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

✍ Scribed by Dr. Ngoc Hung Nguyen

• Publisher: Academic Press
• Year: 2019
• Tongue: English
• Leaves: 180
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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