reconstruction signals by BCS

To simulate measurement uncertainty, each of the M = 50 measurements is added to Gaussian noise with zero-mean and standard deviation d ¼ 0:1. Figure 2b, c show the reconstruction result by MP and BCS, respectively. Because of the existing measurements noise, MP cannot recover original sparse signal exactly, while BCS can recover better than MP. Furthermore, these error bars may be used to describe conﬁdence of the current reconstruction.

Anti-interference Communication Algorithm Based on Wideband 1

Rec. with MP Rec. with BCS

X: 50 Y: 0.9418

0.8

Reconstruction accuracy

291

0.6

0.4

0.2

0 25

30

35

40

45

50

55

60

65

Number of Measurements

Fig. 3. Reconstruction accuracy of MP and BCS as a function increasing number of measurements

Figure 3 indicates deﬁcient number of measurements with BCS and MP, the reconstruction accuracy are all low. However, compared with MP, BCS can attain higher recover accuracy. Under the same number of measurements, for example M = 50, the reconstruction accuracy by BCS can approach to 94.18%, nevertheless, MP only reaches 43.78%. In addition, to achieve accurate recover, MP needs the more than 65 measurements. 4.3

Comparison of Detection Performance

In Fig. 4a, ROC characteristic curve demonstrates detection performance by Bayes compressed sensing frequency domain entropy (BCSFDE) and Bayes compressed sensing energy detection (BCSED), respectively. Due to noise uncertainty, detection probability of ED is clearly lower than FED in the same false alarm probability and SNR. To make the comparison meaningful, how the measurements affecting the detection performance under different SNR is studied. Figure 4b shows the probability of detection with respect to SNR for BCSFDE and BCSED, respectively. Owing to sufﬁciently take the uncertainty of noisy measurements and uncertainty of signal power into account, BCSFDE can obtain better detection property compared with BCSED under less measurements number and low SNR.

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(b)

1

0.9 0.8

Probability of Detection Pd

Probability of Detection Pd

(a)

SNR=-5dB

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Fig. 4. Comparison of detection performance with BCSFDE and BCSED. a Receiver operating characteristic (ROC) curve with signal-to-noise ratio of −5 and −10 dB. b Probability of detection as a function increasing SNR under difference number of measurements

5 Conclusions In this paper, a wide-band spectrum detection algorithm based on Bayesian compressed sensing and entropy is used to avoid wideband Chirp interference signals. The proposed algorithm can allay the effect of uncertainly measurements to improve recover accuracy, and abstain uncertainly noisy to ameliorate detection performance. Besides, this research could be extended in many kinds of interference signal and real wireless channel to enhance system flexibility. Acknowledgements. This work was supported in part by General Project of Domain Fund under Grant 61403110308.

References 1. Ding G, Jiao Y, Wang J (2018) Spectrum inference in cognitive radio networks: algorithms and applications. IEEE Commun Surv Tutorials 20(1):150–182 2. Chen Y, Oh H (2016) A survey of measurement-based spectrum occupancy model for cognitive radios. IEEE Commun Surv Tutorials 18(1):848–859 3. Zhang YL, Zhang QY, Melodia T (2010) A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks. IEEE Commun Lett 14(6):533–535 4. Ali A, Hamouda W (2017) Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun Surv Tutorials 19(2):1277–1304 5. Arjoune Y, Kaabouch N (2018) Wideband spectrum sensing: a bayesian compressive sensing approach. Sensors 18(6):1839 6. Ozaktas HM, Arikan O, Kutay MA (1996) Digital computation of the fractional Fourier transform. IEEE Trans Signal Process 44(9):2141–2150 7. Ji S, Xue Y, Carin L (2008) Bayesian compressive sensing. IEEE Trans Signal Process 56 (6):2346–2356 8. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

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9. Zhang Y, Zhang Q, Wu S (2010) Entropy-based robust spectrum sensing in cognitive radio. IET Commun 4(4):428–436 10. Zhao N (2013) A novel two-stage entropy-based robust cooperative spectrum sensing scheme with two-bit decision in cognitive radio. Wirel Pers Commun 69(4):1551–1565

A Multi-task Dynamic Compressed Sensing Algorithm for Streaming Signals Eliminating Blocking Effects Daoguang Dong(&), Guosheng Rui, Wenbiao Tian, Ge Liu, Haibo Zhang, and Zhijun Yu Navy Aviation University, Yantai, China [email protected]

Abstract. The performance of Multi-task compressed sensing for streaming signals is restricted by blocking effects caused by block sparse transformation. To solve this problem, a multi-task dynamic compressed sensing algorithm based on sparse Bayesian learning is proposed in this paper, which combines multi-task compressed sensing with sliding window based on LOT transform. Experiments show that the proposed algorithm has higher reconstruction accuracy and operation efﬁciency compared with its block DCT based version. Keywords: Streaming

Multi-task Blocking artifacts Compressed sensing

1 Introduction Compressed sensing [1, 2] (CS) is of great signiﬁcance in reducing the amount of data and relieving the pressure of wireless transmission. Compared with the traditional single measurement vector method, multi-measurement vector (MMV) [3–7] method can achieve higher reconstruction accuracy with fewer measurements. However, in the face of streaming signals in time domain, artiﬁcial blocking effects [7] is often unavoidable, the continuity and smoothness of the signal will then be damaged. Sparse Bayesian learning (SBL) [8, 9] was used for MMV in [3], which showed better reconstruction accuracy than greedy based and convex relaxation based algorithms. Though efforts have been made to explore methods of dynamic CS [4, 10–14], the above methods can hardly eliminate the blocking effects when handling streaming signals. Recently, [15] introduced the idea of lapped orthogonal transform [16] (LOT) into CS, and proposed a dynamic sparse reconstruction algorithm based on L1homotopy. [17] combines LOT with SBL on the basis of [15], and proposes a dynamic sparse reconstruction algorithm based on SBL for streaming signal processing, which proves the superiority of SBL algorithm over other algorithms. However, the application condition of the algorithm is single-task. For multi-task observation of streaming signals, there is no effective CS solution available for publication. Here in this paper, we present a SBL-based multi-task dynamic CS algorithm for streaming signals, which is abbreviated as SMT-SBL. We establish a multi-task sliding window observation system based on LOT transform, and complete the reconstruction via SBL. Experiments are conducted using historical prediction data of evaporation © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 294–302, 2020 https://doi.org/10.1007/978-981-13-9409-6_35

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ducts, and the results show that compared with the traditional multi-task SBL algorithms based on block DCT, the proposed algorithm signiﬁcantly eliminates the blocking effects, and has a higher reconstruction signal-to-error ratio (SER).

2 SMT-SBL Algorithm Assume that the streaming signals xl ðL ¼ 1; . . .; LÞ were observed by L sensors, yt;l ¼ Ul xt;l þ et;l

ð1Þ

where Ul 2 RMN ðM\N Þ is the measurement matrix, xt;l 2 RN1 is one piece of block signals among xt at time t, yt;l 2 RM1 is the measurement vector, et;l 2 RM1 is the noise. Assume that the LOT basis matrix is P ¼ ½PT1 ; PT2 T 2 R2NN . Then the corresponding LOT transformation and inverse transformation formulas are as follows wt;l ¼ ½PT1 ; PT2 ½xTt1;l ; xTt;l T

ð2Þ

xt;l ¼ ½P2 ; P1 ½wTt;l ; wTt þ 1;l T

ð3Þ

Tipping and Faul [8] pointed out that information of wtd;l is contained only from yt2d1;l to yt;l , and d ¼ 1 is sufﬁcient to satisfy the requirement of precise reconstruction. Henceforth, a multi-task LOT sliding system is established as shown in Fig. 1, where Bl ¼ Ul ½P2 ; P1 .

Fig. 1. Multi-task LOT sliding system for streaming signals.

h i ^ ^ ^ ^ t ¼ ½ t;L , wt;l ¼ ½wTt2d1;l ; . . .; wTtd1;l T wt;1 ; . . .; w Denote W t ¼ wt;1 ; . . .; wt;L , W t;l ¼ ½wTtd;l ; . . .; wTt þ 1;l T . The observation corresponding to task l is (4) with U l and w ^

l . The priors are in (5)–(6), where partitioned into Ul and U at ¼ ½aTtd ; . . .; aTt þ 1 T , at ¼ ½at;1 ; . . .; at;N T .

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^ lw ~yt;l ¼ U l wt;l þ U t;l þ ~et;l ; t 2d þ 2 p ~et;l ¼ N ~et;l j0; a1 0;l I M ð2d þ 2Þ

ð5Þ

p a0;l ¼ Gamma a0;l jal ; bl

ð6Þ

L tY þ1 Y N Y 1 N wsi;l j0; a1 a 0 s;i

tÞ ¼ pð W

ð4Þ

ð7Þ

l¼1 s¼td i¼1

1 Tyt;l TU U ¼ U lwt;l l l þ At l

ð8Þ

1 ^ w ¼ U TU R t;l l l þ At

ð9Þ

^

^ t ¼ ½yt;1 ; . . .; yt;L and yt;l ¼ ~yt;l U l w Denote Y t;l , and integrate a0;l out, the poste ^ w shown in (8) and (9), t;l is Student’s-t with mean lwt;l and shape matrix R rior of w t;l h i where At ¼ diagfAtd ; . . .; At þ 1 g, At ¼ diagfat g. Denote U l ¼ w1;l ; . . .; wNðd þ 2Þ;l ,

l ¼ I þ U 1 U T and at ¼ ½a1 ; . . .; aNð2d þ 2Þ , lA ~al ¼ 2al þ Mð2d þ 2Þ, C at can be estil t mated iteratively with the auxiliary variables introduced in (10)–(12), and the update formulas for any aj is (13). 1

1

1

wj;l ; Qj;l ¼ wT C yt;l ; Gl ¼ yT C yt;l þ 2bl Sj;l ¼ wTj;l C j;l l l t;l l sj;l ¼

Q2j;l aj Sj;l aj Qj;l ; qj;l ¼ ; gj;l ¼ Gl þ aj Sj;l aj Sj;l aj Sj;l

ð11Þ

L X ~al q2j;l =gj;l sj;l 2 l¼1 sj;l sj;l qj;l =gj;l

ð12Þ

hj ¼

aj ¼

ð10Þ

8 < :

M=

L P l¼1

~al q2j;l =gj;l sj;l

sj;l ðsj;l q2j;l =gj;l Þ

; if denominator [ 0

1;

ð13Þ

else

The procedures of the SMT-SBL algorithm in the sliding window at time t are shown in Algorithm1, and the fast update formulas are shown below. Adding wj;l to the model: 2DLj ¼

8 L < X l¼1

:

log

a0j a0j þ sj;l

!

. 19 q2j;l gj;l = A ~al log@1 0 aj þ sj;l ; 0

ð14Þ

A Multi-task Dynamic Compressed Sensing Algorithm for Streaming

R11;l ¼ R21;l

^ 0w R t;l

" # T w w ^ R12;l U l w R l 0w j;l t;l l j;l t;l ; l t;l ¼ R22;l lj;l

297

ð15Þ

S0k;l ¼ Sk;l Rjj;l ðwTk;l ej;l Þ2 ; Q0k;l ¼ Qk;l lj;l wTk;l ej;l ; G0l ¼ Gl Rjj;l ðyTt;l ej;l Þ2

ð16Þ

1 T ^w 0 ^ w U ^ w T ^ w þ Rjj;l R T where R11;l ¼ R t;l t;l l wj;l wj;l U l Rt;l , Rjj;l ¼ ðaj þ Sj;l Þ , R12;l ¼ Rjj;l Rt;l U l ^ w , R22;l ¼ Rjj;l , lj;l ¼ Rjj;l Qj;l , ej;l ¼ wj;l U ^ w U lR T wj;l . lR wj;l , R21;l ¼ Rjj;l wT U j;l

t;l

t;l

Deleting wj;l from the model: 2DLj ¼

. 1 9 = Q2j;l Gl S A þ log 1 j;l ~a log@1 þ : l aj Sj;l aj ;

8 L < X l¼1

l

0

ð17Þ

w ^ w Rj;l RT =Rjj;l ; l0w ¼ lw lj;l Rj;l =Rjj;l R^0 t;l ¼ R t;l t;l j;l t;l

ð18Þ

0 wk;l Þ2 =Rjj;l ; Q0 ¼ Qk;l þ lj;l RT U S0k;l ¼ Sk;l þ ðRTj;l U l k;l j;l l wk;l =Rjj;l ; Gl T 2 yt;l Þ =Rjj;l ¼ Gl þ ðRT U

ð19Þ

T

j;l

T

l

^ w , Rj;l is the jth column of R ^ w , lj;l is the jth where Rjj;l is the jth diagonal element of R t;l t;l . element of lwt;l Algorithm1: SMT-SBL algorithm in the sliding window at time t ^ ~ t ; U l ð8lÞ; W t ; at1 and termination threshold e; 1: Inputs: Y at as corresponding values of esti2: Initialization: (a) Initialize atd ; . . .; at of ^ ^ l ¼ I þ U 1 U T for all l; lA mation of at1 ; (b) Compute yt;l ¼ ~yt;l U l wt;l and C t

l

^ w ; . . .; R ^ w (use (8) and (9)); (d) Compute all t ¼ ½lw ; . . .; lw and R (c) Compute W t;1 t;L t;1 t;L Sk;l ; Qk;l ; Gk;l (use (10)); 3: Compute all hk ; sk;l ; qk;l ; gk;l (use (11) and (12)); 4: Compute all DLk using (14), (17) and (20), if DLk \e; ð8lÞ,stop iterating and go to Step10, else go to Step 5, end if; 5: Select ak that has the largest DLk as the candidate to be optimized; 6: if hk [ 0 AND ak ¼ 1, update ak as ak ¼ M=hk and add wk;l ð8lÞ to the model, ^ w ; lw ; Sk;l ; Qk;l ; Gl ð8lÞ using (15)–(16); and update R t;l

t;l

7: else if hk \0 AND ak \1, update ak as ak ¼ 1 and delete wk;l ð8lÞ from the ^ w ; lw ; Sk;l ; Qk;l ; Gl ð8lÞ using (18)–(19); model, and update R t;l

t;l

t;l

t;l

8: else if hk [ 0 AND ak \1, update ak as ak ¼ M=hk and remain wk;l ð8lÞ in the ^ w ; lw ; Sk;l ; Qk;l ; Gl ð8lÞ using (21)–(22); model, and update R 9: end if, go to Step3;

^ w (use (8) and (9)). 10: Outputs: at , lwt;l and R t;l

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Remaining wj;l in the model: 2DLj ¼

L X

(

l¼1

h

i ½ðaj þ sj;l Þgj;l q2j;l a0j ð~al 1Þ log 1 þ Sj;l a01 a1 þ~ al log 0 j j ½ðaj þ sj;l Þgj;l q2j;l aj

)

ð20Þ ^ w cj;l Rj;l RT ; l0w ¼ lw cj;l lj;l Rj;l ^ 0w ¼ R R t;l t;l t;l j;l t;l

ð21Þ

0 T wk;l Þ2 ; Q0 ¼ Qk;l þ cj;l lj;l RT U S0k;l ¼ Sk;l þ cj;l ðRTj;l U yt;l Þ2 k;l j;l l wk;l ; Gl ¼ Gl þ cj;l ðRj;l U l l T

T

T

ð22Þ ^ w , Rj;l is the jth where cj;l ¼ ½Rjj;l þ ða0j aj Þ1 1 , Rjj;l is the jth diagonal element of R t;l ^ w , lj;l is the jth element of lw . column of R t;l

t;l

3 Experimental Results Here Ul is a Gaussian random matrix, and the noise is white Gaussian. For the LOT transform, the continuity of signal would be slightly damaged when the blocking length N 32, therefore we set N ¼ 16. The version based on DCT block transformation is adopted as the comparison algorithm and called DCT-SBL, and the two algorithms are abbreviated as DCT and LOT when experimental results are shown, respectively. Historical diagnostic data of evaporation duct [18] height at sea is used, and the data are based on historical meteorological data obtained from meteorological buoy measurement in Laoshan sea area of Qingdao, and are calculated via the Babin prediction model [19]. The experimental variables are task number L, observation number M and signal-to-noise ratio (SNR). The reconstructed signal-to-error ratio (SER) is used to measure the reconstructed accuracy of the algorithm, and the running time is used to measure the efﬁciency of the algorithm. (a)

(b)

Fig. 2. Comparison of SER of SMT-SBL and DCT-SBL with the observation number varying.

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The results of the algorithm comparison of SER varying with the number of measurements are shown in Fig. 2. From Fig. 2a, b, it can be seen that SMT-SBL has a signiﬁcantly higher SER and SER increase speed than DCT-SBL. (a)

(b)

Fig. 3. Comparison of efﬁciency of SMT-SBL and DCT-SBL with observation number varying.

The results of the algorithm comparison of running time varying with the number of measurements are shown in Fig. 3. As can be seen from Fig. 3a, b, under the same SNR and number of tasks, the running time of both algorithms increases with the increase of the number of measurements, but the running time of SMT-SBL is always signiﬁcantly shorter than that of DCT-SBL. In addition, under the same number of measurements, the running time of the two algorithms will increase with the number of tasks or decrease with the increase of SNR. The results of the algorithm comparison of SER varying with SNR are shown in Fig. 4. Figure 4a, b shows that the SER of both algorithms increases with the increase of SNR under the same number of measurements and tasks, but the SER of SMT-SBL algorithm is always signiﬁcantly higher than that of DCT-SBL algorithm. The results of the algorithm comparison of the running time varying with SNR are shown in Fig. 5. Figure 5a, b shows that under the same number of measurements and tasks, the running time of the two algorithms decreases with the increase of SNR. The running time of SMT-SBL algorithm is signiﬁcantly less than that of DCT-SBL algorithm, and Shortens signiﬁcantly faster.

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Fig. 4. Comparison of SER of SMT-SBL and DCT-SBL with SNR varying.

(a)

(b)

Fig. 5. Comparison of efﬁciency of SMT-SBL and DCT-SBL with SNR varying.

The results of the algorithm comparison of SER varying with number of tasks are shown in Fig. 6. As can be seen from Fig. 6a, b, the SER of SMT-SBL algorithm is always signiﬁcantly higher than that of DCT-SBL algorithm. The results of the algorithm comparison of the running time varying with number of tasks are shown in Fig. 7. Figure 7a, b shows that under the same number of measurements and SNR conditions, the running time of the two algorithms increases with the increase of the number of tasks. Nevertheless, the running time of SMT-SBL algorithm is always signiﬁcantly less than that of DCT-SBL algorithm, and the growth rate is also signiﬁcantly less than that of DCT-SBL algorithm.

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Fig. 6. Comparison of SER of SMT-SBL and DCT-SBL with task number varying

(a)

(b)

Fig. 7. Comparison of efﬁciency of SMT-SBL and DCT-SBL with task number varying

4 Summary To eliminate the blocking effects in multitask compressed sensing of streaming signals, a multi-task compression sensing algorithm based on SBL is proposed, witch combines multi-task SBL with sliding window system based on LOT transform. Experiments show that under the same measurement number, task number and signal-to-noise ratio, the proposed algorithm can obtain the reconstructed SER gain from 3 to 20 dB over its DCT version, and has obvious advantages in operation efﬁciency.

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References 1. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52 (2):489–509 2. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306 3. Wipf DP, Rao BD (2007) An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Press 4. Ji S, Dunson D, Carin L (2009) Multi-task compressive sensing. IEEE Trans Signal Process 57(1):92–106 5. Zhang Z, Rao BD (2011) Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning. IEEE J Sel Topics Sig Process 5(5):912–926 6. Chen W (2016) Simultaneous Bayesian sparse approximation with structured sparse models. IEEE Trans Signal Process 64(23):6145–6159 7. Wu Q (2015) Multi-task Bayesian compressive sensing exploiting intra-task dependency. IEEE Signal Process Lett 22(4):430–434 8. Tipping ME, A Faul (2003) Fast marginal likelihood maximization for sparse Bayesian models. In: Proceedings of the international workshop on artiﬁcial intelligence and statistics, pp 3–6 9. Ji S, Xue Y, Carin L (2008) Bayesian compressive sensing. IEEE Trans Signal Process 56 (6):2346–2356 10. Vaswani N (2008) Kalman ﬁltered compressed sensing. In: 15th IEEE international conference on image processing. IEEE 11. Wang Y, Wipf DP, Chen W (2014) Exploiting the convex-concave penalty for tracking: a novel dynamic reweighted sparse Bayesian learning algorithm. IEEE international conference on acoustics. IEEE 12. Ziniel J, Potter LC, Schniter P (2010) Tracking and smoothing of time-varying sparse signals via approximate belief propagation. In: 11th Asilomar conference 13. Ziniel J, Schniter P (2013) Dynamic compressive sensing of time-varying signals via approximate message passing. IEEE Trans Signal Process 61(21):5270–5284 14. Goertz N, Hannak G (2017) Fast Bayesian signal recovery in compressed sensing with partially unknown discrete prior. In: WSA international ITG workshop on smart antennas. VDE 15. Asif MS, Romberg J (2014) Sparse recovery of streaming signals using l1-homotopy. IEEE Trans Signal Process 62(16):4209–4223 16. Malvar HS (1989) The LOT: transform coding without blocking effects. IEEE Trans Acoust Speech Signal Process 37(4):553–559 17. Wijewardhana UL, Codreanu M (2016) A Bayesian approach for online recovery of streaming signals from compressive measurements. IEEE Trans Signal Process 65(1):184– 199 18. Tian W, Rui G, Dong D (2018) Compressed sensing of evaporation duct based on blind adaptive KLT estimation. Acta Electronica Sinica 46(09):22–28 19. Babin SM, Young GS, Carton JA (1997) A new model of the oceanic evaporation duct. J Appl Meteorol 36(3):193–204

Thunderstorm Recognition Algorithm Research Based on Simulated Airborne Weather Radar Reflectivity Volume Scan Data Rui Liao1, Xu Wang1,2(&), and Jianxin He1,2 1

School of Electronic Engineering, Chengdu University of Information Technology, Sichuan, China [email protected] 2 Key Laboratory of Atmospheric Sounding, China Meteorological Administration, Chengdu 610225, China

Abstract. At present, most airborne radars have no volume scan capability, so the echo information detected is limited and it can be difﬁcult to detect the thunderstorms in front of the aircraft completely. First of all, this paper proposes an airborne weather radar that adopts volume scan mode and takes the X-band ground-based weather radar data as the simulation source to obtain the airborne radar reflectivity volume scan data according to a simulation model. Then, based on the Storm Cell Identiﬁcation (SCI) algorithm, this paper researches and proposes a thunderstorm identiﬁcation algorithm applying to this airborne radar by modifying some threshold parameters, which has improvements on identifying thunderstorm cells. Finally, an example of thunderstorm identiﬁcation based on the simulated airborne weather radar reflectivity volume scan data is given, which shows that the algorithm can effectively identify the thunderstorm cells in the scanning sector in front of the radar and get their attributes. It is helpful for monitoring thunderstorm and meaningful for flight safety. Keywords: Airborne weather radar identiﬁcation SCI algorithm

Volume scan Thunderstorm

1 Introduction As an extension of weather radar, airborne weather radar can detect weather system at a close range, making up for the inflexibility of ground-based radar and scant meteorological information with low resolution of space-based radar caused by the long distance [1]. Several countries started to do researches on airborne detection systems earlier [2]. The United States, Britain, France and other developed countries have dedicated meteorological detection aircraft engaged in business and research. Here are some of the most representative airborne weather radars used in these aircrafts and their detection modes shown in Table 1. Except the Spider has limited ability to scan, most

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 303–313, 2020 https://doi.org/10.1007/978-981-13-9409-6_36

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of these radars have no scanning capability. This paper proposes an airborne weather radar with a new scan mode, volume scan, something similar to WSR-88D radar. This scan mode can help radar to obtain high-resolution echo information in the 3D fanshaped space ahead of the flight. It may play an important role for the research and development of airborne weather radar technology, contributes to improving weather monitoring and ensuring flight safety as well. Table 1. Detection mode of existing airborne weather radars Radar Country/Company Wavelength Detection mode ELDORA US and France 3 cm (XRotary scanning of two (ASTRATA) Band) antennas (forward and backward) with an angle of 18.5° WXR-2100 Rockwell-Collins 3.1 cm (X- Emits two beams with Band) slight offset in the vertical (pitch) direction

CRS

ACR

Spider

Goddard Space Flight Center (NASA) UMass and JPL (NASA) Japan

3.2 mm (W-Band) 3 mm (WBand) 3 mm (WBand)

Scanning with a two-axis gimbal hanger (Airborne) RHI scan (Ground-based) Detect vertically downwards or upwards Scanning in the direction of the track from −40° to +95°

Application Convective storm detection with high resolution Detecting convective clouds that threaten aircraft safety Cloud measurement, especially cirrus Weak precipitation and snowfall Cloud measurement

Thunderstorm characterized by short life cycle, small range and strong destructive power refers to the deep moist convection phenomenon [3]. Thunderstorm weather is usually accompanied by lightning, thunder, rainstorm, gale, turbulence, hail and etc. which will affect aviation operation and threaten flight safety. According to the statistics of civil aviation organizations at home and abroad, the accidents directly and indirectly caused by thunderstorms account for more than 50% of all the flight accidents caused by meteorological reasons [4, 5]. At present, there are several methods mainly used for convective target identiﬁcation: threshold segmentation method [6– 10], algorithm based on image processing [11], Gaussian mixture model [12], Cluster analysis [13] et al. Among them, based on threshold segmentation is used most widely. It is found that SCI algorithm has higher accuracy rate of recognizing Convection Cell with higher reflectivity intensity, as it can correctly identify 68% storms with maximum reflectivity factor over 40 dBZ and 96% storms with maximum reflectivity factor over 50 dBZ [14]. Because of convection cell’s growth and dissipating, the multi-threshold method has a better identiﬁcation effect than a single threshold. Ultimately, this paper conducts a study on the thunderstorm identiﬁcation algorithm of simulated Airborne Weather Radar based on SCI algorithm.

