Cover......Page 1
SIGNALPROCESSING FORMULTISTATICRADAR SYSTEMS......Page 4
Copyright......Page 5
Contents......Page 6
About the Authors......Page 9
Preface......Page 10
Symbols......Page 12
1.1 Historical background......Page 14
1.2 Purpose and scope......Page 17
Part 1: Adaptive waveform selection......Page 19
Part 2: Optimal geometry analysis......Page 20
Part 3: Pseudolinear tracking algorithms......Page 21
Part 1 Adaptive waveform selection......Page 23
2.1 Introduction and system overview......Page 25
2.2 Bistatic radar measurements......Page 26
2.3 Bistatic ambiguity function and Cramér-Rao lower bounds......Page 27
Nearly constant velocity model......Page 29
Nearly coordinated turn model......Page 30
2.4.2 Observation model......Page 31
2.4.3 Interacting multiple model - extended Kalman filter......Page 32
2.5 Adaptive waveform selection......Page 35
2.6 Simulation examples......Page 36
Adaptive waveform versus fixed waveform......Page 38
IMM-EKF versus EKF......Page 40
Multistatic radar versus bistatic radar......Page 42
2.8 Appendix......Page 43
3.1 Introduction and system overview......Page 45
Local track estimation at receivers......Page 47
Track-to-track fusion at transmitter......Page 49
3.3 Adaptive waveform selection......Page 50
3.4 Simulation examples......Page 52
3.5 Summary......Page 55
4.1 Introduction and system overview......Page 56
Target position......Page 58
Target velocity......Page 59
Target state vector......Page 60
4.3 Cramér-Rao lower bounds......Page 61
Observation equation......Page 62
Joint optimal selection of radar waveform and Cartesian estimate......Page 63
CRLBs of Cartesian state estimates......Page 64
Performance advantages of joint selection of radar waveform and Cartesian estimate......Page 67
4.6 Summary......Page 72
5.1 Introduction and system overview......Page 73
5.2.1 Phase A - target tracking......Page 75
5.2.2 Phase B - adaptive waveform selection......Page 76
5.4 Simulation examples......Page 77
5.5 Summary......Page 82
Part 2 Optimal geometry analysis......Page 83
6.1 Introduction and problem formulation......Page 86
6.2 Optimal geometry analysis......Page 89
Example 1: Two receivers (N=2)......Page 95
Example 4: Even number of receivers with i.i.d. noise......Page 96
Example 5: Odd number of receivers with i.i.d. noise......Page 97
6.4.1 Numerical solutions......Page 98
6.4.2 Sensor trajectory optimization......Page 99
6.5 Summary......Page 104
Appendix A......Page 105
Appendix B......Page 106
7.1 Introduction and problem formulation......Page 107
7.2 Optimal geometry analysis......Page 109
7.3 Simulation examples......Page 114
7.4 Summary......Page 116
Part 3 Pseudolinear tracking algorithms......Page 119
8.1 Introduction......Page 120
8.2 Problem formulation......Page 122
8.3 Maximum likelihood estimator and Cramér-Rao lower bound......Page 125
8.4 Pseudolinear estimator......Page 127
TDOA......Page 128
8.4.2 Pseudolinear least-squares......Page 130
8.5.1 Bias analysis......Page 131
8.5.2 Bias compensation......Page 133
8.6 Asymptotically-unbiased weighted instrumental variable estimator......Page 134
8.7 Asymptotic efficiency analysis......Page 137
8.9 Algorithm performance and comparison......Page 140
Simulation Example 1......Page 142
Simulation Example 2 (Large TDOA noise)......Page 143
8.10 Summary......Page 145
Appendix A......Page 146
Appendix B......Page 147
Appendix C......Page 148
9.1 Introduction......Page 149
9.2 Maximum likelihood estimator and Cramér-Rao lower bound......Page 152
Stage 1......Page 153
Stage 2......Page 155
Stage 3......Page 157
9.4 Bias analysis......Page 158
9.5.1 Augmented solution with quadratic constraint......Page 160
9.5.2 Instrumental-variable based solution......Page 163
9.6 Algorithm performance and comparison......Page 164
9.7 Summary......Page 168
Bibliography......Page 169
Index......Page 175
Back Cover......Page 180