Signal processing for multistatic radar systems: adaptive waveform selection, optimal geometries and pseudolinear tracking algorithms

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
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