Signal Processing for Multistatic Radar Systems: Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms

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Signal Processing for Multistatic Radar Systems: Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms addresses three important aspects of signal processing for multistatic radar systems, including adaptive waveform selection, optimal geometries and pseudolinear tracking algorithms. A key theme of the book is performance optimization for multistatic target tracking and localization via waveform adaptation, geometry optimization and tracking algorithm design. Chapters contain detailed mathematical derivations and algorithmic development that are accompanied by simulation examples and associated MATLAB codes. This book is an ideal resource for university researchers and industry engineers in radar, radar signal processing and communications engineering. Cover SIGNAL PROCESSING FOR MULTISTATIC RADAR SYSTEMS Copyright Contents About the Authors Preface List of Abbreviations and Symbols Abbreviations Symbols 1 Introduction 1.1 Historical background 1.2 Purpose and scope 1.3 Outline of book Part 1: Adaptive waveform selection Part 2: Optimal geometry analysis Part 3: Pseudolinear tracking algorithms Part 1 Adaptive waveform selection 2 Waveform selection for multistatic tracking of a maneuvering target 2.1 Introduction and system overview 2.2 Bistatic radar measurements 2.3 Bistatic ambiguity function and Cramér-Rao lower bounds 2.4 Target tracking 2.4.1 Target dynamic model Nearly constant velocity model Nearly constant acceleration model Nearly coordinated turn model Multiple models 2.4.2 Observation model 2.4.3 Interacting multiple model - extended Kalman filter 2.5 Adaptive waveform selection 2.6 Simulation examples Adaptive waveform versus fixed waveform IMM-EKF versus EKF Multistatic radar versus bistatic radar 2.7 Summary 2.8 Appendix 3 Waveform selection for multistatic target tracking in clutter 3.1 Introduction and system overview 3.2 Tracking algorithm with probabilistic data association Local track estimation at receivers Track-to-track fusion at transmitter 3.3 Adaptive waveform selection 3.4 Simulation examples 3.5 Summary 4 Waveform selection for multistatic target tracking with Cartesian estimates 4.1 Introduction and system overview 4.2 Target position and velocity estimation in Cartesian coordinates Target position Target velocity Target state vector 4.3 Cramér-Rao lower bounds 4.4 Target tracking with joint selection of radar waveform and Cartesian estimate Target dynamics Observation equation Target tracking with linear Kalman filter Joint optimal selection of radar waveform and Cartesian estimate 4.5 Simulation examples CRLBs of Cartesian state estimates Performance advantages of joint selection of radar waveform and Cartesian estimate 4.6 Summary 5 Waveform selection for distributed multistatic target tracking 5.1 Introduction and system overview 5.2 Algorithm description 5.2.1 Phase A - target tracking 5.2.2 Phase B - adaptive waveform selection 5.3 Communication complexity 5.4 Simulation examples 5.5 Summary Part 2 Optimal geometry analysis 6 Optimal geometries for multistatic target localization with one transmitter and multiple receivers 6.1 Introduction and problem formulation 6.2 Optimal geometry analysis 6.3 Examples Example 1: Two receivers (N=2) Example 2: Three receivers (N=3) Example 3: Four receivers (N=4) Example 4: Even number of receivers with i.i.d. noise Example 5: Odd number of receivers with i.i.d. noise 6.4 Simulations 6.4.1 Numerical solutions 6.4.2 Sensor trajectory optimization 6.5 Summary 6.6 Appendices Appendix A Appendix B 7 Optimal geometries for multistatic target localization by independent bistatic channels 7.1 Introduction and problem formulation 7.2 Optimal geometry analysis 7.3 Simulation examples 7.4 Summary Part 3 Pseudolinear tracking algorithms 8 Batch track estimators for multistatic target motion analysis 8.1 Introduction 8.2 Problem formulation 8.3 Maximum likelihood estimator and Cramér-Rao lower bound 8.4 Pseudolinear estimator 8.4.1 Pseudolinear equations AOA TDOA FDOA 8.4.2 Pseudolinear least-squares 8.5 Bias compensation for pseudolinear estimator 8.5.1 Bias analysis 8.5.2 Bias compensation 8.6 Asymptotically-unbiased weighted instrumental variable estimator 8.7 Asymptotic efficiency analysis 8.8 Computational complexity 8.9 Algorithm performance and comparison Simulation Example 1 Simulation Example 2 (Large TDOA noise) 8.10 Summary 8.11 Appendices Appendix A Appendix B Appendix C 9 Closed-form solutions for multistatic target localization with time-difference-of-arrival measurements 9.1 Introduction 9.2 Maximum likelihood estimator and Cramér-Rao lower bound 9.3 Three-stage least-squares solution Stage 1 Stage 2 Stage 3 9.4 Bias analysis 9.5 Bias compensation techniques 9.5.1 Augmented solution with quadratic constraint 9.5.2 Instrumental-variable based solution 9.6 Algorithm performance and comparison 9.7 Summary Bibliography Index Back Cover