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2 Airborne Weather Radar Scan Mode The scan mode of the simulated airborne weather radar during the flight is shown in Fig. 1. And its parameters are given in Table 2. The simulated airborne weather radar in this paper begins scanning from the lowest elevation angle, −15°. The scanning area is a sector area with azimuths of 120° and a radius of 60 km (Fig. 1a). Every time when the radar ends the current elevation scan, it will raise its antenna angle by 1° and scan the next elevation. And when the last elevation angle of 15° is end of scanning, a volume scan data will be formed (Fig. 1b). As a result, the airborne radar will have a volume scan data with 31 elevation scans, 120 radials per elevation scan and 600 sample volumes per radial. And it has a considerable detection space whose maximum vertical depth is 31 km and furthest distance is 60 km ahead of the flight altitude approximately. Therefore, the simulated airborne weather radar can obtain completely enough echo information. Even if there is a thunderstorm cell with a large horizontal scale (10–30 km) and a high echo top of more than 10 km, quite a number of its echo information can be collected by this radar. And a thunderstorm cell with very small scale that may not be found by ground-based weather radar can be detected by this radar for its high-resolution echo information.

Fig. 1. Simulated airborne weather radar scan mode (a) horizontal direction; (b) vertical direction

Table 2. Volume scan mode parameters of simulated airborne weather radar Parameter Altitude of aircraft Range resolution Azimuth scan range Azimuth resolution Maximum detection range Elevation number Elevation

Value 10 km 100 m −60 * +60° 1° 60 km 31 layers −15 * +15°

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3 Thunderstorm Recognition Algorithm In this paper, a thunderstorm identiﬁcation algorithm based on SCI is studied, and veriﬁed by the radar volume scan data that is simulated by one X-Band radar data of the 8th International Sounding Test conducted by WMO on July 2010 in Yangjiang City, Guangdong Province, China. SCI algorithm is proposed by the National Severe Storms Laboratory (NSSL) in Norman, Oklahoma (USSL), and the speciﬁc identiﬁcation process and parameters can be referred to Ref. [10]. Considering that thresholds of SCI algorithm are proposed according to S-band radar while X-band radar has marked attenuation in convective cloud observation especially [15] and there may be insufﬁcient correction even if attenuation correction is added, we reduce seven default reflectivity thresholds by 5 dBZ. In addition, in order to prevent small thunderstorm cell from being missed, the threshold of component area is changed from 10 km2 to 1 km2. And the threshold of segment length is modiﬁed from 1.9 km to1 km to prevent the thunderstorm cell which is shorter in radial direction and longer in azimuth direction from being omitted.

4 Thunderstorm Identiﬁcation Case Analysis Attenuation corrections have been made to the simulated airborne weather radar by the range-bin-by-range-bin correction algorithm. The thunderstorm identiﬁcation results of the reflectivity volume scan data simulated from X-band radar volume scan data 20100720125839 are taken as an example for analysis. There are two thunderstorm cells are identiﬁed by this thunderstorm identiﬁcation algorithm. Thunderstorm cells’ attributes are shown in Table 3 and their explanations are in Table 4. The attributes of the cell components contained in each storm cell are showing in Tables 5 and 6 respectively, and the meanings of these attributes can be found in Table 7. Thunderstorm cell whose serial number is 1 in Table 3 (thunderstorm cell 1 for short) contains seven cell components whose attributes are shown in Table 5. Since the strong echo region of thunderstorm cell 1 is already very small when the elevation angle reaches −8°, the component no longer exists above −8°. Thunderstorm cell with a serial number of 2 in the Table 3 (thunderstorm cell 2 for short) also contains seven cell components, and the attributes of these components are shown in Table 6. Because the strong echo region of thunderstorm cell 2 almost disappears at the elevation angle of −15°, the component only exists at elevations from −14 to −8°.

Serial number 1 2 Serial number 1 2

22.51 3.55

6.34 5.60

3.87 3.65

−15 –14

−9 −8

YSC (km)

−32 −13

HSC (km)

−25 −7

22.45 25.85

ZMAX (dBZ) HZMAX (km)

VIL (kg/km2)

22.55 34.25

−29.80 23.69 −11.78 20.56 2.23 54.5 4.75 33.36 −9.84 31.85 −5.44 31.38 4.46 51 3.32 7.14 TOP (km) Base (km) Loe EL (°) High EL (°) BEGAZI (°) ENDAZI (°) BEGRAN (km) ENDRAN (km)

XSC (km)

7 7 MSV (km3)

RS (km)

AS (°)

NC (unit)

Table 3. Thunderstorm cells’ attributes of 20100720125839 X-band simulated airborne weather radar data

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Attribute NC AS

RS

XSC

YSC

HSC ZMAX

HZMAX

VIL

Explanation The number of components in a storm cell The azimuth of the centroid (or mass weighted center) of a storm cell The (flat earth projected) range of the centroid of a storm cell The (flat earth projected) xcoordinate of the centroid of a storm cell The (flat earth projected) ycoordinate of the centroid of a storm cell The height of the centroid of a storm cell The maximum reflectivity factor (component) of the component s in a storm cell The height above ground of the sample volume corresponding to ZMAX The vertically integrated liquid

Attribute MSV TOP

Explanation The mass weighted volume of a storm cell The height of the highest component in a storm cell

BASE

The height of the lowest component in a storm cell

LOWEL

The elevation angle of the lowest component in a storm cell

HIGHEL

The elevation angle of the highest component in a storm cell

BEGAZI

The azimuth of the ﬁrst cell segment of a storm cell The azimuth of the last cell segment of a storm cell

ENDAZI

BEGRAN

ENDRAN

The range (slant) to the front (closest to the radar) of the ﬁrst sample volume of a cell segment The ending range (component), the slant range of the farthest part of a component (from the radar)

According to the beginning azimuth (ACbeg) and the ending azimuth (ACend), as well as the beginning range (RCbeg) and the ending range (RCend) of the components contained in each thunderstorm cell, the ranges of these components are marked on echo map that only reflectivity greater than 25 dBZ is retained. Cell components corresponding to thunderstorm cell 1 and thunderstorm cell 2 are respectively represented in red and blue lines. Combining Tables 3, 5, 6 and Fig. 2, it can be found that there are two thunderstorm cells here, one with a larger scale is located between 20 and 30 km at the azimuth of −30° and its elevations ranges from −9 to −15°, another with a smaller scale is located between 30 and 40 km at the azimuth of −10° approximately at elevations from −14 to −8°. Thunderstorm cell 1 is in the mature stage that the aircraft should avoid in time during flying. Thunderstorm

1 2 3 4 5 6 7

Comp

EL (°) −15 −14 −13 −12 −11 −10 −9

−29.74 −29.23 −29.88 −29.86 −29.88 −30.03 −30.32

AC (°)

RC (km) 23.83 23.62 23.51 23.42 23.42 23.47 23.61

XC (km) −11.82 −11.53 −11.71 −11.66 −11.67 −11.75 −11.92

YC (km) 20.69 20.61 20.39 20.31 20.31 20.32 20.38

HC (km) 3.87 4.32 4.75 5.17 5.57 5.96 6.34

DBZECmax (dBZ) 51.5 52.5 54.5 54.5 54.5 54 54.5

MC (km2) 3.21 3.01 4.11 3.84 3.88 4.77 3.55 ACbeg (°) −31 −30 −31 −31 −31 −32 −32

Table 5. Cell components information included in thunderstorm cell 1 ACend (°) −27 −27 −27 −27 −27 −25 −27

RCbeg (km) 23.85 23.35 22.65 22.55 22.45 22.65 23.55

RCend (km) 25.55 25.35 25.35 24.95 24.85 24.85 24.75

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1 2 3 4 5 6 7

Comp

EL (°) −14 −13 −12 −11 −10 −9 −8

−12.74 −9.82 −9.78 −9.89 −9.55 −9.61 −9.61

AC (°)

RC (km) 26.44 32.27 32.44 32.36 32.06 32.10 32.07

XC (km) −5.83 −5.50 −5.51 −5.56 −5.32 −5.36 −5.36

YC (km) 25.79 31.80 31.97 31.88 31.61 31.66 31.62

HC (km) 3.65 2.81 3.32 3.89 4.50 5.05 5.60

DBZECmax (dBZ) 34.5 48 51 49.5 49.5 49.5 49

MC (km2) 0.26 0.49 1.15 1.01 0.97 1.23 1.44 ACbeg (°) −13 −10 −10 −10 −10 −10 −10

Table 6. Cell components information included in thunderstorm cell 2 ACend (°) −11 −8 −8 −8 −8 −8 −7

RCbeg (km) 25.85 32.65 32.55 32.15 31.95 31.85 31.65

RCend (km) 28.25 34.25 34.25 33.95 33.35 33.15 33.25

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HC

YC

XC

RC

Attribute EL AC

Explanation The elevation angle of an elevation scan The azimuth of the mass weighted center of a component The slant range to the mass weighted center of a component The (flat earth projected) x-coordinate of the centroid of a component The (flat earth projected) y-coordinate of the centroid of a component The height above ground (of the mass weighted center) of a component RCend

RCbeg

ACend

ACbeg

Attribute DBZECmax MC

The (flat earth projected) range of the closest part of a component (to the radar) The slant range of the farthest part of a component (from the radar)

The most clockwise extent of a component

The most counterclockwise extent of a component

Explanation The maximum reflectivity factor in a component The mass weighted area of a component

Table 7. Explanations of attributes in Tables 5 and 6

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Fig. 2. Schematic diagram of cell components on echo map with reflectivity above 25 dBZ

cell 2 is in the development stage and the aircraft should pay attention to its development and take precautions. Because cell components with smaller default reflectivity thresholds are discarded and only components with larger threshold are retained in the identiﬁcation process, the plotted cell component area in Fig. 2 is not the whole area larger than 25 dBZ but the strongest echo area. Still, this thunderstorm recognition algorithm can identify thunderstorm cells well in general.

5 Conclusion This paper studies a thunderstorm identiﬁcation algorithm for the simulated airborne weather radar based on SCI algorithm, and gives an example of thunderstorm identiﬁcation. The results show that the algorithm can effectively identify the thunderstorm

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cells in the sector scan area in front of the plane, not only the mature thunderstorm cells, but also those in the development stage with a certain scale. The algorithm also outputs the attributes of identiﬁed thunderstorm cells, including centroid position, the top height, the bottom height, VIL and et al. This study is of guiding signiﬁcance for aircraft flight avoidance and veriﬁes the feasibility of the simulated airborne weather radar volume scan data simultaneously. Acknowledgements. Thanks to National Key R&D Program of China (2018YFC1506104) and Application and Basic Research of Sichuan Department of Science and Technology (2019YJ0316) for research direction and providing research foundation for this topic.

References 1. He L (2014) Research on signal processing technology of beam multi-scan airborne weather radar. Nanjing University of Aeronautics and Astronautics 2. Gao Y (2009) Research on key technologies of airborne weather radar detection system Beijing University of Posts and Telecommunications 3. Yu X, Zhou X, Yu X (2012) Progress of thunderstorm and severe convection near weather forecast technology. Acta Meteorologica Sinica 70(03):311–337 4. Wei X, Jiang H, Wang G et al (2013) Disaster analysis of thunderstorm to aviation flight. Meteorol J Inner Mongolia 4:42–44 5. Zhang X (2011) Analysis and identiﬁcation of thunderstorm weather and its impact on flight. J Changsha Aeronaut Vocat Tech Coll 11(2):49–54 6. Dixon M, Wiener G (1993) TITAN: thunderstorm identiﬁcation, tracking, analysis, and nowcasting—a radar-based methodology. J Atmos Oceanic Technol 10(6):785–797 7. Han L, Fu S, Zhao L et al (2009) 3D convective storm identiﬁcation, tracking, and forecasting—an enhanced TITAN algorithm. J Atmos Oceanic Technol 26(4):719–732 8. Wang L, Liu X, Wei M (2017) Simulation of adaptive hazard the weather warning method for airborne weather radar. J Syst Simul 29(07):1572–1581 9. Kyznarová H, Novák P (2009) CELLTRACK—convective cell tracking algorithm and its use for deriving life cycle characteristics. Atmos Res 93(1):317–327 10. Johnson JT, Mac Keen PL, Witt A et al (1998) The storm cell identiﬁcation and tracking algorithm: an enhanced WSR-88D algorithm. Weather Forecast 13(2):263–276 11. Lakshmanan V, Hondl K, Rabin R (2009) An efﬁcient, general-purpose technique for identifying storm cells in geospatial images. J Atmos Oceanic Technol 26(3):523–537 12. Choi J, Olivera F, Socolofsky SA (2009) Storm identiﬁcation and tracking algorithm for modeling of rainfall ﬁelds using 1-h NEXRAD rainfall data in Texas. J Hydrol Eng 14(7): 721–730 13. Lakshmanan V, Rabin R, De Brunner V (2003) Multiscale storm identiﬁcation and forecast. Atmos Res 67:367–380 14. Han L, Wang H, Tan X et al (2007) Research progress of storm identiﬁcation, tracking and early warning based on radar data. Meteorol Monthly 01:3–10 15. Lv B, Yang S, Wang J et al (2016) Data quality evaluation of X-band dual-line polarization doppler radar. J Arid Meteorol 34(6):1054–1063

FPGA-Based Fall Detection System Peng Wang1,2(&), Fanning Kong1, and Hui Wang1 1

College of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, China [email protected] 2 Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education of China, 150080 Harbin, China

Abstract. As there is a high tendency of falling in the independent living of the elderly and the post-fall injury is very serious. It is necessary to get timely assistance when they fall. The main objective of this work is to build an FPGAbased hardware implementation of video-based fall detection system. First of all, the moving object model will be extracted through background subtraction based on Gaussian Mixture Models (GMM). Second, we judge whether there is a fall through the aspect ratio, the effective area ratio, and the change in the center of the human body. Finally, if the old person falls, the detection system will sound-light alarm and send message to the elderly family and community through GSM. The experimental results demonstrate the accuracy of this fall detection system is up to 95% indoor and this system satisﬁces the requirement of real-time. Keywords: Fall detection

FPGA Background subtraction GSM

1 Introduction With the global aging population grows tremendously, large part of elderly people has to live alone while their children work outside. As reported that 28–35% from age group 65–75 falls at least once a year [1]. Falling exposes the elder to greater chances of suffering fall-related injuries [2]. Therefore, it is essential to put forward an automatically fall detection system for enabling the falling elder get immediate help to avoid any post-fall injuries or deadly cases due to delayed assistance. While various kinds of fall detection system have been researched in recent years, research in video-based fall detection has gained much attention [3]. Most of videobased fall detection which are implemented on the CPU or PC with software processing not targeting for real-time [4, 5]. FPGA has advantages in Processing speed because of the large number of processing cores that work in parallel in FPGA [6–8]. In this work, FPGA is selected as an accelerator to improve the performance of the system.

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 314–321, 2020 https://doi.org/10.1007/978-981-13-9409-6_37

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2 Over View of the System The presented system consists of a OV7725 digital camera, an automatic detection platform with Cyclone IV FPGA device (EP4CE15F17C8N), LEDs, a Buzzer and a GSM module. All computation of the system should be done inside the FPGA. LEDs, Buzzer and GSM module are driven by FPGA. The fall detection system is described in Fig. 1.

OV7725 Camera

LEDs

Automatic detection system with FPGA device

Buzzer GSM

Fig. 1. Overview of the fall detection system

3 Fall Detection Algorithm A number of algorithms for object detection have been presented. Object detection algorithms can be classiﬁed into Frame subtraction schemes, Optical flow method and Background subtraction. Background generation

Read frame

Background subtraction Object detection Image binaryzation Minimum bounding box

N

Aspect ratio>T1? Effective area ratio>T2? Y

N

Body center change >T3? Y Fall detection

Fig. 2. Flow chart of the fall detection algorithm

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The algorithm of Frame subtraction schemes cannot extract the full object image. Optical flow method is complicated to calculate and has poor real-time performance. In this work, we choose Background subtraction method with small amount of calculation and good real-time performance. Figure 2 shows the flow of the fall detection algorithm. 3.1

Background Generation

In this work, GMM (Gaussian Mixture Background Modeling) was adopted for background generation. Every single pixel was expressed the feature with Gaussian model according to the following Eq. (1). Fi xt ; ui;t ;

X

! ¼

i;t

T P1 1 1ðxt ui;t Þ ðxi ui;t Þ 1 e 2 2 n P ð2pÞ2 i;t

ð1Þ

Then the background model was built with the weighted sum of the K-Gaussian models by the Eq. (2). Pðxt Þ ¼

k X

wi;t Fi xt ; ui;t ;

i¼1

3.2

X

! ð2Þ

i;t

Moving Object Segmentation

After background generation, the next step was classifying the pixels into foreground object as the Eq. (3) shows. jIN ðx; yÞ IB ðx; yÞj [ T

ð3Þ

While at coordinate (x, y), IN (x, y) is the intensity value for the new pixel; IB (x, y) is the intensity value for the background pixel. T is a difference threshold which is predetermined. Meanwhile, the pixel will be classiﬁed as the background object if the condition in Eq. (3) is not fulﬁlled. 3.3

Fall Detection

The minimum bounding box was generated with its features (height, width and size) for foreground object following the image binarization. As we know, a standing person should have a height (H) greater than width (W). It turns out to a height to width ratio (aspect ratio) > T1, which T1 is the threshold, as shown in Fig. 3a. On the contrary, a person who is falling should have an aspect ratio < T1, as illustrated in Fig. 3b. In spite

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of that, this work has proposed the supplementary condition to distinguish the real-fall from the daily exercises by effective area ratio as shown in Eq. (4). Reffective ¼

SO SB

ð4Þ

where SO = the area of the foreground object. SB = the area of the bounding box. It will be detected as a fall if the Reffective greater than T2. T2 is the threshold that predetermined. Because normal movement is slow and the center changes little when the old man squats, push-ups or walks normally. Fall is a kind of rapid and violent phenomenon. During the fall down, the change of the center will suddenly increase. Finally, in order to further improve the accuracy of the system, the judgment results are corrected according to changes in the body center. After calibrating the minimum external bounding box of the human body, ﬁnd the center position O(x, y) of the human body, and correct the fall judgment result by using Eq. (5). Ok1y [ Oky ; qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ 2 Ok1y Oky þ ðOk1x Okx Þ2 [ T3

) ð5Þ

Compare human body centers Ok(x, y) and Ok–1(x, y) of two adjacent images. When the center of the human body in the kth frame image is lower than the body center in the k–1 frame image. And the distance between the two centers is greater than the threshold T3, the result of the determination is a fall [9]. Otherwise, it is not a fall.

(a) Person standing

(b) Personfalling down

Fig. 3. Aspect ratio, effective area ratio and body center of bounding box

4 Implementation on FPGA In this work, it has proposed a hardware implementation of fall detection system using FPGA. Figure 4 has shown the functional diagram of the system implemented in FPGA.

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CMOS sensor

Image capture

RAW to RGB

CMOS sensor config

Background generation SRAM

SRAM controller

FPGA

Algorithms

Write FIFO Read FIFO

SDRAM controller

Background subtraction

Minimum bounding box

SDRAM

Fall detection

LED controller

GSM controller

Buzzer controller

LEDs

GSM

Buzzer

Fig. 4. Overview of the fall detection system

Firstly, the CMOS sensor was conﬁgured through a conﬁguration block. The FPGA acquired the video streaming through a capturing module from the CMOS sensor. Then, change the raw data from Bayer format to RGB format through a conversion module. The video frames and the generated background models were stored in the SDRAM with the FIFOs. To detect the moving object, the new image frame and background model will be read together in pixels from the SDRAM. The foreground model was generated by setting their absolute difference threshold. SRAM controllerregisters holding parameters for the fall detection algorithms Finally, the FPGA will drive the alarm controller after a fall event being determined with the bounding box. The project was synthesized for an Altera Cyclone IV (EP4CE15F17C8N) FPGA device using Quartus II Design Suite. The behavioral simulations performed in ModelSim10.1d veriﬁed that the hardware modules Implemented the same functionality as MATLAB R2012a. Table 1 presents the resource usage of the FPGA. It is worth noting that the data from Table 1, even a small FPGA device from Cyclone IV series can run quite complex video-based fall detection system which resource utilization at about 35% of the available resources. Table 1. Project resource utilization Resource FF LUT6 LE DSP48 BRAM

Used 5457 5730 4922 54 22

Available 54,576 27,288 15,408 112 116

Percentage (%) 10 21 32 48 19

5 Results and Discussions An Altera Cyclone IV (EP4CE15F17C8N) FPGA device is used to implement the presented fall detection system. The hardware description language uses Verilog HDL. The same fall detection algorithm was implemented in MATLAB on a PC which contains Intel Core i3-4170 3.70 GHz CPU with 4 GB RAM.

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(a) standing

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(b) squatting

(c) lie down

(d) leg pressing

(e) falling down

(f) doing push-ups

Fig. 5. Fall detection with aspect ratio and effective ratio

Before this experiment, the subject has agreed to publish experimental images and data. and signed the agreement. After repeated experiments, we took 1.2 as the aspect ratio threshold. Using 0.45 as the effective area ratio threshold and 6.5 as the body center threshold in this work, take the ﬁrst 1000 image frames of the video for the tests. Figure 5a, b, c, d, e and f were detected by aspect ratio. Figure 5c, e and f were judgement as fall. Therefore, this work introduces effective area ratios and center changes as corrections for falls to improve the accuracy of the fall detection. 5.1

Accuracy of System

A large number of tests were conducted to assess the accuracy of the fall detection system. Table 2 shows the results of the tests. The true positive of the FPGA-based solution is lower than the MATLAB-based solution. This result could be simply because of the processing speed of FPGA is faster than MATLAB.

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Platform

True positive True negative False positive False negative (%) (%) (%) (%) FPGA 96.00 94.46 5.54 4 MATLAB 97.00 93.56 6.44 3 1 True Positive = correctly detected the number of falls/the actual total number of falls 2 True Negative = number of normal activities detected/total number of normal activities 3 False Positive = number of falls judged by mistake/total number of normal activities 4 False Negative = no number of falls detected/total number of actual falls

Table 2 shows that the fall detection accuracy of FPGA platform is 1% lower than the MATLAB platform. The result may be due to the FPGA processing speed is too fast. Causes the loss of image frames. 5.2

Processing Frame Rate

The processing frame rate of the implemented system was evaluated and is shown in Table 3. The results give a value of 58.82 fps with the resolution 640 480. The number of clock cycles required to complete a frame of image processing by the test system algorithm to calculate the frame rate of the video. In the meantime, the time required for MATLAB to conduct the same image frames processing was calculated. The results demonstrate that the performance of video-based fall detection in FPGA is near to 6X faster than the MATLAB.

Table 3. Detection time for a single frame on the FPGA and MATLAB Platform FPGA MATLAB

5.3

Max. (s) 0.017 0.128

Min. (s) 0.017 0.076

Avg. (s) 0.017 0.099

Frames per second (fps) 58.82 10.10

System Alarm Time

The average time of sound-light alarm response in Table 4 is 0.51 s. The average time of GSM message sending time is 4.97 s. The experimental results prove that the realtime nature of the fall detection alarm system based on FPGA can meet the system requirements. Table 4. The response time for falling alarm Alarm method sound-light alarm GSM

Max. (s) 0.73 6.85

Min. (s) 0.29 3.36

Avg. (s) 0.51 4.97

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Hence, FPGA is an effective instrument in this work to improve the performance of video-based fall detection system.

6 Conclusions In this work, we have presented a video-based fall detection system in FPGA. The experimental results demonstrate that this system is able to process up to 58.82 fps with the resolution of 640 480. At the same time, it could alarm automatically through the conﬁguration with Verilog HDL when a fall happened. This work shows the performance of better stability and lower false positives. And it also demonstrates good robustness of FPGA for fall detecting with image processing.

References 1. Tarabini M, Saggin B, Bocciolone M, et al (2016) Falls in older adults: kinematic analyses with a crash test dummy. In IEEE international symposium on medical measurements and applications, pp 1–6 2. Zhang D, He Y, Liu M, Yang HB et al (2016) Study on incidence and risk factors of fall in the elderly in a rural community in Beijing. Zhonghua liuxingbingxue zazhi 37(5):624–628 3. Rougier C, Meunier J, St-Arnaud A et al (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611 4. Asano S, Maruyama T, Yamaguchi Y (2009) Performance comparison of FPGA, GPU and CPU in image processing. In: International conference on ﬁeld programmable logic and applications, pp 126–131 5. Che S, Li J, Sheaffer JW et al (2008) Accelerating compute-intensive applications with GPUs and FPGAs. In: Symposium on application speciﬁc processors, pp 101–107 6. Ong PS, Ooi CP, Chang YC et al (2014) An FPGA-based hardware implementation of visual based fall detection. In: IEEE region 10 symposium, pp 397–402 7. Kryjak T, Komorkiewicz M, Gorgon M (2011) Real-time moving object detection for video surveillance system in FPGA. In: Conference on design and architectures for signal and image processing, pp 1–8 8. Wang Z (2015) Hardware implementation for a hand recognition system on FPGA. In: IEEE international conference on electronics information and emergency communication, pp 34–38 9. Liu H, Zuo C (2012) An improved algorithm of automatic fall detection. In: 2012 AASRI conference on computational intelligence and bioinformatics, p 6

Artiﬁcial Intelligence and Game Theory Based Security Strategies and Application Cases for Internet of Vehicles Zhiyong Wang1, Miao Zhang1, He Xu3, Guoai Xu1(&), Chengze Li2(&), and Zhimin Wu2(&) 1

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT), Beijing, China {lichengze,wuzhimin}@cert.org.cn 3 University College London, London, UK

Abstract. Information security of Internet of Vehicles (IoV) has attracted much attention in recent years. In view of security vulnerabilities existed in automobiles, many countries launch guidelines and cybersecurity standards concerning IoV security and plenty of new techniques have been applied to combat threats. In this paper, a variety of attacks on IoV are summarized and classiﬁed, then artiﬁcial intelligence and game theory based security countermeasures for IoV are highlighted, and their protection mechanisms are illustrated. Finally, a few application cases of artiﬁcial intelligence and game theory based security strategies for IoV is analyzed, aiming to provide helpful reference for the development of IoV security techniques. Keywords: Internet of vehicles (IoT) theory Security Application

Artiﬁcial intelligence(AI) Game

1 Introduction There are more than 1.2 billion motor vehicles across the globe now, and it is expected to hit two billion by 2035. It is estimated that over 125 million network connected automobiles will be manufactured between 2018 and 2022 [1]. In China, as of 2017, there were more than 17.8 million users of IoV [2]. IoV is regarded as a typical kind of Internet of Things (IoT), and it can ameliorate driving safety, provide convenience information and facilitate trafﬁc management. IoV implements the communications between vehicles and public networks via vehicle-to-road (V2R), vehicle-to-human (V2H), vehicle-to-vehicle (V2V), and vehicle-to-sensor (V2S) interactions. However, the rapid development of IoV raises concerns about security and privacy problems which can threaten driving safety and driver’s lives and invade people’s privacy. In 2015, security flaws of BMW vehicles equipped with connected drive were found to enable thieves to unlock doors and steal car data. Following this, more than 1.4 million of Chrysler cars in US were recalled due to network security problems [3]. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 322–329, 2020 https://doi.org/10.1007/978-981-13-9409-6_38

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In 2016, US National Highway Trafﬁc Safety Administration (NHTSA) launched “Cybersecurity Best Practices for Modern Vehicles”, in which cybersecurity standards, principles, and best practices for car industry were described to improve the information security of vehicles. In 2017, “White Papers of Network Security of Internet of Vehicles” was published by China Academy of Information and Communications Technology (CAICT), aiming to promote the safe development of IoV. Until now, plenty of security strategies have been put forward to ensure the security of IoV, such as encryption, intrusion detection system, secure routing protocols and key management. However, more effective and flexible methods need to be developed to meet the needs of the special features of IoV, including dynamic change of network topology, limited storage capacity and processing capacity of automobile terminal. Artiﬁcial intelligence can not only enhance the detection accuracy for threats, but also can ﬁnd hidden risks by learning from data without explicit programming. Game theory is also an intelligent tool to analyze the interaction process between the attackers and defenders. In this article, we will discuss the structure of IoV, security threats and countermeasures for network security. Meanwhile, artiﬁcial intelligence (AI) and game theory based security scheme for IoV will be emphasized, and the application cases of IoV with AI and game theory will be analyzed accordingly.

2 Literature Survey 2.1

Structure of IoV

Vehicle system architecture can be hierarchically grouped into four layers in terms of security, namely, external communication layer (Level 1), vehicle gateway layer (Level 2), in-vehicle network layer (Level 3), and hardware layer (Level 4) (shown in Fig. 1) [4].

Fig. 1. Structure of IoV

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External communication layer achieves the communications between vehicles and outside world by linking on-board communication equipment to V2X systems, Wi-Fi and mobile networks. Vehicle gateway layer regulate automotive systems in a vehicle like the headquarter, in which vehicle gateway connects internal ECUs (Electronic Control Units) to external communication equipment at Level 1 and controls message transfer in the in-vehicle networks. In-vehicle network layer is responsible for transmitting messages among ECUs, and it can be classiﬁed into multiple of network subunits in terms of ECU functions, such as body domain, control domain, and telematics domain. CAN (Controller Area Network) or LIN (Local Interconnect Network) is commonly used as communication protocols at this layer. Naturally, hardware layer is comprised of ECUs and various components that perform speciﬁc functions related to vehicle [4]. 2.2

Attack Classiﬁcation in IoV

Attacks can be mainly classiﬁed into ﬁve types in IoV: authentication attacks, attacks on availability, privacy attacks, attacks on routing and attacks of data authenticity. Attacks on authentication include Sybil attack, GPS camouflage, camouflage attack, and wormhole attack. With regard to availability attacks, interference on channel and service denial are two common attacks. Specially, the secrecy attacks steal customer data by interception or eavesdropping. With respect to routing attacks on routing, there are four attack types related to routing, including interception, camouflage, service denial and route modiﬁcation. In regard to data authenticity attacks, they can be categorized into the masquerading attack, replay attack, illusion attack and information fabrication and falsiﬁcation (shown in Fig. 2) [5].

Fig. 2. Attack model

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Countermeasures for IoV Security

A wide range of countermeasures have been proposed to prevent the threats on IoV according to special characteristics of IoV attacks, including establishing suitable model of threat, adopting honeypot system, constructing intrusion monitor system, employing privacy protection mechanism of routing, using reliable routing protocols and key management. With regard to threat model, constructing mathematical model and adopting graph-based methods are two main approaches for simulating threats. Intrusion detection system (IDS) can employ anomaly detection approach and signature detection method to hinder attacks through collecting and analyzing internal system’s information. In addition, SVM-based security framework and protocol analysis can offer protection for IoV security as well. Honeypots can realize protection by tempting and hoaxing attackers’ attention to avoid invading in vital system data in the context of IoV. Several secure routing protocols can not only perform normal routing functions, but also can restrain attacks on routing such as SAODV, Ariadne, and SRP protocol. Routing privacy protection mechanism contains a few of algorithms to guard against routine nodes data leakage, including SLPD, ALAR, and STAP. Key management is a crucial strategy for IoV security in that encryption is a signiﬁcant method for information security, and successful encryption relies on suitable key management. Additionally, pseudonym signature and certiﬁcateless signature can also provide effective protection (shown in Fig. 3) [5].

Fig. 3. Countermeasures for IoV security

2.4

Artiﬁcial Intelligence and Game Theory Based Security Strategies for IoV

Artiﬁcial intelligence (AI) is a kind of ability that a machine simulates human behavior intelligently and carries out speciﬁc tasks arranged by humans. Machine learning and deep learning are two main techniques to implement artiﬁcial intelligence. Some scholars argued that machine learning, deep learning and reinforcement learning can be

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adopted to safeguard the information security in Internet of Things (IoT). For example, AI can build up real-time behavioral modeling for net nodes, servers and equipment, and reduce new malware attacks and APT malware (Phishing, Adware, Trojans, etc.) [6]. Machine learning can defend against threats on IoV via gathering and storing right data, the vehicle’s internal network can be monitored by storing and analyzing logs, thereby detecting wicked threats and combating attacks. Once user logs are acquired, machine learning can check anomalies existed in the picture. Thus, machine learning can be able to analyze outside service data and information to detect unusual activities and malware attacks [7]. Loukas put forward a deep learning based intrusion detection approach to prevent cyber-physical attacks in IoV, which can enhance intrusion detection accuracy for vehicles compared with other deep learning and machine learning approaches [8]. Support Vector Machine (SVM) based detection system differentiates normal contents and anomalies by analyzing the training data of normal parts. Naturally, the input space is therefore classiﬁed into normal and abnormal parts [9]. Kang developed a Deep Belief Network (DBN) to detect intrusion for in-vehicle network, which ensure 97.8% accuracy and 1.6% false positive rate [10]. Vuong et al. adopted decision tree to search for command injection and denial of service threats on robotic vehicles, indicating the introduction of physical input characteristics can increase detection accuracy and eliminate the false positive rate [11]. Game theory is exploited to ﬁnd out optimal choices when facing conflicts. It refers to the process which individuals or organizations select strategies from action sets to make best decisions in a speciﬁc context [12]. Of these, security games focus on the interaction between malicious attackers and defenders and it is applied to detect intrusion in IoV networks. For instance, Buchegger and Alpcan proposed two-player zero-sum game to generate solutions to security of IoV, in which they deﬁned one player as attacker, and a group of mobile nodes as defender to imitate jamming and Sybil attacks in a vehicular network. The result showed the mobile nodes can improve their safeguard strategy by adopting zero-sum game approach [13]. In the aspect of understanding attack and defense comprehensively, game theory is proven to be an effective analysis tool. Alpan utilized game theory model based on noncooperation approach and provided Nash equilibrium analysis for lots of common network attack detection [14]. Chen mentioned abnormal network attack detection and provided mixed Nash equilibrium analysis [15]. Afterwards, Ismail [16] applied Chen’s conclusion into privacy attack detection of ammeter infrastructure data. However, most of conclusions are based on assumption that attacker’s identity is known already, and this assumption does not work due to high masquerading of attackers [14–16]. To resolve the unknown identity problem, some scholars employed Bayesian game theory to solve attack detection problem, whose rationale is based on credibility evaluation of attacker’s previous behaviors and real-time update by Bayesian theory [17–25]. In terms of features of mobile wireless ad hoc networks of IoV, researchers performed a numbers of studies on intrusion node detection and node communication incentive [26, 27]. Additionally, game theory is also used for intrusion detection of industrial control including networked control system [28] and energy system [29]. Apart from this, game theory is also widely applied in numerous of ﬁelds [30–32].

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Case Study of Artiﬁcial Intelligence and Game Theory Based Security Strategies for IoV

In the aspect of artiﬁcial intelligence, Kang et al. adopted deep neural network method to construct intrusion monitor system for security of IoV. In this system, parameters can be originated by utilizing deep belief networks as a pre-processing step. Following this, high-dimensional packet data can train neural networks to discriminate and analyze hacking and normal data’s statistical properties and ﬁnd out the relevant characteristics [33]. Regarding artiﬁcial intelligence’s practical application, “learn and prevent” device was developed through machine learning by Miller and Valasek, aiming to detect intrusion in the vehicle. The device is essentially a NXP micro-controller, and its simple board can be plugged into the OBD-II port. It can collect the typical data patterns of vehicle in the beginning of driving as observation mode, and then it changes to detection mode to monitor unusual information. Once any attacks are found, the automobile will be switched into “limp mode” to interrupt the networks and suspend vital functions like steering, then prevention and alert mode will be stimulated when any anomalies are found. The prevention mode enables the vehicle to neglect the malicious attacks and attackers can be inhibited, while alert mode empowers the driver to take actions by sending messages. With respect to game theory, Raya et al. designed a repudiation protocol for network security based on game theory method. Raya et al. presented three choices that each player can adopt according to the available protocols. Firstly, a player can give up the local repudiation step by choosing A due to mobile node’s unwillingness of involving in repudiation step. Secondly, a player can use vote V to ﬁght detected attacker by participating in local voting step. Finally, a player can perform invalidity procedure for attacker’s identity and its own identity and commit suicide. By introducing dynamic game, researchers deﬁne mobile nodes as players to solve the repudiation problem. Eventually, Raya et al. applied repudiation procedure based on gme theroy method to resolve practical problems. The protocol realizes quick and best repudiation process through motivating mobile nodes to be involved in repudiation process actively. Realistic simulation in IoV of this game theory method indicated a better tradeoff among various approaches [34].

3 Conclusions A wealth of countermeasures based on different theory and new techniques have been employed to defend against threats for the security of IoV. Among them, artiﬁcial intelligence and game theory based security strategies can prevent IoV from wicked attacks effectively and securely. Along with the occurrence of more application cases based on aforementioned two methods, artiﬁcial intelligence and game theory based secure scheme can play a stronger and broader role in cybersecurity of IoV in the future.

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With the rapid development of IoV and techniques, more malicious attacks will emerge and more effective techniques can be developed to ﬁght against threats in the future. Given this, any single technique cannot guarantee the absolute security of IoV, thus combined application of multiple of effective techniques may be a better way to prevent from threats of IoV networks. Additionally, the advent of 5G era and the emergence of a great number of innovative and effective techniques deﬁnitely bring new methods for IoV security protection and automobile manufacture industry to ensure the safe driving and facilitate the establishment of smart cities. Acknowledgements. This work is supported by the National Key Research and Development Program of China (Grant No.: 2018YFB0803605), the National Natural Science Foundation of China (Grant No.: 61897069), and the Foundation Strengthening Program for Key Basic Research of China (Grant No.: 2017-JCJQ-ZD-043). Guoai Xu, Chengze Li and Zhimin Wu are the corresponding authors.

References 1. Millman R (2018) Connected cars report: 125 million vehicles by 2022, 5G coming. In: Internet of business. https://internetofbusiness.com/worldwide-connected-car-market-to-top125-million-by-2022/ 2. Analysis of status development of internet of vehicles in China in 2018 (2018) In: RFID world. http://news.rﬁdworld.com.cn/2018_09/6746f0f84b2cd8cd.html 3. Takefuji Y (2018) Connected vehicle security vulnerabilities. IEEE Technol Soc Mag 37 (1):15–18 4. Tanaka M, Takahashi J, Oshima Y (2017) Cyber-attack countermeasures for cars. NTT Technical Rev 15(5):1–5 5. Sun YC, Wu L, Wu SZ, Li SP, Zhang T, Zhang L, Xu JF, Xiong YP, Cui XG (2017) Attacks and countermeasures in the internet of vehicles. Ann Telecommun 72:283–295 6. Lee GM Artiﬁcial intelligence (AI) for development series: report on AI and IoT in security aspects. ITU. 10 7. Causevic D How machine learning can enhance cybersecurity for autonomous cars. Total. https://www.toptal.com/insights/innovation/how-machine-learning-can-enhancecybersecurity-for-autonomous-cars 8. Loukas G, Vuong T, Heartﬁeld R, Sakellari G, Yoon Y, Gan D (2018) Cloud-based cyberphysical intrusion detection for vehicles using deep learning. IEEE Spec Sect Secur Anal Intell Cyber Phys Syst 6:3491–3508 9. Carlos A, Catania FB (2012) An autonomous labeling approach to support vector machines algorithms for network trafﬁc anomaly detection. Expert Syst Appl 39(2):1822–1829 10. Kang MJ, Kang JW (2016) Intrusion detection system using deep neural network for invehicle network security. PLoS ONE 11(6):1–17 11. Vuong TP, Loukas G, Gan D (2015) Decision tree-based detection of denial of service and command injection attacks on robotic vehicles. IEEE Int Work Inf Forensics Secur 1–6 12. Liang XQ, Yan Z (2019) A survey on game theoretical methods in Human-Machine networks. Futur Gener Comput Syst 92:674–693 13. Buchegger S, Alpcan T (2008) Security games for vehicular networks. In: Proceedings of the 46th annual allerton conference on communication, control and computing, pp 244–251

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14. Alpcan T, Basar T (2011).Network security: a decision and game-theoretic approach. Cambridge University Press 15. Chen L, Leneutre J (2009) A game theoretical framework on intrusion detection in heterogeneous networks. IEEE Trans Inf Forensics Secur 4(2):165–178 16. Ismail Z, Leneutre J, Bateman D, Chen L (2014) A game theoretical analysis of data conﬁdentiality attacks on smart-grid AMI. IEEE J Sel Areas Commun 32(7):1486–1499 17. Liu Y, Comaniciu C, Man H (2006) A Bayesian game approach for intrusion detection in wireless ad hoc networks. In: ACM international conference proceeding series 18. Nguyen KC, Alpcan T, Basar T (2009) Security games with incomplete information. In: IEEE international conference on communications 19. Zhang Y, Tan XB (2011) Perception method of internet security based on Markov game theory model. J Softw 22(3):495–508 20. Hu H (2011) Strategy model of internet security based on Markov game theory. J Xi’an Jiaotong University 45(4):18–24 21. Fu Y (2009) Study on strategy selection of attacks and defenses of internet. J Beijing Univ Posts Telecommun 32(1):35–39 22. Zhu Q, Tembine H, Basar T (2010) Network security conﬁgurations: a nonzero-sum stochastic game approach. In: American control conference 23. Nguyen KC, Alpcan T, Basar T (2009) Stochastic games for security in networks with interdependent nodes. In: International conference on game theory for networks 24. Nguyen KC, Alpcan T, Basar T (2010) Security games with decision and observation errors. In: American control conference 25. Jiang W (2009) Security evaluation and optimal active defense based on game theory model. Chin J Comput 32(4):817–827 26. Sagduyu YE, Berry R, Ephremides A (2009) MAC games for distributed wireless network security with incomplete information of selﬁsh and malicious user types. In: International conference on game theory for networks 27. Zhu Q, Fung C, Boutaba R, Basar T (2012) GUIDEX: a game-theoretic incentive-based mechanism for intrusion detection network. IEEE J Sel Areas Commun 30(11):2220–2230 28. Amin S, Schwartz GA, Sastry SS (2013) Security of interdependent and identical networked control system’s. Automatica 49(1):186–192 29. Maharjan S, Zhu Q, Zhag Y, Gjessing S, Basar T (2012) Dependable demand response management in the smart grid: a Stackelberg game approach. IEEE Trans Smart Grid 61 (8):3693–3704 30. Manshaei M, Zhu Q, Alpcan T, Basar T, Hubaux JP (2013) Game theory meets network security and privacy. ACM Comput Surv 45(3):1–39 31. Roy S, Ellis C, Shiva S, Dasgupta D, Shandilya V, Wu Q (2010) A survey of game theory as applied to network security. In: Proceedings of the 43rd Hawaii international conference on system sciences 32. Liang X, Xiao Y (2013) Game theory for network security. IEEE Commun Surv Tutor 5 (1):472–486 33. Kang MJ, Kang JW (2016) A novel intrusion detection method using deep neural network for in-vehicle network security. Proc IEEE VTC Fall 1–5 34. Raya M, Manshaei MH, Felegyhazi M, Hubaux JP (2008) Revocation games in ephemeral networks. In: Proceedings of ACM conference on computer and communications security (CCS)

The Eﬀect of Integration Stage on Multimodal Deep Learning in Genomic Studies Fariba Khoshghalbvash(B) and Jean X. Gao Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA [email protected]

Abstract. With recent advances in high-throughput sequencing, reading the human genome is not an arduous task anymore. The extensive collection of diﬀerent types of omics data and possible causal relations between them have led the scientists to exploit specialized machine learning methods such as deep learning and perform integrative analysis of multi-source datasets. In this paper, we compare the performance of both generative and discriminative deep models based on their integration stage. First, we explain the architecture and mathematical point of view of these methods. Then we evaluate the performances of diﬀerent models by applying them on two sets of cancer-related data to discover the eﬀect of the integration stage on classiﬁcation accuracy.

Keywords: Data integration

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· Omics · Deep learning

Introduction

With recent developments in next-generation sequencing, a vast amount of heterogeneous omics datasets are available for analysis. It has been demonstrated that due to possible relations between input modalities, an integrative analysis can be more beneﬁcial than single-input studies. For example, in the earlier era of integrative studies, Srivastava and Salakhutdinov [1] suggested that by combining text data and image data one can get better results. Shortly after, integrative analysis stepped into other research areas, and due to possible causal and regulatory relations between diﬀerent genomic data sources, scientist proposed multimodal models that are superior to single source old-fashion machine learning methods. Previously, methods like Bayesian Networks [2] and Kernel-based methods [3] have been applied on multiplatform genomic datasets. However, using classical machine learning algorithms for this purpose is limited to two diﬀerent approaches. One is to join input modalities by simple concatenation prior to any model training. Although this approach can reduce complexity, it is not applic Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 330–338, 2020 https://doi.org/10.1007/978-981-13-9409-6_39

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cable when two data modalities carry diﬀerent characteristics and have diﬀerent natures such as text and image. The second method is to train individual models for each modality and compute the average result to perform an ensemble learning technique. However, ensemble learning fails to capture between modality relations and is only beneﬁcial for getting a more robust result. On the other hand, specialized machine learning methods such as deep learning are able transform a high-dimensional feature set and reach the abstract representation of any kind of input which can be integrated during the model training process. Deep Neural Networks (DNNs) are feed-forwarding artiﬁcial neural networks consist of an input layer, multiple hidden layers, and an output layer [4, 5]. A DNN-based structure can reveal possible dependencies between distinct modalities. By selecting a speciﬁc sub-network for each modality in the lower layers, one can get the abstract representations and then integrate them at the higher levels. This provides the ﬂexibility of applying a deep learning method which suits the modality in a speciﬁc sub-network. Although, many studies have shown the superiority of deep integrative techniques [6–8], to our best knowledge, no study examines the eﬀect of the integration stage in multimodal deep approaches. In this paper, we perform a classiﬁcation task using cancer-related datasets by constructing both discriminative and generative deep models. Based on the integration stage and being supervised or semi-supervised we build six deep networks and compare their performances to examine the eﬀect of the integration stage on classiﬁcation.

2

Data

The dataset that is used in this study, contains normal and tumor samples of BReast CArcinoma (BRCA) and LUng ADenocarcinoma (LUAD) from TCGA. We downloaded miRNA (1870 genes) and gene Expression (20,530 genes) using TCGA-Assembler tool [9, 10] and lncRNA (12,727 genes) from TANRIC [11]. For BRCA datasets, we chose the three subtypes with the most population including Inﬁltrating Ductal Carcinoma (335 patients) and Inﬁltrating Lobular Carcinoma (66 patients), and Normal cases (73 patients) to construct a multiclass dataset. LUAD dataset is also categorized into three stages of Normal (18 patients), Stage I (226 patients), and a mixture of Stage II and III (168 patients). There might be a number of zero variance features in each modality. In order to reduce the computation and perform a more reliable analysis all zero variance feature are removed and all of the features are scaled between (0, 1). The remaining genes (with non-zero variance) of miRNA, gene Expression (geneExp), and lncRNA are 1562, 20,212, and 12,682 (in the same order) for BRCA and 1596, 20,161, and 12,610 for LUAD (Table 1).

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3

Class groups (# samples) Modalities Original size Reduced size

LUAD Normal (18) Stage I (226) Stage II, III (168)

miRNA GeneExp lncRNA

1870 20,530 12,727

1596 20,161 12,610

BRCA Normal (73) Ductal Carcinoma (335) Lobural Carcinoma (66)

miRNA GeneExp lncRNA

1870 20,530 12,727

1562 120,212 12,682

Methods

In this study, three discriminative networks are used to directly use the original input sets and perform a completely supervised classiﬁcation task. The diﬀerence between these networks is the layer in which integration takes place. Let’s name the layers in a general deep network with L layers h(0) , . . . , h(L) where h(0) is the input layer and h(L) is the output layer. Then the kth layer for k = 1, . . . , L is calculated as: (1) h(k) = σ W (k−1) × h(k−1) + b(k−1) . where σ is the activation function and W (k−1) is the weight matrix between the kth and (k − 1)th hidden layer and b(k−1) is the bias term. In a multimodal deep network, with Lm hidden layers in modal-speciﬁc sub-network for modality m, for k = 1, . . . , Lm hidden layers are computed as: (k−1) . (2) × h(k−1) + b(k−1) h(k) m = σ Wm m m In order to integrate three modalities of M ∈ {miRNA, geneExp, lncRNA} the ﬁrst general hidden layers (h(1) ) is calculated as: (1) (Lm ) (Lm ) (Lm ) W m × hm + bm (3) h =σ m∈M

For late integration, h(1) is also the ﬁnal output layer. In this paper, concatenating input modalities in the ﬁrst layer of the network (Fig. 1a) is called Early Integration (EI-DNN), building individual sub-networks followed by shared hidden layers (Fig. 1b) is called Middle Integration (MI-DNN), and integrating the individual outputs (Fig. 1c) is Late Integration (LI-DNN). It is worth mentioning that in LI-DNN all four sets of outputs are considered to minimize the multi-objective loss function. The loss functions is for the supervised network is categorical cross-entropy (CCE): CCE = −

N C 1 1y ∈C log pmodel [yi ∈ Cc ] N i=1 c=1 i c

(4)

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333

where N is the number of samples, C is the number of classes, 1yi ∈Cc is an indicator function of the ith sample belonging to class c, and pmodel [yi ∈ Cc ] is the predicted probability of the ith sample that belongs to class c. In semi-supervised learning, prior to discriminative classiﬁcation, a generative network such as AutoEncoder (AE) which is constructed of two parts (encoder and decoder) is used to reduce the input size. The encoder transfers the original input space and represents it in another new space with lower dimension. Then, the decoder uses the encoded space to reconstruct the original input. When the error of the reconstruction is minimized, the encoded data can be counted as a good representation of the initial input. The objective function in AE is Mean Squared Error (MSE): N (xi − R(xi ))2 (5) MSE = i=1

where R(xi ) is the reconstruction of sample xi or in other words, the out put of the AE.

(a) EI-DNN

(b) MI-DNN

(d) EI-AE

(e) MI-AE

(c) LI-DNN

(f) LI-AE

Fig. 1. Deep architectures used for classiﬁcation.

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Later, the encoded input is fed to one of the three aforementioned discriminative networks. Based on the integration stage, three semi-supervised model are designed. EI-AE-DNN and MI-AE-DNN are used when the shared encoded input from EI-AE and MI-AE (Fig. 1d, e) is used to train a simple single input DNN. If we apply separate AEs, to extract individual encoded inputs (Fig. 1f) and then use them as three encoded input modalities for an LI-DNN (Fig. 1c) it is called LI-AE-DNN.

4

Experimental Results

We applied the six aforementioned multimodal deep networks on breast cancer data described in Sect. 2 which is categorized into three groups to perform a multi class classiﬁcation task. We used stratiﬁed 10-fold cross validation to run our experiment. Speciﬁcally, we trained and tested all the models for 10 iterations. During each iteration, %90 of the data was used for training and %10 for testing. Moreover, %20 of the training set was used for validation of the network. In total during each iteration, %72 of data was used for training, %18 for validation, and %10 for testing. We collected all the test results to compute Accuracy (Acc), Precision (Pre), F1-Score, Matthews correlation coeﬃcient (Mcc), construct Precision-Recall Curves (PRC), and build confusion matrices. Note that the confusion matrices carries normalized values, therefor the total number of samples in each class is converted to 100. Results in Table 2, Fig. 2 suggest that middle stage integration has superiority compared with other stages on integration in both supervised and semi-supervised learning. Although, the supervised task is more promising. Moreover, Fig. 3 suggests that discriminative analysis is more capable of handling imbalanced data distribution. Table 2. Result of deep integrative classiﬁcation of BRCA data. Method

Accuracy (%) Precision (%) F1-score (%) AUC (max = 1)

EI-DNN

84.18

83.85

82.04

0.79

MI-DNN

89.03

87.96

87.43

0.93

LI-DNN

86.08

85.53

81.93

0.88

EI-AE-DNN 70.68

49.95

58.53

0.58

MI-AE-DNN 87.97

86.34

85.71

0.93

LI-AE-DNN

49.95

58.53

0.61

70.68

The Eﬀect of Integration Stage on Multimodal Deep

(a) Average PRC

335

(b) Average PRC

Fig. 2. Average precision recall curves and metrics comparison for BRCA dataset.

(a) EI-DNN

(b) MI-DNN

(c) LI-DNN

(d) EI-AE-DNN

(e) MI-AE-DNN

(f) LI-AE-DNN

Fig. 3. Confusion matrices for BRCA dataset.

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Validation

We validated our observation by using another datasets associated with lung cancer explained in Sect. 2. Although due to lower number of samples general performance is not as good as using BRCA datasets, it is still demonstrated that middle stage integration led to better result compared to other methods (Fig. 4 and Table 3). By examining the confusion matrices in Fig. 5, it can be inferred that Normal samples can be distinguished from Tumor samples more easier that samples with diﬀerent stages. This can be due to similarities between cancer stages or caused by possible noise in clinical categorization of diﬀerent patients and assigning wrong labels due to lack of in depth knowledge. Table 3. Result of deep integrative classiﬁcation of LUAD data. Method

Accuracy (%) Precision (%) F1-score (%) AUC (max=1)

EI-DNN

51.21

54.81

51.41

0.49

MI-DNN

60.68

60.87

60.72

0.57

LI-DNN

56.55

55.06

49.10

0.59

EI-AE-DNN 49.27

47.79

48.41

0.48

MI-AE-DNN 60.44

59.73

56.65

0.62

LI-AE-DNN

47.72

48.26

0.52

52.43

(a) Average PRC

(b) Average PRC

Fig. 4. Average precision recall curves and metrics comparison for LUAD dataset.

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(a) EI-DNN

(b) MI-DNN

(d) EI-AE-DNN

(e) MI-AE-DNN

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(c) LI-DNN

(f) LI-AE-DNN

Fig. 5. Confusion matrices for LUAD dataset.

6

Discussions and Conclusions

Genomic data integration using deep networks has been one of the most popular research areas during the past years. Diﬀerent multimodal deep models can be formed by applying the integration task at diﬀerent stages in both supervised and semi-supervised classiﬁcation. Although it has been demonstrated that deep models achieve competitive or superior results compared to classical classiﬁcation algorithms, there remains a question about when to apply integration and whether the integration stage has any signiﬁcant impact on classiﬁcation result or not. In this work, we conducted a comprehensive study to examine the performance of models with diﬀerent architectures to ﬁnd a possible answer for the mentioned question. We constructed six diﬀerent deep models according to their integration level and being supervised or unsupervised. Following this, these models were applied to two sets of real cancer-related data. According to the result extracted from our experiments, middle stage integration has superiority in both supervised and semi-supervised cases. Promising result and the ability to integrate diﬀerent modalities with diﬀerent characteristics in the middle stage of model training shows the necessity of deep models in multimodal studies. In addition to this, supervised models seem to be more useful in dealing with imbalanced class distribution. We believe this paper will be insightful

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for future studies on integrative models. A possible interesting future research direction is to extract between modality relations for further analysis other than classiﬁcation such as biomarker discovery.

References 1. Srivastava N, Salakhutdinov RR (2012) Multimodal learning with deep Boltzmann machines. In: Advances in neural information processing systems, pp 2222–2230 2. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M (2003) A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644): 449–453 3. Lanckriet GR, De Bie T, Cristianini N, Jordan MI, Noble WS (2004) A statistical framework for genomic data fusion. Bioinformatics 20(16):2626–2635 R 4. Bengio Y et al (2009) Learning deep architectures for AI, foundations and trends. Mach Learn 2(1):1–127 5. Hinton G, Deng L, Yu D, Dahl GE, Mohamed A-R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97 6. Liang M, Li Z, Chen T, Zeng J (2015) Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 12(4):928–937 7. Sun D, Wang M, Li A A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform 8. Chaudhary K, Poirion OB, Lu L, Garmire LX (2018) Deep learning-based multiomics integration robustly predicts survival in liver cancer. Clin Cancer Res 24(6):1248–1259 9. Zhu Y, Qiu P, Ji Y (2014) Tcga-assembler: open-source software for retrieving and processing tcga data. Nat Methods 11(6):599 10. Wei L, Jin Z, Yang S, Xu Y, Zhu Y, Ji Y (2017) Tcga-assembler 2: software pipeline for retrieval and processing of tcga/cptac data. Bioinformatics 34(9):1615–1617 11. Li J, Han L, Roebuck P, Diao L, Liu L, Yuan Y, Weinstein JN, Liang H (2015) Tanric: an interactive open platform to explore the function of LNCRNAS in cancer. Cancer Res CANRES—0273

An Advanced Aerospace High Precision Spread Spectrum Ranging System Technology Ning Liu1(&), Pingyuan Lu2, and Xiaohang Ren3 1

3

Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected] 2 Shanghai Aerospace Electronic Co., Ltd., Shanghai, China [email protected] Changping NCO School of the Equipment Institute, Beijing, China [email protected]

Abstract. First introduced the working principle of pseudo-code ranging for aerospace spread spectrum ranging system. An advanced method of spread spectrum ranging based on on-orbit automatic correction is proposed. The ranging error is analyzed, and the measured data is used to verify the effectiveness of the method. Keywords: Spread spectrum ranging analysis and veriﬁcation

Automatic correction Ranging error

1 Introduction The space measurement and control communication system adopts the spread spectrum system, and its core is to introduce digital communication technologies such as pseudocode ranging, pseudo-code spread spectrum, code division multiple access, timedivision multi-channel, etc. into the system to realize telemetry, remote control and measurement of satellites [1]. Functions such as distance, speed measurement, tracking, angle measurement, and digital transmission complete the measurement and control tasks, and realize multi-target measurement and control communication by code division multiple access [2]. The spread spectrum ranging technology has excellent characteristics such as high ranging accuracy, no fuzzy distance and strong anti-interference performance. It is more and more widely used in satellite navigation and timing, satellite measurement and control [3]. Orbit determination and inter-satellite ranging and time synchronization have become the ﬁrst choice for precision ranging in today’s complex electromagnetic environment.

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 339–347, 2020 https://doi.org/10.1007/978-981-13-9409-6_40

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2 Basic Principle of Pseudo-Code Ranging The principle of radio ranging is to measure the transmission delay of radio waves. The distance is calculated by ﬁrst transmitting a radio wave and then measuring the delay generated by the signal forwarded by the target relative to the transmitted signal [4]. The relationship between the target distance R and transmission time s is R ¼ s c=2

ð1Þ

where: c is the radio propagation speed (speed of light). Therefore, ranging is the measurement delay. The direct sequence spread spectrum system measures the distance between the spacecraft and the ground monitoring station by utilizing the phase difference between the receiving end and the received arriving signal [5]. The principle of ranging based on direct spread spectrum technology is that the ground station controls the spread spectrum and carrier modulated signals. The spacecraft receives the signal and forwards it back to the ground. The ground solves the phase of the local signal and the received signal. Thus giving the distance between the satellite and the station. In the ranging process, the two-way spread spectrum communication task between the stars and the ground is simultaneously completed. After the ground monitoring station performs the frequency conversion processing and the related despreading processing on the receiver part, the phase difference s0 between the received signal and the transmitted signal is obtained by the TDOA detection technology, and the time difference of arrival (TDOA) can be calculated according to the Eq. (2). This gives the distance L between the stars and the ground. The relationship between L and the phase difference s0 is expressed by the Eq. (3). TDOA ¼ s0 Tc Tz Tj L ¼ TDOA

C C ¼ ðs0 Tc Tz Tj Þ 2 2

ð2Þ ð3Þ

where L is the distance between the stars and the ground, TDOA is the time difference of arrival, s0 is the number of symbols of the difference between the local sequence of the base station and the received sequence, Tc is the width of the spreading code, C is the speed of light, Tz is the spacecraft frequency conversion The processing time (generally a constant, assumed to be known), Tj is the processing time for the base station to solve the TDOA, i.e. the phase capture time, (generally related to the time function, assumed to be known).

3 Optimization Design of Pseudo Code Ranging System 3.1

Conventional Pseudo-Code Ranging System

In the aerospace measurement and control system, various pseudo-code ranging implementations are based on the above principles, and the basic methods are the same.

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The working process of the measurement and control system is as follows: the receiving antenna receives the uplink RF signal from the ground spread spectrum monitoring and control station, and the signal form is two BPSK modulated spread spectrum signals, which have the characteristics that the carrier is suppressed [6]. The uplink RF signal is received by the receiving branch into the receiving channel of the spread spectrum transponder to complete low noise ampliﬁcation, down conversion, intermediate frequency ﬁltering, intermediate frequency signal ampliﬁcation and AGC control. The uplink IF signal enters the digital baseband portion for A/D sampling, and the sampled data performs a series of processing operations in the digital baseband [7]. After completing the transmission code acquisition and tracking, carrier acquisition and tracking, and remote control information bit synchronization, the uplink remote control channel sends the solved remote control PCM code together with the synchronous clock and the strobe pulse to the remote control subsystem for subsequent processing; the downlink telemetry channel receives After the telemetry PCM code and the synchronization clock from the telemetry unit, the telemetry PCM code is spread and BPSK modulated; the measurement channel uplink and downlink signals adopt a prescribed measurement frame structure, and the downlink measurement frame is ﬁlled with the transponder state information, such as pseudo code information, PseudoDoppler measurement information, etc. The typical aerospace measurement and control system terminal structure is shown in Fig. 1.

Power and command interface TC

IF signal

Receiving channel

uplink RF signal

Digital baseband 1553B

IF signal

Lower computer module

Launch channel

downlink RF signal

Power amplifier

typical TT&C Terminal Fig. 1. Conventional spread spectrum ranging terminal principle

The uplink signal and the downlink signal are designed as follows: the uplink remote control and the ranging are both PCM/CDMA/BPSK, sharing the same carrier frequency, and the uplink remote control signal and each ranging signal are independent of each other, and the signals are distinguished by code division multiple access;

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Both downlink telemetry and ranging use PCM/CDMA/BPSK, sharing the same carrier frequency. The downlink telemetry signal and the ranging signal are independent of each other, and each signal is distinguished by code division multiple access. The modulated spread spectrum signal is upconverted, RF ﬁltered, and power ampliﬁed in the transmit channel, and then the RF signal is transmitted from the antenna to the ground spread spectrum measurement and control station to form a downlink. 3.2

Advanced High Precision Pseudo-Code Ranging System

In the conventional spread spectrum ranging system, the most important component device is the measurement and control terminal, and the distance value corresponding to the delay of the system itself is called the distance zero value, and the value fluctuates with various factors. For example, the consistency of the switch, the change of the level of the ranging signal, the number of uplink ranging signals, and the temperature change of the measurement and control terminal itself. Aiming at the zero-value fluctuation caused by the above-mentioned factors, this paper proposes an improved high-precision ranging system, which adopts the method of adding self-calibration module to detect the change of the distance zero value of the measurement and control terminal with various factors. And feedback to the uplink ranging signal, by effectively detecting the self-calibration value and performing cancellation, the distance zero value fluctuation can be effectively eliminated, thereby obtaining the ranging value of the modiﬁed accuracy. The system is introduced as follows. 3.2.1 System Structure The self-calibration module is added to realize the self-closing function of the baseband self-calibration signal and the receiving transmission channel. The self-calibration function can offset the influence of device aging and temperature on ranging. The main principle is: the uplink signal and the downlink signal are combined into the receiving channel, and the baseband module demodulates the uplink and downlink signals to calculate the distance of the transponder itself. The zero value is sent to the ground through the downlink measurement frame to achieve high-precision ranging. According to the actual use of the answering machine, in order to reduce the influence of the level on the ranging signal, the self-calibration signal power can be adjusted to any value within the working level of the transponder through software setting. The self-calibration module is mainly considered in design. Group delay fluctuations and level control accuracy problems in the school channel. The block diagram of the improved high-precision pseudo-code ranging terminal is as follows (Fig. 2).

An Advanced Aerospace High Precision Spread Spectrum Ranging

Power and command interface Receiving channel

IF signal

TC Digital baseband 1553B

Launch channel

IF signal

Lower computer module

uplink RF signal

RF signal

Local oscillator signal

343

Selfcalibration module

RF signal

downlink Power RF signal amplifier

Improved high-precision pseudo-code ranging terminal

Fig. 2. Improved high-precision pseudo-code ranging terminal block diagram

Among them, the core part of the self-calibration module principle block diagram as shown (Fig. 3).

Receiving channel

Isolator

Uplink filter

Control signal TTL Secondary power supply +5V, -5V

Programmed attenuator

Secondary power supply filtering Reference clock 10MHz

Launch channel

uplink RF signal

Combiner

Downlink filter

Isolator

Coupler

downlink RF signal

Self-calibration module

Fig. 3. Self-calibration module block diagram

The uplink signal is sent to the receiving front end of the on-board measurement and control terminal as an input signal by the combiner and the uplink ﬁlter; a part of the downlink output signal is outputted to the back-end power ampliﬁer through the down-converter, and the other part is set by the coupler. The appropriate coupling degree is used to control the amplitude of the self-calibration frequency conversion control module; the self-calibration frequency conversion control module converts the downlink frequency to the uplink frequency, and adjusts the self-calibration signal power level sent to the receiving channel through the baseband control signal.

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The difference between a self-calibration channel and a normal ranging channel is: (1) The self-calibration channel clearly knows the code phase and carrier frequency that it sends. (2) The self-calibration parameter is a slow-change parameter, so the self-calibration channel can be accumulated for a long time to improve the measurement accuracy. 3.2.2 Frequency Flow The frequency scheme of the improved high-precision pseudo-code ranging terminal proposed above is as follows. The receiving channel performs two down conversions on the uplink RF signal. The converted intermediate frequency signal contains the uplink remote control information and measurement information. After the A/D signal is sampled by the A/D, the FPGA despreads the signal. For demodulation work, the FPGA also constructs downlink measurement frame information, and collects the telemetry digital signal for spread spectrum modulation processing and then sends it to the transmission channel to complete the RF frequency conversion work in the transmission channel. The selfcalibration module converts the downlink RF signal into an uplink signal frequency point, and combines with the uplink signal to enter the receiving channel to realize the self-calibration function. In the frequency flow, the M1, M2, N1, N2, and L1 parameters can be set, and the parameter coverage can achieve full coverage of the S-band. 3.2.3 Error Analysis Compared with conventional spread spectrum ranging technology, the difference between high-precision spread spectrum technology mainly has the following two points. First, the software realizes millimeter-level ranging error using carrier smoothing pseudo-range technology. Theoretical analysis and simulation veriﬁcation are as follows: (1) Assume that the pseudo code rate RPN is equal to 10 MHz, Then the chip interval TC is equal to 100 ns,According to engineering experience, the accuracy of the code tracking loop TC0 can generally reach TC =100, that is 1 ns. Therefore, the magnitude of the pseudorange measurement error is c TC0 ¼ 0:3 m. (2) Assuming that the signal carrier frequency fc is 2 GHz, the carrier period T is 0.5 ns, and the accuracy of the carrier recovery loop can generally reach 10°, the time error ðT 0 Þ based on the carrier measurement can reach T 0 ¼ T=36 ¼ 0:014 ns, and the corresponding pseudorange measurement error is c T 0 ¼ 0:0042 m. It can be seen from the performance comparison that the ranging accuracy obtained after the carrier smoothing pseudorange is greatly improved. Second, the self-calibration channel is used to eliminate the change in the zero value of the transponder. The analog ﬁlter mainly includes an out-of-band rejection ﬁlter of the radio frequency port, an intermediate frequency image frequency ﬁlter, and a channel selection

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ﬁlter. The higher the center frequency of the ﬁlter and the smaller the number of ﬁlter stages, the smaller the delay of the ﬁlter. Generally, the delay of the RF ﬁlter is relatively small, and the delay is relatively small with temperature fluctuations. The channel delay error is mainly caused by changes in material transmission characteristics and changes in ﬁlter component parameters due to environmental changes, resulting in variations in transmission line delay and ﬁlter group delay. In order to reduce the variation of channel delay, we can start from the following three aspects: one is to select the ﬁlter form that is less sensitive to the change of component parameters; the other is to control the variation range of component parameters and minimize the change of component parameters; The third is to design a closed-loop self-correction system that detects the change when the ﬁlter delay changes and compensates for this value during digital signal processing. The effect of delay fluctuations from 0 to 40 ns on the delay estimation variance is shown in the following ﬁgure. It can be seen that the in-band variation of 10 ns increases the pseudo-range measurement variance by 6%, the in-band variation by 40 ns, and the pseudo-range measurement variance by 16% (Fig. 4).

Fig. 4. Effect of RF group delay on ranging (pseudo-code speed: 10.23 Mcps)

4 Test Veriﬁcation The high-precision pseudo-code ranging terminal of a satellite was tested with the ground TTC station, aiming to verify this technology. The temperature control during the test is to adopt the method of active cooling to reduce the ambient temperature of the high-precision pseudo-code ranging terminal by 15 °C, and to monitor the temperature change by using on-board temperature telemetry (Table 1).

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Temperature (°C) 24.4

17.4

16.5

15.4

15.4

15.4

13

Test items Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration Original measured value On-board self-calibration Ground self-calibration

Ranging (m) 1923.497 1763.182 125.446 1923.477 1763.160 125.445 1923.467 1763.154 125.443 1923.468 1763.159 125.442 1923.465 1763.168 125.443 1923.467 1763.168 125.446 1923.460 1763.163 125.445

Correction value (m) 34.869

34.871

34.869

34.866

34.853

34.852

34.852

The average value of the range obtained by the high-precision pseudo-code ranging terminal and the ground measurement and control system equipment under various working conditions, the on-board self-calibration value, the ground self-calibration value, and the correction value curve are shown in the ﬁgures below (Fig. 5). In the above ﬁgure, by comparing the error between the correction value and the original measurement value, it can be seen that the on-board self-calibration and ground self-calibration techniques can effectively reduce the ranging fluctuation from 4 cm to less than 2 cm.

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Fig. 5. Temperature change conformance test

5 Conclusion By designing the on-board self-calibration technology in the traditional spread spectrum ranging system, the variation of the ranging value with temperature can be effectively reduced, and the improvement of the ranging accuracy is veriﬁed by the actual measured on-board data. This provides an effective guarantee for satellite high-precision ranging and orbit determination.

References 1. Cerri L, Berthias JP, Bertiger WI et al (2010) Precision orbit determination standards for the Jason series of altimeter missions. Mar Geodesy 33(S1):379–418 2. Bertiger W, Desai SD, Dorsey A et al (2010) Sub-centimeter precision orbit determination with GPS for ocean altimetry. Mar Geodesy 33(4):363–378 3. Ablain M, Philipps S, Picot N et al (2010) Jason-2 global statistical assessment and crosscalibration with Jason-1. Mar Geodesy 33(S1):162–185 4. Misra P, Enge P (2015) Global positioning system: signals, measurements, and performance (revised second edition). Ganga-Jamuna Press, Lincoln, MA 5. Urlichich Y, Subbotin V, Stupak G et al (2011) GLONASS: developing strategies for the future. GPS World 22(4):42 6. Fernández FA (2011) Inter-satellite ranging and inter-satellite communication links for enhancing GNSS satellite broadcast navigation data. Adv Space Res 47(5):786–801 7. Motella B, Savasta S, Margaria D et al (2011) Method for assessing the interference impact on GNSS receivers. IEEE Trans Aerosp Electron Syst 47(2):1416–1432

Weight-Assignment Last-Position Elimination-Based Learning Automata Haiwei An(&), Chong Di, and Shenghong Li School of Cyber Space Security, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240 Shanghai, China [email protected]

Abstract. Learning Automata (LA) is an adaptive decision-making unit under the reinforcement learning category. It can learn the randomness of the environment by interacting with it and adaptively adjust its behavior to maximize its long-term beneﬁts from the environment. This learning behavior reflects the strong optimization ability of the learning automaton. Therefor LA has been applied in many ﬁelds. However, the commonly used estimators in previous LA algorithms have problems such as cold start, and the initialization process can also affect the performance of the estimator. So, in this paper, we improve these two weaknesses by changing the maximum likelihood estimator to a conﬁdence interval estimator, using Bayesian initialization parameters and proposes a new update strategy. Our algorithm is named as weight-assignment last-position elimination-based learning automata (WLELA). Simulation experiments show that the algorithm has higher accuracy and has the fastest convergence speed than various classical algorithms. Keywords: Learning automaton Weight-assignment initialization Conﬁdence interval estimator

Bayesian

1 Introduction Learning automaton can be seen as an adaptive decision-making unit, It can constantly interact with the random environment to adjust its choices to maximize the probability of being rewarded. The process of the LA interacting with the environment is shown in Fig. 1; [1]. At each moment t, an action a(t) will be chosen by LA to interact with the random environment and receives the environment feeds back b(t), which can be either a reward or a penalty. Then, the automaton updates the state probability vector according to the received feedback. Because it’s simple algorithm, strong anti-noise ability and strong optimization ability, it has received extensive attention and has been applied in many ﬁelds, such as random function optimization, QoS Optimization and certiﬁcate authentication.

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 348–354, 2020 https://doi.org/10.1007/978-981-13-9409-6_41

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Fig. 1. LA interacts with the random environment

In the LA ﬁeld, the most classical discrete pursuit algorithm with deterministic estimator is the DPRI algorithm given by Oommen in [2]. The main idea of the DPRI algorithm is to increase the probability vector which has the maximum value in the running estimates when the environment rewards the current action and decreases others; otherwise, the automaton changes nothing. Furthermore, many classical pursuit algorithms, such as DGPA [3] and SERI [4], also use estimators and discretization to improve the convergence speed of automaton. In [5], an algorithm named last-position elimination-based LA( LELA) which is contrary to the classical pursuit algorithm is proposed. Instead of greedily increasing the probability vector of the optimal estimation action, this algorithm reduces the probability of choosing the current worst estimation action, t Experiments show that LELA can get faster convergence speed than DGPA. However, the estimator used by LELA has some innate defects. One typical flaw is the cold start and initialization problem, for example, since the maximum likelihood estimator does not have any information at the beginning, each action has to interact with the environment a certain number of times, it will Increase the cost of getting information in some complicated situations. And the update strategy of the LELA algorithm simply makes all active actions equally share the penalized state probability from the last-position action, does not consider the difference between optimal action and other actions at all. Thus, in this paper, we propose a weight assignment LELA (WLELA) algorithm which has made the following three changes: 1. Improvement of initialization parameters; 2. the estimator improvement; 3. changes in the probability vector update strategy. In Sect. 2, a brief introduction of the LELA algorithm is given. In Sect. 3 we will show our algorithm WLELA in detail. Then in Sect. 4 part, we will give the simulation results of the WLELA algorithm, which will be compared with LELA and other classic algorithms such as DPRI, DGPA. Finally, summarize in Sect. 5.

2 Related Work LA can be represented by a four-tuple hA, B, Q, Ti model. They are explained as follows.

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A is the set of actions. B is the feedbacks from random environment, when B = {0, 1}, where b = 0 represents that the LA has been penalized, and b = 1 means the LA has been rewarded. Q = , E represents the estimator, it contains all the historical information that each action interacts with the environment. The most commonly used estimator is the maximum likelihood estimator. PPis the state probability vector of choosing an action a(t) at any instant t, it satisﬁes pi(t) = 1. T is the state transition function of LA, which determines how LA migrates to the state of t + 1 according to the state of output, input, and t at time t. The random environment can also be described by a triple mathematical model. where A and B are deﬁned in the same way as above, and C is deﬁned as C = {cij = Pr{b(t) = bj|a(t) = ai}}. In the original LELA algorithm [5], it uses the maximum likelihood estimator to record the historical information of all actions, according to the following formula diðtÞ ¼

WiðtÞ ZiðtÞ

ð1Þ

where Zi(t) is the number of times the action ai was selected up to time instant t and Wi (t) is the sum of the environmental feedbacks received up to time t. when an action is rewarded, the automaton will select the worst performing action from the estimator vector set and decrease corresponding state probability vector by a step Δ = 1/rn, where r is the number of allowable actions and n is a resolution parameter. If some action’s state probability vector is reduced to zero during the process, this action will be removed from the optional set of actions, while the remaining actions will evenly share the state probability value from each decrease. The update scheme is described as follows: If b(t) = 1 then Find m 2 Nr such that dm ðtÞ ¼ minfdi ðtÞjpi ðtÞ 6¼ 0g; i 2 Nr pm ðt þ 1Þ ¼ maxfpm ðtÞ D; 0g If pm ðt þ 1Þ ¼ 0 Then kðtÞ ¼ kðtÞ 1 Endif pm ð t Þ pm ð t þ 1Þ pj ðt þ 1Þ ¼ min pj ðtÞ þ ; 1 ; 8j 2 Nr ; such thatpt ðtÞ [ 0: k ðtÞ Else pi ðt þ 1Þ ¼ pi ðtÞ8i 2 Nr Endif. j(t) denotes the number of active actions and is initialized by r. LELA has been proved to be e-optimal in every stationary random environment.

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3 Proposed Learning Automata In order to overcome the shortcomings of the LELA algorithms, we propose the following improvements. Firstly, we use the Bayesian estimator introduced in [6] to solve the cold start and initialization problems. However, if the Bayesian estimator is used directly in the LA algorithm, the convergence speed will be additionally affected, so in WLELA, we directly modify it to the mean of the posterior distribution which is to set all actions’ di(0) = 0.5, thereby improving convergence efﬁciency while ensuring overcoming cold start and initialization problems. Secondly, in order to get more information, in the WLELA algorithm we used the conﬁdence interval estimator proposed in [7] which is di ð t Þ ¼

1þ

Zi ðtÞ Wi ðtÞ ðWi ðtÞ þ 1ÞF2ðWi ðtÞ þ 1Þ;2ðZi ðtÞWi ðtÞ;0:005

1

; 8i 2 Nr

ð2Þ

where F2ðWi ðtÞ þ 1Þ; 2 ðZi ðtÞWi ðtÞ; 0:005 is the 0.005 right tail probability of the F distribution 2ðWi ðtÞ þ 1Þ and 2ðZi ðtÞ Wi ðtÞ dimensional degrees of freedom. Last, since all state probability vectors add up to a total of 1, so the value of each state probability vector can be thought of as its weights in the vector set. So, WLELA increase their probability vector according to their weights. In this way, similar to the idea of ﬁnding the optimal action in the classic pursuit algorithm, the probability vector of the optimal action will get more attention when updating, so that more values can be added to the optimal action’s state probability vector each time. Assigning the added value by weight is more in line with the purpose of learning the automatic machine to select the optimal action. A detailed description of the WLELA is as follows Algorithm WLELA Initialize pi ð0Þ ¼ 1r ; Wi ð0Þ ¼ 1; Zi ð0Þ ¼ 2; 8i 2 Nr 1 Initialize di ð0Þ ¼ 1 þ ðWi ð0Þ þ 1ÞFZi ð0ÞWi ð0Þ ; 8i 2 Nr 2ðWi ð0Þ þ 1Þ;2ðZi ð0ÞWi ð0Þ;0:005

Step 1: At time t, pick a(t) = ai according to the state probability vector P(t); Step 2: Receive feedback bi(t) {0,1}. Update the estimate values Wi ðtÞ ¼ Wi ðt 1Þ þ bi ðtÞ; Zi ðtÞ ¼ Zi ðt 1Þ þ 1 di ðtÞ ¼

Zi ðtÞ Wi ðtÞ 1þ ðWi ðtÞ þ 1ÞF2ðWi ðtÞ þ 1Þ;2ðZi ðtÞWi ðtÞ;0:005

Step 3: If bi(t) = 1 Then Find m 2 Nr such that

1

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dm ðtÞ ¼ minfdi ðtÞjpi ðtÞ 6¼ 0g; i 2 Nr pm ðt þ 1Þ ¼ maxfpm ðtÞ D; 0g If pm ðt þ 1Þ ¼ 0 Then kðtÞ ¼ k ðtÞ 1 Endif p j ð t þ 1Þ ¼

min

8j2Nr;pj ðtÞ [ 0

pj ðtÞ þ fpm ðtÞ pm ðt þ 1Þg fpj ðtÞ þ

pm ð t Þ pm ð t þ 1 Þ g; 1 k ðtÞ

Endif Step 4: If bi(t) = 0 Then pi ðt þ 1Þ ¼ pi ðtÞ8i 2 Nr Goto Step 1. Endif Step 5: If maxfPðtÞg ¼ 1; Then CONVERGE to the action whose p = maxfPðtÞg. ELSE Goto step 1. Endif END The parameter k(t) has the same meaning in algorithm LELA.

4 Simulation Results This section we compare the relative performances of the proposed WLELA with the LELA and the classical pursuit algorithms DPRI and DGPA by presenting their accuracy and convergence speed. The random environment we used is the most commonly used benchmark environment E1-E4 with 10 allowable actions as shown in Table 1. Table 1. Benchmark environments E1 E2 E3 E4

C1 0.60 0.55 0.70 0.10

C2 0.50 0.50 0.50 0.45

C3 0.45 0.45 0.30 0.84

C4 0.40 0.40 0.20 0.76

C5 0.35 0.35 0.40 0.20

C6 0.30 0.30 0.50 0.40

C7 0.25 0.25 0.40 0.60

C8 0.20 0.20 0.30 0.60

C9 0.15 0.15 0.50 0.50

C10 0.10 0.10 0.20 0.30

In the process of the LA simulation experiment, if the state probability vector of a certain action exceeds the set threshold T(0 < T 1), the algorithm is considered to have converged, if the converged action has the highest reward probability in the environment, it is considered that the learning automaton converged correctly.

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For all the algorithms in the experiment, they are simulated with their best parameters, which are deﬁned as the values that yielded the fastest convergence speed and guaranteed the automaton converged to the optimal action in a sequence of NE experiments. Speciﬁcally, in our experiment, we set the same threshold T and NE in [2, 3, 5], that is, T = 0.999 and NE = 750. After adjusting to the best parameters, we carried out 250,000 experiments to evaluate the average convergence rate and accuracy. Accuracy is an indicator for judging the performance of an automaton, accuracy is deﬁned as the probability that a learning automaton converges to the optimal action in an environment. As can be seen from Table 2, “Res” denotes the best resolution parameter, all algorithms can converge with high accuracy, while WLELA has higher accuracy than other algorithms, although the difference is not insigniﬁcant. Table 2. Accuracy (number of correct convergences/number of experiments) ENV E1 E2 E3 E4

WLELA Res n = 24 n = 98 n = 12 n = 31

Acc 0.997 0.997 0.998 0.998

LELA Res n = 20 n = 68 n = 10 n = 27

Acc 0.996 0.995 0.997 0.997

DGPA Res n = 65 n = 204 n = 28 n = 55

Acc 0.996 0.995 0.99 0.997

DPRI Res n = 653 n = 3221 n = 216 n = 881

Acc 0.994 0.993 0.996 0.994

A. Average converge times Convergence speed is one of the most critical performance indicators in learning automata. Convergence speed comparison data is shown in Table 2, “Ite” denotes the convergence speed. From the Table 3, we can see that the WLELA algorithm is better than other algorithms in terms of convergence speed. Compared with LELA, the rate of convergence improvement in each environment is {6.93, 19.76, 3.13, 12.49 %}. Compared with the traditional DGPA and DPRI algorithms, the rate of improvement is {27.91, 40.43, 18.01, 28.28%} and {51.45, 72.24, 21.65, 56.02%}. It can be seen that WLELA converges faster than the other three algorithms, and the E2 environment is the most complex compared to other environments, and WLELA still performs best.

Table 3. Convergence speed ENV E1 E2 E3 E4

WLELA Res n = 24 n = 98 n = 12 n = 31

Ite 1209 3090 619 1037

LELA Res n = 20 n = 68 n = 10 n = 27

Ite 1299 3851 639 1185

DGPA Res n = 65 n = 204 n = 28 n = 55

Ite 1677 5187 755 1446

DPRI Res n = 653 n = 3221 n = 216 n = 881

Ite 2490 11,132 790 2358

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5 Conclusion This paper proposes an improved algorithm WLELA. By using Bayesian initialization eliminates the cold start problem, using conﬁdence interval estimator gets more interactive information and using weight allocation strategy to realize the classical LA’s idea of pursuing the best behavior. These three improvements allow the WLELA algorithm to achieve high accuracy and fast convergence in the simulation experiments, and the results show that WLELA not only has the highest accuracy, but also the fastest convergence speed. Especially in the most complex environment, the WLELA still performs very well. In future work, consider using a random estimator instead of a deterministic estimator in WLELA. and the WLELA algorithm can be used in many applications that need to learn automata. Acknowledgements. This work was supported by the National Key Research and Development Project of China under Grant 2016YFB0801003.

References 1. Thathachar M, Sastry PS (2004) Networks of learning automata: techniques for online stochastic optimization. Kluwer, Dordrecht 2. Oommen BJ, Lanctôt JK (1990) Discretized pursuit learning automata. IEEE Trans Syst Man Cybern 20(4):931–938 3. Agache M, Oommen BJ (2002) Generalized pursuit learning schemes: new families of continuous and discretized learning automata. IEEE Trans Syst Man Cybern Part B Cybern 32 (6):738–749 4. Papadimitriou GI, Sklira M, Pomportsis AS (2004) A new class of e-optimal learning automata. IEEE Trans Syst Man Cybern Part B 34(1):246–254 5. Zhang J, Wang C, Zhou MC (2014) Last-position elimination-based learning automata. IEEE Trans Cybern 44(12):2484–2492 6. Xuan Z, Granmo OC, Oommen BJ (2013) On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata. Appl Intell 39 (4):782–792 7. Hao G, Jiang W, Li S et al (2015) A novel estimator based learning automata algorithm. Appl Intell 42(2):262–275

Nonlinear Multi-system Interactive Positioning Algorithms Xin-xin Ma1,2(&), Ping-ke Deng1,2, and Xiao-guang Zhang2 1

2

University of Chinese Academy of Sciences, 100049 Beijing, China [email protected] Department of Navigation System, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100094, China

Abstract. The Bayesian probabilistic observation model is established by using the interactive input of multi-system observation data. The positioning information between multi-system is directly interacted. The non-linear problem of the observation system is solved by the extended Kalman ﬁlter theory. Moreover, the system probability is updated in real time by using the ﬁltering innovation and variance of each system, and the estimated results are fused with each weight to output. The simulation results show that the proposed algorithm has better stability and adaptability than the traditional location algorithm under the same observation conditions. Keywords: Nonlinear algorithms

Extend kalman ﬁlter Multi-system Interactive

1 Introduction The idea of multi-system interactive positioning algorithms originates from Interacting Multiple Model (IMM) tracking algorithm [1]. BLOM H A P and Bar-shalom Y ﬁrst proposed the Bayesian interactive structure between motion models in Ref. [2]. In target tracking, multiple models are established to describe the motion state of objects, which reduces the limitation of single model to describe the motion of objects. Since then, the research direction of intelligent parallel fusion tracking technology has been initiated, and many improved algorithms for Bayesian multi-model interaction have been studied. References [3] and [4] propose an adaptive IMM algorithm for model set based on K-L (Kullback-Liber) theory and a multi-model set switching algorithm to solve the problem of incomplete description of motion by a single model set. In Ref. [5], an asymmetric interactive ﬁltering algorithm parallel to extended Kalman ﬁlter (EKF) is proposed, which effectively solves the problem of coexistence of nonlinear and linear systems. References [6] and [7] propose an IMM algorithm for adaptive adjustment of model transition probability, which effectively solves the constraints of ﬁxed transfer probability on target tracking and positioning performance. In Ref. [8], an IMM algorithm based on Unscented Kalman ﬁlter (UKF) is proposed to solve the problem of data loss. Reference [9] proposes an interactive multi-model algorithm with scalar weights, which can deal with complex environment. Besides the limitation of motion model, the performance of positioning system also plays a decisive role in © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 355–365, 2020 https://doi.org/10.1007/978-981-13-9409-6_42

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target tracking and positioning performance. References [10] and [11] proposed an interactive multi-sensor algorithm, which successfully introduced the interactive theory into the research of intelligent collaboration between systems, but it did not directly interact with real-time positioning information, resulting in the problems of lag and error accumulation in the collaboration of systems. Referring to the idea of interactive multi-model, document [12] proposes Interacting Multiple System Algorithm (IMS). In practice, many positioning systems are actually non-linear, and the applicability of this algorithm is limited. With the development of positioning system, multi position to ensure target in the changeable environment more widely, the scene, maintain the continuity and stability of tracking and positioning, then improved Interacting Multiple System Algorithm (IMS). The non-linear problem of the observation system is solved by the extended Kalman ﬁlter theory. Moreover, the system probability is updated in real time by using the ﬁltering innovation and variance of each system, and the estimated results are fused with each weight to output.

2 System Modeling Target tracking is carried out simultaneously by multiple systems. Considering the nonlinearity of target motion, the state equation and observation equation of target motion can be written in the following forms: xðk þ 1Þ ¼ f ½k; xðkÞ þ GðkÞxðkÞ

ð1Þ

Zi ðkÞ ¼ hðk; xðkÞÞ þ vðkÞ

ð2Þ

The x(k) means state vector in moment k, f ðÞ means state transition function, GðkÞ means noise driving matrix. xðkÞ and vðkÞ are process noise and observation noise respectively, and Gaussian white noise with zero mean of process noise and observation noise. And xðkÞ and vðkÞ are independent of each other, the covariances are QðkÞ, Ri ðkÞ respectively. The state equation and observation equation of the non-linear model are expanded around the ﬁlter value ^xðkÞ of the previous moment, and the local linearization of the non-linear system is carried out. The equation of state is: xðk þ 1Þ ¼ Uðk þ 1jkÞxðkÞ þ GðkÞxðkÞ þ uðkÞ

ð3Þ

_ @f ½x ðkÞ:k ¼ Uðk þ 1jkÞ ¼ _ _ @xðkÞ @xðkÞ _x ðkÞ¼xðkÞ

ð4Þ

@f _ xðkÞ uðkÞ ¼ f ½xðkÞ:k @xðkÞ xðkÞ¼_x ðkÞ

ð5Þ

Among: @f

_

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The observation equation is: ZðkÞ ¼ HðkÞxðkÞ þ yðkÞ þ mðkÞ

ð6Þ

@h HðkÞ ¼ _ _ @xðkÞ xðkÞ ¼ x ðkÞ

ð7Þ

@h _ yðkÞ ¼ hðx ðkjk 1Þ; kÞ _ x ðkjk 1Þ _ @xðkÞ xðkÞ ¼ x ðkÞ

ð8Þ

Among:

_

In the actual observation, multiple systems are observed at the same time. It is assumed that the transition probability between systems is subject to the Markov chain property. It is assumed that from system i to j at the time of k to k + 1, the system transition probability is: pij ¼ Pfmk þ 1 ¼ jjmk ¼ ig

ð9Þ

3 Nonlinear Multi-system Interactive Positioning Algorithm In principle, in data processing, centralized fusion may lead to a large amount of data passing through the network, while bayesian theory is used to preprocess data from multiple systems to reduce data trafﬁc. The basic idea of the bayesian multi-system interactive positioning algorithm is to set a basic set M, add the mixed observation information obtained by using the mixed probability to the system set M, put the mixed observation information in M into the corresponding ﬁlter, and the output result is the data fusion form of each mixed information. The algorithm is mainly composed of the following four core steps: interaction, ﬁltering, updating and fusion output. 3.1

Multiple System Interaction

The new initial values are obtained according to the markov transfer matrix between different systems. The mixed initial probability of mj ðkÞðj 2 S ¼ f1; 2. . .NgÞ of each corresponding system and the corresponding initial observation information and noise variance are calculated. Assuming that system j is effective at time k + 1, the mixing probability is calculated as follows: lijj ðkjk þ 1Þ ¼

pij li ðkÞ cj

ð10Þ

cj is normalization constant, li ðkÞ is the posterior probability of system i at time k, calculate the normalization constant as:

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cj ¼

n X i¼1

pij li ðkÞ

ð11Þ

Zi ðk þ 1Þlijj ðkjk þ 1Þ

ð12Þ

Mixed observation information: _0

Z j ðk þ 1jk þ 1Þ ¼

n X i¼1

Mixed variance of noise: R0j ðk þ 1jk þ 1Þ ¼

n lijj ðkjk þ 1ÞfRi ðk þ 1Þ þ ½Zi ðk þ 1Þ X i¼1

_0

_0

Z j ðk þ 1jk þ 1Þ ½Zi ðk þ 1Þ Z j ðk þ 1jk þ 1ÞT g

ð13Þ

Using the above interactive information as the input value of ﬁltering, the second step ﬁltering is carried out. 3.2

Multiple System Parallel Filtering

Similar to the multi-model tracking algorithm, the mixed observed values and mixed variances obtained in the ﬁrst step are ﬁltered and calculated as the input of the ﬁlter. In order to improve the performance of the localization, the extended kalman ﬁlter is used to solve the nonlinear problem. After linearization of the Eqs. (3) and (6) ﬁltering recursive: _

_

x ðk þ 1jkÞ ¼ f ðxðkjkÞÞ

ð14Þ

pðk þ 1jkÞ ¼ Uðk þ 1jkÞpðkjkÞUT ðk þ 1jkÞ þ Qðk þ 1Þ

ð15Þ

Gain factor: Kðk þ 1Þ ¼ pðk þ 1jkÞH T ðk þ 1Þ½Hðk þ 1Þpðk þ 1jkÞH T ðk þ 1Þ þ R0j ðk þ 1Þ1 ð16Þ The variance is: Sj ðk þ 1Þ ¼ Hðk þ 1Þpðk þ 1jkÞH T ðk þ 1Þ þ R0j ðk þ 1Þ _

_0

_

_

ð17Þ

xðk þ 1jk þ 1Þ ¼ xðk þ 1jkÞ þ Kðk þ 1Þ½Z j ðk þ 1Þ hðx ðk þ 1jkÞ

ð18Þ

pðk þ 1Þ ¼ ½I Kðk þ 1ÞHðk þ 1Þpðk þ 1jkÞ

ð19Þ

The innovation is: _0

_

vj ðk þ 1Þ ¼ Z j ðk þ 1Þ hðx ðk þ 1jkÞ

ð20Þ

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3.3

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System Probability Update

System probability updating is a crucial step in the algorithm. And the updating use the posterior probability of system j in time k + 1. the likelihood function of each system and the weight of the system are calculated. The form mk þ 1 ¼ j means in time k + 1, system j is effective. It’s probability is expressed as qj ðk þ 1Þ. Zj ðk þ 1Þgnj¼1 denotes the set of observation vectors of multi-system at time k + 1. lj ðk þ 1Þ ¼ Pfqj ðk þ 1ÞjX k þ 1 g ¼ Pfqj ðk þ 1ÞjX k ; fZj ðk þ 1Þgnj¼1 g 1 ¼ PfZj ðk þ 1Þjqj ðk þ 1Þ; X k g cj c

ð21Þ

The likelihood function is: Kj ðk þ 1Þ ¼ PfZj ðk þ 1Þjqj ðk þ 1Þ; X k g

ð22Þ

¼ N½vj ðk þ 1Þ : 0; Sj ðk þ 1Þ The normalization constant is: c¼

n X

Ki ðk þ 1Þcj

ð23Þ

j¼1

3.4

System Fusion Output

At the output end, we use Bayesian theory to fuse the results according to the weight of each system. Get a combined estimation and estimation error covariance of each system: _

x ðk þ 1jk þ 1Þ ¼

n X _ x j ðk þ 1jk þ 1Þlj ðk þ 1Þ

ð24Þ

j¼1

pðk þ 1jk þ 1Þ ¼

n X j¼1 _

_

lj ðk þ 1Þfpj ðk þ 1jk þ 1Þ þ ½x j ðk þ 1jk þ 1Þ _

_

ð25Þ T

xðk þ 1jk þ 1Þ. . .. . . ½x j ðk þ 1jk þ 1Þ x ðk þ 1jk þ 1Þ g

4 Analysis of Simulation Experiment In most practical positioning applications, the observation of moving objects is based on the measurement of distance and azimuth. When sensors locate objects, such as GNSS satellite positioning, radar positioning, etc. Target tracking based on distance

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information is widely used, but the observation equation of target positioning is a nonlinear problem. In order to solve this problem, and effectively improve the robustness of multi-system positioning and other positioning performance, the traditional interacting multi-system positioning algorithm is improved. In this paper, a non-linear multi-system interactive positioning algorithm is proposed, simply called NL-IMS. At present, simulation experiments are carried out to verify the effectiveness of the algorithm. In the simulation, multi-system based on distance information is used to carry out experiments. For illustration, taking plane positioning as an example, the observation equation is established according to distance observation. ZðkÞ ¼

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ðXðkÞ X0 Þ2 þ ðYðkÞ Y0 Þ2 þ VðkÞ

ð26Þ

In the simulation, the initial position of the target is set to (0 m, 0 m), the initial speed is set to (2 m/s, 10 m/s), and the positioning period is set to 0.2 s, and set 90 s. The coordinates of the reference station are set to (200 m, 200 m) and three systems are set to locate and track the target. The observation errors of the three systems are set as follows: Sampling time (s) 1–40 41–80 80–120

System1 (m) 3 10 10

System2 (m) 10 3 10

System3 (m) 10 10 3

The covariance matrix of observed noise is:

diagð½9 m2 ; 9 m2 Þ; diagð½100 m2 ; 100 m2 Þ;

k ¼ 1 30 s k ¼ 31 90 s

8 < diagð½100 m2 ; 100 m2 Þ; R2 ðkÞ ¼ diagð½9 m2 ; 9 m2 Þ; : diagð½100 m2 ; 100 m2 Þ;

k ¼ 1 30 s k ¼ 31 60 s k ¼ 61 90 s

R1 ðkÞ ¼

R3 ðkÞ ¼

diagð½100 m2 ; 100 m2 Þ; diagð½9 m2 ; 9 m2 Þ;

k ¼ 1 60 s k ¼ 61 90 s

The system probability at the initial time of the three systems is set to qi ¼ 1=3. The transformation between the three systems obeys the Markov chain, and the Markov transition probability matrix is set to: 2

0:98 pij ¼ 4 0:01 0:01

0:01 0:98 0:01

3 0:01 0:01 5 0:98

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The process noise driving matrix in the simulation is set as follows: G¼

1

2T

2

0

T 0

0 1 2 T 2

0 T

T

State transition matrix: F ¼ diag

1 0

T 1

The variance matrix of process noise is set as Q ¼ 1e 4 diagð½0:5; 1Þ. Monte Carlo simulation is carried out. The tracking and positioning performance results of the proposed algorithm are displayed and analyzed, and compared with the IMS algorithm proposed in [12]. The performance advantages of the proposed algorithm are summarized. Figures 1 and 3 are the results of RMSE of location and velocity, Figs. 2 and 4 are the results of RMSE probability statistics based on location and velocity, Fig. 5 is the distribution of system probability of three systems in the whole experiment. Figure 6 is the results of comparison between real trajectory and estimated trajectory.

Fig. 1. Position deviation

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Fig. 2. Position deviation probability statistics

Fig. 3. Position deviation

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Fig. 4. Position deviation probability statistic

Fig. 5. System probability distribution

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Fig. 6. Target tracking line

5 Conclusion Analysis It can be seen from Figs. 1 and 3 that in the nonlinear case, the algorithm proposed in this paper has higher accuracy and stability than ims algorithm. The probability statistics in Figs. 2 and 4 supplement this situation. The system probability of the three systems in the whole fusion process is illustrated in Fig. 5. In the simulation, the systematic errors of the three systems in different time are tested. It can be clearly seen that when the system error is small, the weight of the system is larger, and the system alternates timely, indicating the effectiveness of the multi-system fusion positioning algorithm. In Fig. 6, the estimated trajectory of the multi-system fusion output is basically consistent with the real trajectory, with small difference and relatively stable. Experimental analysis to get the following conclusion: (1) The NL-IMS algorithm proposed in this paper has good accuracy and stability in multi-system fusion localization. (2) Compared with the traditional IMS algorithm, this algorithm is more suitable for the nonlinear situation in the real location and has practical feasibility. (3) This algorithm can adjust the system probability timely according to the system performance and improve the positioning performance of multi-system positioning. (4) The Markov transition probability of this algorithm is determined by artiﬁcial prior, and its self-adaptation has a certain hysteresis, so there is still a certain delay when the system is switched, and the self-adaptability of the transition probability of the system needs to be improved.

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References 1. Hui L (2006) The status quo and trend of target tracking based on interactive multiple model. Fire Control Command Control 31(11):865–868 2. Blom HAP, Bar-shalom Y (1988) The interacting multiple model algorithm for systems with Markovian switching coefﬁcients. IEEE Trans Auto Control 33(8):780–783 3. Can Sun, Jianping X, Haozhe L et al (2012) S-IMM: switched IMM algorithm for maneuvering target tracking. J Convergence Inf Technol 14(7):461–468 4. Liang C, Junwei Y, Xiaodi S (2013) Model-set adaptive algorithm of variable structure multiple-model based on K-L criterion. Syst Eng Electron 35(12):2459–2466 5. Guixi L, Enke G, Chunyu F (2007) Tracking algorithms based on improved interacting multiple model particle ﬁlter. J Electron Inf Technol 29(12):2810–2813 6. Xiaobing L, Hongqiang W, Xiang L (2005) Interacting multiple model algorithm with adaptive markov transition probabilities. J Electron Inf Technol 27(10):1539–1541 7. Weidong Z, Jianan C, Long S (2014) Interacting multiple model with optimal mode transition matrix. J Harbin Inst Technol 46(11):101–106 8. Zhigang L, Jinkuan W (2012) Interacting multiple sensor ﬁlter for sensor networks. Acta Electron Sin 40(4):724–728 9. Zhigang L, Jinkuan W, Yanbo X (2012) Interacting multiple sensor ﬁlter. Signal Process 92:2180–2186 10. Liu M, Tang X, Zheng S et al (2013) Filtering of nonlinear systems with measurement loss by RUKF-IMM. J Huazhong Univ Sci Technol (Nat Sci Edition) 41(5):57–63 11. Weidong Z, Mengmeng L, Yongjiang Y (2014) An improved interacting multiple model algorithm based on multi-sensor information fusion theory. J South China Univ Tech (Nat Sci Edition) 42(9):82–89 12. Xiaoguang Z (2016) Interacting multiple system tracking algorithm. J Electron Inf Technol 38(2):389–393

Bandwidth Enhancement of Waveguide Slot Antenna Array for Satellite Communication Pengfei Zhao(&), Shujie Ma, Peiyao Yang, Fan Lu, and Shasha Zhang Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. Owing to many advantages such as low losses in the feeder, high power handling capability, and high efﬁciency, waveguide slot antenna array has been widely used. However, the bandwidth of this kind of antenna is very limited. In this paper, 3 dB couplers are inserted to enhance the bandwidth of the waveguide slot antenna. To verify the validity of the bandwidth enhancement technique, A waveguide slot antenna array working at Ka band is designed, fabricated, and measured. Good agreement is found between the simulated and measured results, and the results show that the bandwidth is enhanced to 2.3 GHz (6.7%), which makes it suitable for satellite communication systems. Keywords: Waveguide slot antenna communication

Bandwidth enhancement Satellite

1 Introduction With the development of wireless communication technologies, broadband, high-gain and high efﬁciency antennas are in demand in satellite communication systems. Waveguide slot antenna array is a good candidate for its advantages such as low losses in the feeder, high power handling capability, and high efﬁciency [1, 2]. However, this kind of antenna suffers from narrow working bandwidth. For an array of four slots, the bandwidth is about 2% [3]. In [4] and [5], by bringing in cavity portion between the radiating slots and the coupling aperture, an 11% bandwidth is achieved. However, the complex multi-layer structure is depending on the new processing technic called diffusion bonding, which will increase the cost. Some substrate integrated waveguide [6] slot antennas were proposed; a 5% bandwidth is achieved. By using center-feeding technology [7], a 9.8% bandwidth is achieved. But the loss on the substrate is unacceptable, which leads to about 50% decrease of radiation efﬁciency. In this paper, a waveguide slot antenna array with inserted 3 dB couplers is designed and fabricated by soldering and brazing technology. The proposed antenna contains feeding network, 180° waveguide bend, 14-element sub-array, and 3 dB couplers between the waveguide bend and the feeding network. As we know, the real part of characteristic impedance of the waveguide slot antenna depends on the ratio of the modulus value of the transverse component of the electric ﬁeld Et, to the magnetic ﬁeld Ht. Sub-array with the 3 dB couplers can match the impedance of the feeding network in a wider band, thus the working bandwidth is improved from 2 to 6.7%. We use High Frequency Structure Simulator (HFSS) to model the antenna, and good agreement is found between the simulated and measured results. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 366–372, 2020 https://doi.org/10.1007/978-981-13-9409-6_43

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2 Antenna Design and Fabrication Figure 1 shows the perspective top view of the proposed waveguide slot antenna array. It is made up of two layers. Feeding network and 3 dB couplers are on the bottom layer, 14-element sub-arrays are on the top layer, and 180° waveguide bend connect the two layers.

Fig. 1. Perspective top view of the proposed antenna

The feeding network is made up of ﬁve H-plane T junctions to realize central symmetric and speciﬁc weights of each excitation, which is calculated based on the Taylor algorithm. The real part and imaginary part of the designed feeding network are 50 Ω and 0j Ω, respectively. The 14-element sub-arrays are made up of radiating waveguide, radiating slots, and short end. The slots are of same dimensions, offset and spaces between slots. For the sub-array end with a short terminal, the wave in it is traveling-standing. And the characteristic impedance is affected by parameters of the slots. By adjusting the slots, the imaginary part of characteristic impedance can be close to zero in a wide band. However, the real part of characteristic impedance is instable in a wide band. As shown in Fig. 2, from 33.3 to 35.6 GHz, the real part varies from 17 to 110 Ω. This is because the slots cut off the current on the waveguide wall, and electromagnetic ﬁelds are disturbed, so the ratio of Et to Ht has been changed. However, the characteristic impedance of the connected feed network is purely real of 50 Ω, and can’t match the sub-array in a wide bandwidth. We can note that with the 3 dB coupler, in a wider band the real and imaginary part are relatively stable and close to 50 Ω and 0j Ω, respectively. Thus, the matched bandwidth is broadened. The 3 dB coupler’s

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structure is shown in Fig. 3. Port1 connects the feeding network; port2 and port3 connect two sub-arrays. Detailed values of the parameters are listed in Table 1. Also, the output phase at port2 differs 90° from port3, so we add the 180° waveguide bend to eliminate the phase difference.

Fig. 2. Characteristic impedance varies with frequency

Fig. 3. Schematic diagram of 3 dB coupler

Table 1. Dimensions of the 3 dB coupler (mm) w1 w2 w3 l1 l2 l3 Sw Sd 2 4.7 6.6 0.7 1.3 1.9 1 2

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In order to suppress the reflection, the bandwidth of the feeding network must be extended compared with the sub-array. Simulated results of each component are shown in Fig. 4.

Fig. 4. VSWR of each component

3 Results and Discussion Using soldering and brazing technology, the proposed waveguide slot antenna array is fabricated with aluminum to achieve light weight and high metal conductivity, as shown in Fig. 5. The dimension of it is 34 mm 100 mm 10 mm (exclusive the mechanical ﬁxture for testing). In Fig. 6, the simulated and measured results are compared. It can be seen from Fig. 6 that the measured VSWR is below 2 from 33.3 to 35.6 GHz, which is 6.7% of relative bandwidth. Figure 7 presents the simulated and measured radiation patterns in both E-plane and H-plane. The side-lobe level is lower than −23 dB in the E-plane. The beam widths are 6° and 18.5° in E-plane and H-plane, respectively. Measured results show good agreement with the simulated ones. Figure 8 shows the frequency behavior of the calculated directivity, measured gain, and efﬁciency characteristics. The gain of the antenna is measured in an anechoic chamber, and the conductor loss and the reflection loss are included. The measured results of gain show that the efﬁciency is upon 80% in the working band including the losses.

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Fig. 5 Photograph of the proposed antenna

Fig. 6. Measured and simulated VSWR

Fig. 7. Measured and simulated normalized gain

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Fig. 8. Frequency behaviour of the calculated directivity measured gain, and efﬁciency characteristics

4 Conclusion A 3 dB couplers inserted waveguide slot antenna with more than 24 dBi gain and more than 80% antenna efﬁciency in the Ka band is designed, fabricated, and measured. The matched bandwidth of the radiating sub-array is enhanced because the inserted couplers adjust the real part of the characteristic impedance. The feeding network is designed to suppress the reflection over a wide bandwidth. The measured VSWR is below 2 from 33.3 GHz to 35.6 GHz (bandwidth: 6.7%). The aperture ﬁeld distribution follows the Taylor algorithm to achieve a −23 dB side-lobe in the E-plane. The proposed antenna is suitable for satellite communication systems. Next we plan to develop its sum and difference patterns functions, so that it can be used to transmit, receive information as well as track the source.

References 1. Stevenson AF (1948) Theory of slot in rectangular waveguide. J Appl Phys 19:24–28 2. Kimura Y, Miura Y, Shirosaki T, Taniguchi T, Kazama Y, Hirokawa J, Ando M, Shirozu T (2005) A low-cost and very compact wireless terminal integrated on the back of a waveguide planar array for 26 GHz band ﬁxed wireless access (FWA) systems. IEEE Trans Antenna Propag 53(8):2456–2463 3. Mazen H (1989) Frequency limitations on broad-band performance of shunt slot arrays. IEEE Trans Antenna and Propag 37(7):817–823 4. Huang GL, Zhou SG, Chio TH, Yeo TS (2014) Broadband and high gain waveguide-fed slot antenna array in the Ku-band. IET Microwaves Antenna Propag 8(13):1041–1046 5. Miura Y, Hirokawa J, Ando M, Shibuya Y, Yoshida G (2011) Double-layer full-corporatefeed hollow-waveguide slot array antenna in the 60-GHz band. IEEE Trans Antenna Propag 59(8):2844–2851

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6. Xu JF, Hong W, Chen P, Wu K (2009) Design and implementation of low sidelobe substrate integrated waveguide longitudinal slot array antennas. IET Microwaves Antennas Propag 3 (5):790–797 7. Chen M, Che WQ (2011) Bandwidth enhancement of substrate integrated waveguide (SIW) slot antenna with center-fed techniques. In: 2011 international workshop on antenna technology (iWAT), Hong Kong, China, pp 348–351

Design of an Enhanced Turbulence Detection Process Considering Aircraft Response Yuandan Fan(&), Xiaoguang Lu, Hai Li(&), and Renbiao Wu Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, China {ydfan_16,elisha1976}@163.com

Abstract. Turbulence is very hazardous to the flight safety, which generally can be detected by airborne weather radar. In newly speciﬁcation DO-220A revised by Radio Technical Commission for Aeronautics (RTCA), standards of enhanced turbulence detection with airborne weather radar have been complemented. In the speciﬁcation, it is stated that the characteristics of aircrafts should be taken into account in the turbulence detecting process. The aircraft response following a turbulence encounter is analysed in this paper, and then the characteristics of aircrafts are quantiﬁed by employing the load factor. Based on the quantiﬁed analysis, the vertical load factor is predicted based on both radar observation and the characteristics of aircraft. It can provide more accurate turbulence metrics for crews involved with different aircraft types. The simulation results demonstrate that the vertical load factor based turbulence detection process meets requirements of DO-220A. Furthermore, the research is important for the study of enhanced turbulence detection speciﬁcations documented in DO-220A. Keywords: Airborne weather radar metrics Vertical load factor

Turbulence detection Turbulence

1 Introduction Atmospheric turbulence is a hazardous weather to the flight safety, and generally is caused by the rapid and irregular motion of air. The turbulence encounters can lead to aircraft bumps and stresses acting on structural elements. Severe turbulence would even cause injury to passengers and damage to aircraft structure [1]. On April 19, 2018, an Air India flight from Amritsar to Delhi ran into such severe turbulence that three passengers suffered injuries, the inside part of a window panel came off and some overhead oxygen masks got deployed [2]. In order to warn pilots in advance of the potential turbulence hazards on the flight path, the airborne weather radar is equipped on the commercial aircrafts. In the early days, for non-coherent airborne weather radars, the turbulence is indicated by the amplitudes of the radar echoes [3], which is not reliable. After the appearance of the full coherent Doppler weather radar, with the Doppler effect, the mean speed of weather target and its speed diversion can be obtained by measuring the phase change of the

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radar echo. And then turbulence can be detected by estimating the spectrum width of the radar echoes [4], which has been applied in actual systems. Currently, the turbulence detection is processed according to the spectrum width of the radar echoes in the certiﬁed airborne weather radar. It is generally considered that weather objects with the velocity spectrum width of 5 m/s or greater would be considered turbulent in aviation [5]. In March 2016, the Radio Technical Commission for Aeronautics (RTCA) revised the minimum operational performance standards for airborne weather radar systems, w.r.t. DO-220A. DO-220A incorporates corrections to the previous version and technological advances in the ﬁeld of airborne weather radar. In addition to modernizing the requirements and test procedures for the weather, ground mapping, and predictive wind shear functions set out in its predecessors, speciﬁcations were added for radar detection of turbulence and atmospheric threat awareness [6]. Speciﬁcations require that both the spectrum width and characteristics of aircraft should be considered for turbulence detection. And three aircraft classes based on wing loadings (aircraft weight divided by wing area) is established in DO-220A. It means that the different types of aircrafts would react much differently when they encounter turbulence, due to the differences in aircraft performance. Characteristics of the aircraft are necessary to be considered when detecting the turbulence using radar. As for the previous detection algorithms, the spectrum width is regarded as the only indication (it is generally considered that if the spectrum width of the echo is larger than 5 m/s, there is a turbulence). Actually, the turbulence with a spectrum width of 5 m/s may not impact a large aircraft, because of the well maneuverability of the aircraft. Thus, the traditional turbulence metric to indicate its absence and warn pilots may cause unnecessary re-route and reduce flight efﬁciency. However, the threshold deﬁned would be too high for a small aircraft, resulting in a missed alarm and accompanied by an irreversible damage to aircraft. Furthermore, researches have suggested that the occurrence rate of mid-high intensity turbulence in winters on NAT will rise to its 40–170% by 2050, compared with that of before the industrialization [7]. Therefore, the more accurate detection of turbulence is important for improving flight safety and flight efﬁciency. In this paper, the turbulence detection is researched considering the impacts of the characteristics of aircraft. The aircraft’s response to turbulence encounters has been analysed, and the factor of the characteristics of an aircraft are quantiﬁed employing theories on load factor. Based on the quantiﬁed analysis, the vertical load factor is calculated with radar observations and the characteristics of aircraft, which provides more accurate scale of turbulence hazards for crews. Finally, an enhanced turbulence detection process is designed. This is a more reliable turbulence detection method, which is helpful for pilots to make a more efﬁcient flight route with better safety and shorter diversions. The designed turbulence detection process is based on the requirements of DO-220A, and the research is of great signiﬁcance for the study of enhanced turbulence detection speciﬁed in DO-220A.

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2 The Estimation of the Vertical Load Factor During the flight, the steady airflow provides a smooth and constant lift forces for the aircraft. And so the aircraft could fly smoothly. However, encountered with a turbulence, the disturbed updraft will change the lift forces and hence a dynamical response of the aircraft. Then aircraft loads will also be produced, which are proportional to point variance of the turbulence velocity ﬁeld. The stronger the turbulence is, the greater the load factor will be. When the maximum load is over, it would have an effect of the safety of the aircraft. For pilots and passengers, the preceding turbulence detection will improve the flight safety. In reference [8], a methodology of turbulence detection was given. Both the spectrum width of the radar echoes of the turbulence and the characteristics of the aircraft should be considered for turbulence detection. And the aircraft loads, denoted by rDn , is to quantiﬁes a turbulence hazard to an aircraft. In detail, the structure of a generic hazard prediction algorithm based on airborne radar observables can be approximated by [8]: ^Dn ¼ r

rDn ½M 2 ð~ xÞ0:5 pﬃﬃﬃﬃﬃﬃﬃ ﬃ r2v ðrÞ unitrw

ð1Þ

r

where rDn =unitrw (g/m/s) means aircraft scale (conversion) factor and rw is deﬁned as the standard deviation of the vertical component of the turbulent wind ﬁeld. M 2 ð~ xÞ (m2/ p ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ s2) is a quantity related to the spectrum width, and r2v ðrÞ=r is the theoretical compensation factor of the radar pulse volume. For clarity of notation, Eq. (1) can be simpliﬁed as: z¼xy

ð2Þ

where z is deﬁned as the estimated vertical load factor, which is an airborne radar observable associated with the characteristics of the aircraft. The estimate can be used as a turbulence hazard metric for radar detection. y is the spectrum width of the radar echo. The spectrum width can be estimated employing many methods [9]. x is scaling factor of the aircraft in general, which depends on the characteristics of the aircraft (altitude, airspeed, and wing loadings). The speciﬁc estimation process of x is not be provided by literature [8] and the DO-220A, and the calculation details of x will be further discussed. The following mainly focus on the scaling factor of the aircraft. x can be determined by analyzing the aircraft’s response to turbulence, then Eq. (2) can be used to calculate z, and ﬁnally an enhanced turbulence detection process based on the vertical load factor can be given.

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3 Enhanced Turbulence Detection Process Based on Vertical Load Factor In order to compute the aircraft scaling factor, the aircraft’s response to the turbulence should be analysed in detail. Firstly, it is necessary to construct a turbulence model which is considered as an input for the aircraft system. Secondly, a simpliﬁed aircraft model is also needed to be provided. Accordingly, the aircraft’s response to the turbulence can be quantiﬁed in virtue of the aerodynamics concepts, theories of flight mechanics model and relevant theory [10, 11]. And then the aircraft scaling factor which quantiﬁes the impact of aircraft characteristics would be resolved according to the load factor and relevant theory [10]. According to Eq. (2), the calculation of the vertical load factor can be performed in further general detail. 3.1

Turbulence Model and Its Power Spectral Density Function

The aircraft’s response to turbulence is very complicated. For computation simplicity, it is very necessary to simplify the turbulence model. For a flying aircraft, turbulence can be regarded as gusts with obvious changes in airflow direction and intensity [12]. It is assumed that the turbulence is isotropic [13]. For simplicity, there is only the wings’ response to the symmetric vertical component of gusts hereon. Furthermore, although turbulence is a complex atmospheric phenomena, the real turbulence is highly unlikely to be either discrete gust or continuous Gaussian turbulence ﬁeld. For lowing the research difﬁculty, it is possible to assure robustness of the aircraft structure to gusts and turbulence by covering these extremes [10]. The following only focuses on the analysis of the impact of continuous gusts on flight. It is provided that the continuous gusts can be represented by the random variation of the wind velocity along the flight path of the aircraft. And the random variable has a Gaussian distribution with zero mean, and its power spectral density (PSD) is represented by the Von Karman turbulence PSD function, Ugg ðxÞ, with units of (m/s)2/ (rad/m) [10]. L 1 þ ð8=3Þð1:339Lx=V Þ2 Ugg ðxÞ ¼ r2g h i11=6 p 1 þ ð1:339Lx=V Þ2

ð3Þ

where rg (m/s) denotes the turbulence intensity, which is also the root mean square turbulence velocity, L (m) is the turbulence scale. 3.2

Predicting Vertical Load Factor

Similarly, it is necessary to simplify the aircraft system model to simplify the aircraft’s response to turbulence. The following assumptions are made: the aircraft is a rigid aircraft with a weight of m, and its wings are not swept. The aircraft is in a trimmed level flight condition (with lift = weight) prior to encountering the turbulence [10, 11]. When an aircraft encounters turbulence, the gust velocity is constant across the aircraft span, and the aircraft will heave (move up or down) without pitching.

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It also assumes that the quasi-steady aerodynamic representation will be employed, which means that the lifting surface enters the gust instantaneously, and the effective incidence angles and lift force are developed instantaneously. The above incremental lift forces are due to both the response and gust velocity. Thus, the heave equation of motion of the aircraft can be established by using Newton’s second law and performed in the frequency domain [10]. As a result, the transfer function relating the (downwards) heave acceleration response to the (upwards) gust velocity at frequency x is given by [10] Hzg ðxÞ ¼ x2

12 qVSW a ~zc ¼ x2 wg0 x2 m þ ix 12 qVSW a

ð4Þ

where ~zc is the displacement due to aircraft’s heave response, wg0 and q are amplitude of gust velocity and air density respectively, V and SW are the flight speed and the wing area, and a is the lift curve slope for the whole aircraft. The center of mass acceleration response PSD is then obtained by connecting the transfer function of the system and the gust velocity PSD 2 Urr ðxÞ ¼ Hrg ðxÞ Ugg ðxÞ

ð5Þ

The RMS normal load per RMS vertical gust intensity, which is the aircraft scaling factor to be determined, is given by ﬃ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ R xmax Urr ðxÞdx rr 0 ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ x¼ R xmax rg U ðxÞdx 0

ð6Þ

gg

Based on the content mentioned above, the vertical load factor can be predicted from Eq. (2), assuming the spectrum width is known. Consequently, calculation process of z based on response is shown in Fig. 1. Considering the different levels of turbulence intensity are deﬁned by the different values of vertical load factor, the categorization adopted within this paper can be found in reference [8]. At this time, the severity of turbulence can be quantiﬁed, according to the categorization.

Fig. 1. Calculation flowchart of vertical load factor

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4 Numerical Examples 4.1

Analysis of Examples

PSD

Taking an aircraft for example, a numerical experiment is implemented to calculate aircraft scaling factor and predict vertical load factor in a speciﬁc flight condition for estimating a speciﬁc turbulence hazard for the aircraft. Consider an aircraft with the following performance parameters: V ¼ 180 m/s, H ¼ 4500 m, SW ¼ 30 m2 , m ¼ 10000 kg, a ¼ 4:5=rad, wing loading is 332.0 kg/m2, rg ¼ 1 m/s, L ¼ 762 m. Assuming the known spectrum width is 5 m/s and the rigid aircraft uses the heave only model with quasi-steady aerodynamics, ﬁnd the aircraft scaling factor and the prediction of vertical load factor due to the turbulence. First, in term of Eq. (3), the Von Karman turbulence PSD is plotted in Fig. 2.

10

1

10

0

10

-1

10

-2

10

-3

10

-4

10

-5

10

|Transfer function|2 Acceleration response PSD Von Karman turbulence

-2

10

-1

10

0

10

1

Frequency(Hz)

Fig. 2. Von Karman turbulence, |Transfer function|2 and the acceleration response PSD

The modulus squared value of the transfer function and the acceleration response PSD are shown in Fig. 2, based on the calculation flowchart of vertical load factor presented in Fig. 1. It can be concluded that the transfer function is an aircraft system property and the transfer function also dictates how the aircraft behaves effect by gusts at any frequency. According to Eq. (6), the aircraft scaling factor can be calculated as 0.0654 g/m/s. Vertical load factor may be calculated by inspection of Eq. (2) as z ¼ 0:0654 5 ¼ 0:327 g. Therefore, comparing this value with the turbulence intensity table shown in Ref. [8], the turbulence with a spectrum width of 5 m/s is a severe turbulence for this aircraft.

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Application Analysis

Three aircraft classes based on wing loading is established in DO-220A. Class A, B, and C are deﬁned as aircraft with wing loading equal to 390.6–659.1 kg/m2, 292.9– 488.2 kg/m2, and 146.5–341.8 kg/m2, respectively. In order to verify that the aircraft scaling factor is related to the quantization of the impacts of the characteristics of aircraft, the response of different types of aircraft to the same turbulence is compared in this section. Assume that flight conditions are the same as above and the spectrum width is 5 m/s, in order to quantify the impact of turbulence on different aircrafts, the typical types in these classes are selected for simulation. The results are shown in Table 1.

Table 1. The predicted value of vertical load factor for several types of aircraft Aircraft class Class A

Class B Class C

Types B777300ER B747400 B777300 B737700 A380 A330200 A320200 C919 ERJ190 ARJ21 ERJ145 ERJ135 MA-60

Wing loading (kg/m2) 821.7

x (g/m/s)

z (g)

0.0365

0.1825

776.7

0.0380

0.1900

699.8

0.0409

0.2045

664.9

0.0425

0.2125

662.7 636.1

0.0426 0.0438

0.2130 0.2190

628.2

0.0442

0.2210

561.4 539.6 507.1 410.1 371.2 290.7

0.0478 0.0492 0.0513 0.0593 0.0634 0.0745

0.2390 0.2460 0.2565 0.2965 0.3170 0.3725

Levels of turbulence intensity Moderate turbulence

Moderate to severe turbulence

Severe turbulence

As seen from Table 1, when aircrafts encounter the same turbulence, as for the aircraft with the smaller wing loading, the turbulence has a greater impact on the aircraft. It can be concluded that these aircrafts with different wing loading react differently to turbulence with the same spectrum width under the same flight conditions, and vertical load factor is a more accurate turbulence hazard metric. It also indicates that the aircraft scaling factor is a constant for the same aircraft with the same flight condition and is only related to the inherent characteristics of the aircraft under a speciﬁc flight condition. It is also the response following a unit vertical gust velocity.

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5 Conclusion The aircraft response induced by a turbulence encounter was analysed in this paper, and then the characteristics of aircrafts were quantiﬁed. Based on the quantiﬁed scaling factor and radar observation, the vertical load factor which is a more accurate turbulence metrics can be estimated, thereby a turbulence detection process was designed. Finally, some examples have been given according to the designed turbulence detection process. The demonstration results show that different aircrafts would react differently to a same turbulence. And the aircraft with the smaller wing loading, the higher the value of vertical load, and the greater the impact on the aircraft. Therefore, the design of an enhanced turbulence detection process is very helpful to reasonably predict areas of turbulence risks to aircraft by using airborne weather radars. Acknowledgements. We thank the Foundation Items: National Nature Science Foundation of China (NSFC) under grant U1633106, U1733116, National University’s Basic Research Foundation of China under grant No. 3122017006, and Foundation for Sky Young Scholars of Civil Aviation University of China.

References 1. Golding WL (2002) Turbulence and its impact on commercial aviation. J Aviat/Aerosp Educ Res 11(2):8 2. Newshub Live At 6 pm, http://www.newshub.co.nz/home/world/2018/04/three-injured-onair-india-ﬂight-as-another-plane-window-detaches.html 3. Lee JT, McPherson A (1971) Comparison of thunderstorms over Oklahoma and Malaysia based on aircraft measurements. In: Proceedings of the international conference on atmospheric turbulence, pp 1–13 4. Lu XG, Xia D (2011) Method for setting threshold of turbulence detection based on statistical conﬁdence level. J Civil Aviat Univ China 29(4):27–30 (in Chinese) 5. Collins R (2003) Collins WXR-2100 MultiScan™ radar fully automatic weather radar. Internet Citation. Jan, 1-1OPP 6. RTCA/DO-220A (2016) Minimum operational performance standards (MOPS) for airborne weather radar system. RTCA Inc, Washington D.C 7. Williams PD (2017) Increased light, moderate, and severe clear-air turbulence in response to climate change. Adv Atmos Sci 34(5):576–586 8. Bowles RL, Buck BK (2009) A methodology for determining statistical performance compliance for airborne Doppler radar with forward-looking turbulence detection capability. NASA CR. 215769 9. Warde DA, Torres SM (2014) Improved spectrum width estimators for Doppler weather radars. In: Proceedings of the 8th European conference on radar in meteorology and hydrology, Garmisch-Partenkirchen, p 8 10. Wright JR, Cooper JE (2008) Introduction to aircraft aeroelasticity and load. Wiley, England 11. Howe D (2004) Aircraft loading and structural layout. Professional Engineering Publishing, London

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12. Zhang JT (2005) Vertical and lateral gust loads analysis of airplane. Master, Northwestern Polytechnical University (in Chinese) 13. Zhao SN, Hu F (2015) Turbulence question: How do view “the homogenous and isotropic turbulence”? Scientia Sinica Physica, Mechanica & Astronomica 45(2):24701 (in Chinese)

Rain-Drop Size Distribution Case Study in Chengdu Based on 2DVD Observations Yan Liu1(&), Debin Su1,2, and Hongyu Lei1 1

Chengdu University of Information Technology, No. 24, Section 1 Xuefu Road, Southwest Airport Economic Development Zone, Chengdu 610225, China [email protected] 2 Key Laboratory of Atmospheric Detection, China Meteorological Administration, Chengdu 610225, China

Abstract. This paper selects the precipitation data of three precipitation processes on July 2, July 8 and July 11 of 2018 obtained from the two-dimensional video disdrometer (2DVD) of Chengdu University of Information Technology (CUIT). By counting the raindrop size distribution, calculating the total particle density and the median volume diameter during the sampling time to analyze the change of the raindrop spectrum during the precipitation process, and then calculating the precipitation intensity and the radar reflectivity factor during the sampling time. Combining the above related parameters for analysis, the following conclusions are obtained: The three precipitation processes are mainly composed of small raindrops with a diameter of 0.1–1 mm; Unstable precipitation will lead to a large change in the total particle density and median volume diameter, and the total particle density will change by 2 orders of magnitude, and the median volume diameter will vary by 1 mm. Keywords: 2DVD Raindrop size distribution Total particle density Median volume diameter Precipitation intensity

1 Introduction Researchers at home and abroad have been studying the observation of raindrop spectrum for a long time. In the early days, the raindrop spectrum was measured by the ﬁlter paper stain method and the flour ball method, the impact type raindrop spectrometer was developed in the 1960s, and the laser raindrop spectrometer appeared in the late 1990s. With the advancement of technology, Austrian Joanneum Research developed 2DVD to observe the raindrop spectrum. However, there are few studies on the water drop spectrum in China using 2DVD observation data. Liu et al. [1] analyzed the raindrop spectrum data of the Chengdu area obtained by laser raindrop spectrometer, and concluded that the precipitation intensity mainly depends on the heavy raindrops, and the contribution rate of the small raindrops is negatively correlated with the rain intensity. Zhou et al. [2] analyzed the data of the laser raindrop spectrometer in Shandong province, and concluded that the precipitation intensity mainly depends on the maximum raindrop diameter, which is positively © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 382–389, 2020 https://doi.org/10.1007/978-981-13-9409-6_45

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correlated with the raindrop concentration, but has little relationship with the average diameter. Gong et al. [3] analyzed raindrop spectrum data in Liaoning and concluded that the increase of urban aerosol particles would increase raindrop number density. In the summer of 2018, continuous rainstorm weather occurred in Chengdu. The analysis of the droplet spectrum characteristics of convective precipitation by using 2DVD observation data is of great signiﬁcance for further understanding the convective precipitation process, providing scientiﬁc basis for the numerical model, and quantitative estimation of precipitation by radar in summer in Chengdu.

2 Instruments and Data 2.1

Instrument Introduction

The raindrop data of this paper was continuously observed by the 2DVD in the observation ﬁeld of CUIT (103.98° E, 30.55° N). 2DVD is an advanced precipitation particle measuring device developed by Joanneum Research of Austria. It scans highspeed moving objects linearly by two cameras placed at different heights and at 90° to measure the size, shape, orientation and landing speed of individual precipitation particles in real time. 2DVD’s superior performance is measured in small objects. When the particle falling speed 1 @ sin a0 cos d0 A > ~ r0 ¼ parc > 100 ½N0 > < sin d10 0 cos a cos d0 > 0 > > > @ sin a0 cos d0 A ~ > ¼ ½N r 0 0 > : sin d0

if

p[0 ð4Þ

if

p¼0

0 1 8 la cos d0 =p > > > ~ 3 ð90 a0 ÞR 1 ð90 þ d0 Þ@ ld =p A > V ¼ ½N0 R > > < Vr 1 0 cos d0 l > a > > > 3 ð90 a0 ÞR 1 ð90 þ d0 Þ@ ld A ~ > V ¼ ½N0 R > : 0

if

p[0 ð5Þ

if

p¼0

where, ½N0 is the centroid equatorial coordinate system of the initial ephemeris, generally the [ICRS] coordinate system at time J2000.0, p is the parallax Angle of celestial bodies, la ; ld is the self-parameter of celestial bodies, and Vr is the apparent velocity of celestial bodies. After making this correction, we need to convert the star position r1 . vector ~ r10 into the unit direction vector ~ (2) Correction of annual parallax ~ r1 þ D~ r2 ¼ ~ r1 þ p~ r1 ~ r1 ~ R r2 ¼ ~

ð6Þ

p is the parallax Angle of celestial bodies, and ~ R is the coordinate vector of the earth’s center of mass relative to the center of the solar system. This position vector can be obtained by reading the solar system numerical ephemeris DE405, or by using the VSOP2000 analytic ephemeris [5]. For this project, the use of DE405, DE421 or DE430 calendar table can ensure that the accuracy requirements of the project. (3) Gravitational deflection correction of light ~ r2 þ D~ r3 ¼ ~ r2 þ h~ r2 ~ r2 ~ R r3 ¼ ~

ð7Þ

D is the complementary Angle between the direction of the sun at the center of the earth and the direction vector of the measured star, and ~ R is the unit direction vector of the sun relative to the center of the earth. (4) Correction of annual aberration

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1 ~ r3 ~ R_ r3 ~ c

ð8Þ

~ r3 þ D~ r4 ¼ ~ r3 þ r4 ¼ ~

c is the speed of light, ~ R_ is the instantaneous velocity vector of the earth’s center of mass relative to the center of the solar system, which can be obtained by reading the solar system DE numerical ephemeris. (5) Precession rotation of the earth N 1Þ~ ~ r4 þ D~ r5 ¼ ~ r4 þ ðP r4 r5 ¼ ~

ð9Þ

and the nutation matrix N are shown below The precession matrix P ¼ Rz ðZA ÞRy ðhA ÞRz ðfA Þ P

ð10Þ

¼ Rx ðe DeÞRz ðDwÞRx ðeÞ N

ð11Þ

fA is the moving component of the mean vernal equinox on the equator of the initial ephemeris; hA is the total displacement of the instantaneous mean celestial pole from the initial mean celestial pole, and is also the declination component of the movement of mean vernal equinox. ZA is the moving component of the mean vernal equinox on the instantaneous equator; Dw and De are meridional nutation and angular nutation. Their expressions and speciﬁc values are provided by the astronomical constant system, as detailed in the IERS2010 speciﬁcation [6]. (6) Diurnal parallax correction ~ r5 þ D~ r6 ¼ ~ r5 þ p~ r5 ~ r5 ~ RN r6 ¼ ~

ð12Þ

~ RN is the geocentric radial vector of the station (i.e. the position of the ground observer). (7) Correction of diurnal solar aberration ~ r6 þ D~ r7 ¼ ~ r6 þ r7 ¼ ~

1 ~ r6 ~ R_N r6 ~ c

ð13Þ

~ R_N is the velocity vector of the station’s Diurnal motion relative to the center of the earth. (8) Atmospheric refraction correction

~ r7 þ D~ r8 ¼ ~ r7 þ R secðzobs Þ~ r7 ð~ r7 ~ rz Þ ¼ sin1z7 f~ r7 sinðz7 RÞ þ~ rz sinðRÞg r8 ¼ ~ ~ r8 robs ¼ ~ ð14Þ

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~ rz is the direction vector of the local zenith at the time of observation, R is the refraction Angle of the atmosphere, zobs is the observed zenith distance of celestial bodies, and z7 is the true zenith distance of celestial bodies. However, the observation zenith distance zobs ¼ z7 R can only be obtained after the atmospheric refraction correction is completed, so the atmospheric refraction correction needs iteration to solve the exact value. Here, the atmospheric refractive Angle R can be calculated by the following formula when the zenith distance is not greater than 70°. 8 273:15 > < RðT; PÞ ¼ R0 PðmbÞ 1013:25 273:15 þ Tðo CÞ > : R0 ¼ 6000 :29 tanðzÞ 000 :06688 tan3 ðzÞ

ð15Þ

T, P, respectively, for the temperature and pressure of observation time, R0 is a standard atmospheric conditions (north latitude 45° sea level, the temperature 0 °C, pressure 1013.25 mm), the wavelength of 0.57 microns of yellow star approximate refraction. (9) Transformation from the equatorial coordinate system to the horizontal coordinate system All the above steps can be derived and calculated in the equatorial coordinate system, but in the actual observation, we need to get its position parameters in the horizontal coordinate system. The observation position of celestial bodies is converted from the right ascension and declination coordinates ðaobs ; dobs Þ in the geocentric equatorial coordinate system to the azimuth and altitude coordinates ðA; H Þ in the horizontal coordinate system, which can be achieved by the following formula 0

cos aobs cos dobs

1

B C C ~ robs ¼ ½Z½Z0 ½NB @ sin aobs cos dobs A sin dobs 0 1 cos aobs cos dobs B C p ¼ ½ZRy u Rz ðSl þ pÞB sin aobs cos dobs C @ A 2 sin dobs 0 1 cos A cos H B C C ¼ ½ZB @ sin A cos H A sin H

ð16Þ

Sl is the local sidereal time at the observation time, u is the astronomical latitude of the observation place, Sl þ p is the azimuth starting point of the horizon is astronomical north, and it is positive to the west, and the right-handed system. If you want to get the

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horizontal coordinates (north–east–south) deﬁned by the traditional left-handed system, just take the negative of the azimuth calculated here.

4 Simulation (1) Calculation of the position of stars Suppose there is a star, and the information of the catalog is as follows: SID: 647,080 Right ascension (hour): 0.001823085 ICRSJ2000 declination (degrees): 25.88645705 Right ascension proper motion (milliarcseconds/year): 20.23 Declination self (fems/year): −7.14 Parallax (femtosecond): 4.34 Apparent velocity (km/s): −31.0 Calculate the star’s position at 10:00:00 UTC on October 15, 2017. (1) Call iau_CAL2JD function (green calendar conversion to Julian day function), iau_DAT function (calculate the TAI UTC of the speciﬁed date, i.e. International atomic time coordinated universal time function) and iau_TAITT (international atomic time TAI conversion to earth time function) to calculate the TT corresponding to the observation time, TT = JD 2458041.91746741 (2) Call PLACE, and set the parameter as: The OBJECT = ‘*’ LOCATN = 0 ICOORD = 1 STAR (1) = 0.001823085 do STAR (2) = 25.88645705 do STAR (3) = 20.23 do STAR (4) = 7.14 do STAR (5) = 4.34 do STAR (6) = 31.0 do (3) Relative to the imaginary observer at the center of the earth, the apparent direction of the star is RA ¼ 0:2587296; DE ¼ 25:9869708: (2) Calculation example: calculation of the position of solar system celestial bodies Suppose Neptune is observed at a different position at 10:00:00 UTC on October 15, 2017.

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(1) Call iau_CAL2JD, iau_DAT and iau_TAITT to calculate the TT corresponding to the observation time. TT ¼ 2458041:91746741 JD (2) Call PLACE, and set the parameter as: The OBJECT = ‘NEPTUNE’ LOCATN = 0 ICOORD = 3 (3) Relative to the imaginary observer at the center of the earth, the astrometric direction of Neptune is RA ¼ 343:3945581; DE ¼ 8:0771549:

5 Conclusion In this paper, a time-reference and time-system conversion relation and correlation algorithm are designed for a high-precision space time reference system based on ground-based observation positions. The related influencing factors of ground-based observation positions are enumerated, and the common coordinate systems and their mutual conversion relation in high-precision space time reference system are strictly deﬁned. Completed the design involving time reference and time system, common coordinate system and mutual conversion relation, etc., provided the geometric information and spatial and temporal distribution information of space geographic space, and laid the theoretical foundation for subsequent high-precision celestial body observation, satellite precise positioning and other space applications.

References 1. Pan Q, Ye Z, Feng Q (2015) Design of uniﬁed space-time reference based on Beidou/RFID system. Electr Measur Technol 10:11–16 2. Chen D, Su Y, Cui H (2019) Temporal and spatial connotation and characteristics of entities in pan-spatial information system. Geomat Spatial Inform Technol 42:52–55 3. Lei W, Zhang H, Li K (2016) Calculation and comparison of two coordinate transformation models between GCRS and ITRS. J Geom Sci Technol 33(3):236–240 4. Zhang H, Zheng Y, Ma G (2011) Research on coordinate transformation between GCRS and ITRS. J Geodesy Geodynam 31(1):63–67 5. Su M, Bao H, Zhao J (2015) A deduction of implement formula on astronomic longitude and latitude reducing to center. Eng Survey Mapp 6:1–3 6. Lei W, Zhang H, Li K (2016) Effects of precession-nutation models update, polar motion, difference between UT1 and TT on coordinate transformation. J Spacecraft TT C Technol 35(1):53–62

Study on Two Types of Sensor Antennas for an Intelligent Health Monitoring System Yang Li, Licheng Yang, Xiaonan Zhao, Bo Zhang, and Cheng Wang(&) Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China {liyang_tongxin,xiaonan5875}@163.com, [email protected]

Abstract. In this study, two types of in-body sensor antennas, which were designed for intelligent health monitoring systems, are studied and discussed. The impendence matching of two types of in-body sensor antennas are investigated. The transmission characteristics of in-body sensor antennas are explored. The traits of these two types of in-body sensor antennas are summarized. And the application range of these in-body sensor antennas is also proposed. Keywords: Intelligent health monitoring system Body-centric wireless communications In-body sensor antenna Transmission characteristic

1 Introduction Due to the widespread application on Intelligent Health Monitoring System, sensor network gains more and more attentions from communicators and Antenna researchers [1, 2]. Generally, an intelligent health monitoring system based on body-centric wireless communications (BCWCs) [3] transmits the data collected inside of human body by a wireless in-body sensor. Considering the structure of human body, the physical dimension of an in-body sensor is limited to approximately 26 mm in length and approximately 10 mm in diameter. Restricted to its geometric size, the in-body antenna is supposed to have bad performances in the areas of in-body efﬁciency, absorption of electromagnetic waves and propagation loss [4]. To cope with these imperfections, there have been several types of in-body antenna design methodologies [5–8] which provide workable solution for Intelligent Health Monitoring System. In these previous researches, the methods of antenna design could be divided into two different design thought. The former was to place the antenna inside the in-body sensor [5–7]. On the contrary, in the later design, the antenna was fabricated on the outer wall of the in-body [8], which was in direct contact with the human body medium. However, a comparison of characteristics of these two types of in-body antenna has not been sufﬁciently studied and investigated.

© Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 415–421, 2020 https://doi.org/10.1007/978-981-13-9409-6_49

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This article is written in the following parts. Two types of in-body sensor antenna are shown in Sect. 2. Then, in Sect. 3, the transmission characteristics of antenna are investigated. Finally, the results and observations are summarized in Sect. 4.

2 Two Types of in-Body Sensor Antenna Two types of in-body sensor antenna, namely, the out-wall type antenna and the in-wall sensor antenna, are investigated and discussed. Refer to the validity of the experimental results, it is critical to adopt the correct simulation method and the correct experimental method. In addition, we found that the results of ﬁnite-difference time-domain (FDTD) simulation accorded well with the measurement results of the dipole antenna in deionized water. Thus, a correct simulation method was obtained. And a kind of human body equivalent liquid material, developed by SPEAG Co. Ltd., was used as human body tissue simulating liquid. This tissue simulating liquid was called as “HBTSL” in the following parts. Figure 1 shows the relative permittivity and conductivity of the liquid material, which indicates that there is no particularly difference between the value of the HBTSL and the measured data of human body tissues provided by Gabriel [9] in the frequency range of 200 MHz–2 GHz. As for numerical analysis, a commercial human torso-shaped phantom named “Torso”, whose shell is made of ﬁberglass (er = 3.5) was in use as a container of HBTSL, as shown in Fig. 1. An in-body sensor antenna is put inside the torso-shaped phantom and an external receiving sensor antenna is fabricated outside the torso-shaped phantom. The distance between two antennas is set to D = 74 mm (Fig. 2). In the former parts, two different types of in-body sensor antennas were proposed. In order to study these two antennas, the traits of the two types of in-body sensor antennas which were proposed in [10] are shown in Fig. 3. In situation (a), the distance of the sensor inside the in-body d = 4 mm to the center axis, the sensor separation of liquid from the human body in situation (b), and the out-wall sensor antenna with a distance d = 5 mm to the center axis, which means that the sensor antenna exposed to the human body medium.

Fig. 1. Relative permittivity and conductivity of the HBTSL.

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Front-view

In-body sensor antenna, l1

D Out-body sensor antenna, l2 l1=20 mm, l1=260 mm, D=74 mm

Fig. 2. Analysis model: torso-shaped human body phantom.

r=1

l1

d 10

r=1

d

30 l1 = 20 mm, d = 4 mm

l1 = 20 mm, d = 5 mm

In-wall sensor

Out-wall sensor

Fig. 3. Geometries of the in-body sensor antennas performed in [10]. a In-wall sensor antenna. b Out-wall sensor antenna.

The frequency characteristics of the input impedance of the two types of in-body sensor antenna are shown in Fig. 4. The results indicated that, in the frequency range of 200 MHz–2 GHz, in-wall sensor antenna is an electric small antenna, comparing to the wavelength. Its resistance is less than 20 Ω, which is quite small, and the reactance is negative, appearing capacitance. When it comes to out-wall sensor antenna, the resistance becomes large, while its reactance is X = 0 at 1.2 and 2 GHz. The wavelength of antenna in dielectric liquid becomes small. Presuming that the effective dielectric permittivity is from: eg ¼

er þ 1 2

ð1Þ

The effective wavelength kg at 1.2 GHz becomes 50 mm when er = 49, closed to the twice the dipole length, which is 20 mm in length, which resonance X = 0 at 1.2 GHz. The effective wavelength is given by: k0 kg pﬃﬃﬃﬃ eg

ð2Þ

eg is the effective dielectric permittivity approximated in Eq. (1). When er = 55.4 and l1 = 0.43kg, the effective wavelength kg = 47 mm, which manifests that the outwall sensor antenna is a kind of half-wavelength antenna at 1.2 GHz.

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(a)

(b)

Fig. 4. Impedance of the in-body sensor antennas. a Resistance R. b Reactance X.

Figure 5a shows that the input reflection coefﬁcient |S11| of in-wall sensor dipole antenna is quite big on the frequency range of 200 MHz–2 GHz, while the |S11| of outwall sensor antenna is smaller. As for the |S21|, which is from the in-body sensor antenna through the torso-shaped phantom to the external antenna, is performed in Fig. 5b, it indicates that in-wall sensor antenna has a higher performance of |S21| = −29 dB at 1 GHz, while the value of |S21| of out-wall sensor antenna is −57 dB. These simulation analysis results means that the electric length of antenna which is placed on the out-wall will increase when in contact with liquid and in this case in-body sensor antenna will gain a maximum of |S21|.

3 In-Body Antenna Transmission Characteristic To investigated transmission characteristic of these two sensors of in-body sensor antenna better; the transmission factor s [11] is adopted to weigh up the maximum received power of antenna in the case of complex conjugate matching. A source with an internal impedance of ZS supplies excitation for the antenna, while the external antenna with an internal impedance of ZL. The power PL is transmitted to the ZL. Pin is

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(a)

0

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(a) Isolated In-wall sensortype

|S11 | [dB]

(b) Surface type Out-wall sensor

-10

-20

-30

0

0.5

1

1.5

2

Frequency [GHz]

(b)

0

In-wall sensor

-10

ZS=50

, ZL= 50

Out-wall sensor

|S21 | [dB]

-20 -30 -40 -50 -60 -70 -80

0

0.5

1 1.5 Frequency [GHz]

2

Fig. 5. Scatter parameters of the in-body sensor antennas. a Reflection coefﬁcients. b Transmission coefﬁcients.

the input power, while Pinc is the incident power. The reflection coefﬁcients CS and CL looking toward the source ZS and the load ZL and Cin and Cout are the reflection coefﬁcients from Port 1 and Port 2. The transmission factor is determined as PL PL 1 1 jCL j2 ¼ ¼ s¼ jS21 j2 2 Pinc ZS ¼Z ;ZL ¼Zout Pin 1 jCS j j1 S22 CL j2 in

ð3Þ

The transmission factors of the two types of in-body sensor antennas are displayed in Fig. 6, which demonstrates that a local maximum exists in the range of frequency. It is clear that the transmission factor s is higher than the value of out-wall sensor antenna, due to the increasing of conductivity loss when the antenna touches with liquid. An inwall sensor in-body antenna, ZS = 3.02 + j2467.22 Ω and ZL = 18.62 + j467.14 Ω, a large value of s = −20.0 dB at 500 MHz is acquired. In a word, for the in-wall sensor antenna, the impedance matching performance is good but has a small value of transmission factor. As for the out-wall sensor antenna, the value of transmission factor is larger, but has a bad performance on impedance matching. And its impedance matching performance is influenced by the relative permittivity of material surrounding it. Besides, the conductivity of the material impacts the transmission factor of antenna.

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Transmission factor [dB]

0

Out-wall sensor

* ZS=Zin* , ZL=Zout

-10 -20 -30

In-wall sensor

-40 -50 -60

0

0.5

1

1.5

2

Frequency [GHz]

Fig. 6. Transmission factors of the in-body sensor antennas.

4 Conclusion In this study, two types of in-body sensor antenna which are designed for intelligent health monitoring systems are studied and investigated. The traits of antennas, the transmission characteristics of antennas, the influences of relative permittivity and conductivity are compared and discussed. The in-wall sensor in-body sensor antenna, which is generally placed inside of the in-body sensor, thus it has a small conductivity loss, though the impedance matching performance is not good enough in the frequency range of 200 MHz–2 GHz. For the out-wall sensor antenna, which is usually in contact with the human body tissue liquid, therefore, the impedance matching is good in the frequency range of 1–2 GHz, however, the conductivity loss of out-wall sensor is larger. The characteristics of these two type sensor antennas have been summarized. When it comes to the application of these antennas, we can implement the appropriate antenna according to the practical situation. The out-wall sensor antenna could be applied to make use of the out-wall sensor when the inner space of in-body is strictly limited. And matching circuits are not required because of its good performance on impedance matching. Otherwise, when the demand of received power is more critical, the in-wall sensor antenna with matching circuits is more appropriate. Acknowledgements. This work made use of the Funding Program of Tianjin Higher Education Creative Team. The authors acknowledge the Natural Science Foundation of Tianjin City (18JCYBJC86000, 18JCYBJC86400), the Science and Technology Development Fund of Tianjin Education Commission for Higher Education (2018KJ153) and the Doctoral Funding of Tianjin Normal University (52XB1604, 52XB1905) for supporting this work. C.W. acknowledges the Distinguished Young Talent Recruitment Program of Tianjin Normal University (011/5RL153). The authors also would like to thank Professor Qiang Chen at Tohoku University for allowing us to use the computer with the electromagnetic software installed in his lab.

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References 1. Liang Q, Cheng X, Huang SC, Chen D (2014) Opportunistic sensing in wireless sensor networks: theory and applications. IEEE Trans Comput 63(8):2002–2010 2. Liang Q, Chen X, Samn SW (2010) NEW: network-enabled electronic warfare for target recognition. IEEE Trans Aerosp Electr Syst 46(2):558–568 3. Hall PS, Hao Y (2012) Antennas and propagation for body-centric wireless communications, 2nd edn. Artech House, London, England, UK, pp 586–589 4. Iddan G, Meron G, Glukhovsky A, Swain P (2000) Wireless capsule endoscopy. Nature 405(6785):417 5. Chirwa LC, Hammond PA, Roy S, Cumming DRS (2003) Electromagnetic radiation from ingested sources in the human intestine between 150MHz and 1.2GHz. IEEE Trans Biomed Eng 50(4):484–492 6. Izdebski PM, Rajagopalan H, Rahmat-Samii Y (2009) Conformal ingestible capsule antenna: a novel chandelier meandered design. IEEE Trans Antenn Propag 57(4):900–909 7. Lee SH, Lee J, Yoon YJ, Park S, Cheon C, Kim K, Nam S (2011) A wideband spiral antenna for ingestible capsule endoscope systems: experimental results in a human phantom and a pig. IEEE Trans Biomed Eng 58(6):1734–1741 8. Yun S, Kim K, Nam S (2010) Outer wall loop antenna for ultra wideband capsule endoscope system. IEEE Antenn Wirel Propag Lett 9:1135–1138 9. Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz–20 GHz. Phys Med Biol 41(11):2251–2269 10. Sato H, Li Y, Xu J, Chen Q (2018) Design of inner-layer capsule dipole antenna for ingestible endoscope. In: Proceedings of 2018 international symposium on antennas and propagation (ISAP 2018). Busan, Oct 2018 11. Chen Q, Ozawa K, Yuan QW, Sawaya K (2012) Antenna characterization for wireless power-transmission system using near-ﬁeld coupling. IEEE Trans Antenn Propag Mag 54(4):108–116

A Fiber Bragg Grating Acceleration Sensor for Measuring Bow Slamming Load Jingping Yang(&), Wei Wang(&), Yuliang Li, Libo Qiao, and ChuanQi Liu Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected], [email protected]

Abstract. In view of the serious consequences of Slamming Loads on ships at high speed, a ﬁber Bragg grating acceleration sensor for measuring the Slamming Loads on bows is designed in this paper. It is mainly composed of the sensor shell, the sensor shell cover, the accelerometer sensitive devices and the hinge structure, which realize the automatic monitoring of the Slamming Loads on bows. The experimental results show that the sensitivity of the sensor is 295 pm/g in the frequency range of 0–100 Hz. Keywords: Slamming loads Flexible hinge

Fiber Bragg grating Acceleration sensor

1 Introduction When a ship sails at high speed under harsh conditions, bottom of bow will be severely impacted by waves. At the moment of slamming, the vertical acceleration of the hull will suddenly change, and then the hull will vibrate at high frequency. Strong impact cause a series of serious consequences, therefore slamming load measurement should be one of the safety standards for high-speed ship design. Acceleration sensors can count the magnitude, frequency and location of vibration, they can judge the safety status of hull. In the design of acceleration sensor, the cantilever beam structure is the most used model. Although this structure can satisfy the requirement of frequency, its sensitivity will be limited by frequency, it is also vulnerable to lateral interference. Wang Hongliang designed a double-strength cantilever beam model, the working range of this structure is too small, when the frequency of vibration is about 80 Hz on the ship, the system will resonate and be damaged [1]. Zhang Dongsheng designed a FBG vibration sensor with a tubular model, which placed the mass block in the middle of the steel tube, pasted the mass block inside the steel tube with a double grating ﬁber, and changed the wavelength of the ﬁber through the vibration of the mass block [2]. This structure is complex in the manufacture of the sensor, and has weak anti-transverse interference ability. In order to satisfy the stability, sensitivity and measurement range of acceleration sensor system, a new type of sensor is proposed in the paper, it realizes the automatic monitoring of the bow slamming load of the hull structure, provides real and reliable data for the decision-makers on the ship, provides scientiﬁc basis. © Springer Nature Singapore Pte Ltd. 2020 Q. Liang et al. (Eds.): CSPS 2019, LNEE 571, pp. 422–430, 2020 https://doi.org/10.10