MIMO RADAR SIGNAL PROCESSING

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1 MIMO RADAR SIGNAL PROCESSING Edited by JIAN LI PETRE STOICA WILEY A JOHN WILEY & SONS, INC., PUBLICATION

PREFACE CONTRIBUTORS xiii xvii 1 MIMO Radar Diversity Means Superiority 1 Лап Li and Petre Stoica 1.1 Introduction 1 1.2 Problem Formulation 4 1.3 Parameter Identifiability 5 1.3.1 Preliminary Analysis 5 1.

2 PREFACE CONTRIBUTORS xiii xvii 1 MIMO Radar Diversity Means Superiority 1 Лап Li and Petre Stoica 1.1 Introduction Problem Formulation Parameter Identifiability Preliminary Analysis Sufficient and Necessary Conditions Numerical Examples Nonparametric Adaptive Techniques for Parameter Estimation Absence of Array Calibration Errors Presence of Array Calibration Errors Numerical Examples Parametric Techniques for Parameter Estimation ML and BIC Numerical Examples Transmit Beampattern Designs Beampattern Matching Design Minimum Sidelobe Beampattern Design Phased-Array Beampattern Design 39

1.6.4 Numerical Examples 40 1.6.5 Application to Ultrasound Hyperthermia Treatment of Breast Cancer 47 1.

3 1.6.4 Numerical Examples Application to Ultrasound Hyperthermia Treatment of Breast Cancer Conclusions 56 Appendix IA Generalized Likelihood Ratio Test 57 Appendix IB Lemma and Proof 59 Acknowledgments 60 References 60 MIMO Radar: Concepts, Performance Enhancements, and Applications 65 Keith W. Forsythe and Daniel W. Bliss 2.1 Introduction A Short History of Radar Definition and Characteristics of MIMO Radar Uses of MIMO Radar The Current State of МГМО Radar Research Chapter Outline Notation MIMO Radar Virtual Aperture МГМО Channel МГМО Virtual Array: Resolution and Sidelobes MIMO Radar in Clutter-Free Environments Limitations of Cramer-Rao Estimation Bounds Signal Model Fisher Information Matrix Waveform Correlation Optimization Examples 85' 2.5 Optimality of МГМО Radar for Detection Detection High SNR Weak-Signal Regime Optimal Beamforming without Search Nonfading Targets Some Additional Benefits of MIMO Radar МГМО Radar with Moving Targets in Clutter: GMTI Radars Signal Model Localization and Adapted SNR Inner Products and Beamwidths SNR Loss 103

vii 2.6.5 SNR Loss and Waveform Optimization 107 2.6.6 Area Search Rates 109 2.6.7 Some Examples 109 2.

4 vii SNR Loss and Waveform Optimization Area Search Rates Some Examples Summary 111 Appendix 2A A Localization Principle 111 Appendix 2B Bounds on R(N) 114 Appendix 2C An Operator Norm Inequality 115 Appendix 2D Negligible Terms 115 Appendix 2E Bound on Eigenvalues 115 Appendix 2F Some Inner Products 116 Appendix 2G An Invariant Inner Product 117 Appendix 2H Krönecker and Tensor Products 118 2H.1 Lexicographical Ordering 118 2H.2 Tensor and Krönecker Products 118 2H.3 Properties 119 Acknowledgments 119 References Generalized MIMO Radar Ambiguity Functions 123 Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey 3.1 Introduction Background MIMO Signal Model MIMO Parametric Channel Model Transmit Signal Model Channel and Target Models Received Signal Parametric Model MIMO Ambiguity Function MIMO Ambiguity Function Composition Cross-Correlation Function under Model Simplifications Autocorrelation Function and Transmit Beampatterns Results and Examples Orthogonal Signals Coherent Signals Conclusion 149 References 150

Vlll 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars 153 Joseph Tabrikian 4.1 Introduction 153 4.2 Problem Formulation 155 4.3 Properties 158 4.3.1 Virtual Aperture Extension 159 4.

5 Vlll 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars 153 Joseph Tabrikian 4.1 Introduction Problem Formulation Properties Virtual Aperture Extension Spatial Coverage and Probability of Exposure Beampattern Improvement Target Localization Maximum-Likelihood Estimation Transmission Diversity Smoothing Performance Lower Bound for Target Localization Cramer-Rao Bound The Barankin Bound Simulation Results Discussion and Conclusions 180 Appendix 4A Log-Likelihood Derivation 181 4A.1 General Model 182 4A.2 Single Range-Doppler with No Interference Appendix 4B Transmit-Receive Pattern Derivation 185 Appendix 4C Fisher Information Matrix Derivation 186 References Adaptive Signal Design For MEMO Radars 193 Benjamin Friedlander Introduction Problem Formulation Signal Model with Reduced Number of Range Cells Multipulse and Doppler Effects The Complete Model The Statistical Model Estimation Beamforming Solution Least-Squares Solutions Waveform Design for Estimation Detection The Optimal Detector The SINR

ix 5.4.3 Optimal Waveform Design 217 5.4.4 Suboptimal Waveform Design 218 5.4.5 Constrained Design 219 5.4.6 The Target and Clutter Models 220 5.4.7 Numerical Examples 221 5.

6 ix Optimal Waveform Design Suboptimal Waveform Design Constrained Design The Target and Clutter Models Numerical Examples МГМО Radar and Phased Arrays Scan Transmit Beam after Receive Adaptation of Transmit Beampattern Combined Transmit-Receive Beamforming 229 Appendix 5A Theoretical SINR Calculation 231 References MIMO Radar Spacetime Adaptive Processing and Signal Design 235 Chun-Yang Chen and P. P. Vaidyanathan 6.1 Introduction Notations The Virtual Array Concept Spacetime Adaptive Processing in МГМО Radar Signal Model Fully Adaptive MDVIO-STAP Comparison with SDVIO System The Virtual Array in STAP Clutter Subspace in MIMO Radar Clutter Rank in MIMO Radar: МГМО Extension of Brennan's Rule Data-Independent Estimation of the Clutter Subspace with PSWF New STAP Method for МГМО Radar The Proposed Method Complexity of the New Method Estimation of the Covariance Matrices Zero-Forcing Method Comparison with Other Methods Numerical Examples Signal Design of the STAP Radar System MIMO Radar Ambiguity Function Some Properties of the МГМО Ambiguity Function The МГМО Ambiguity Function of Periodic Pulse Radar Signals Frequency-Multiplexed LFM Signals Frequency-Hopping Signals 276

X 6.8 Conclusions 278 Acknowledgments 279 References 279 7 Slow-Time MIMO SpaceTime Adaptive Processing 283 Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik 7.

7 X 6.8 Conclusions 278 Acknowledgments 279 References Slow-Time MIMO SpaceTime Adaptive Processing 283 Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik 7.1 Introduction МГМО Radar and Spatial Diversity MIMO and Target Fading MIMO and Processing Gain SIMO Radar Modeling and Processing Generalized Transmitted Radar Waveform SIMO Target Model SDVIO Covariance Models SDVIO Radar Processing Slow-Time MIMO Radar Modeling Slow-Time MIMO Target Model Slow-Time MMO Covariance Model Slow-Time MIMO Radar Processing Slow-Time МГМО Beampattern and VSWR Subarray Slow-Time MIMO SIMO versus Slow-Time МГМО Design Comparisons MIMO Radar Estimation of Transmit-Receive Directionality Spectrum OTHr Propagation and Clutter Model Simulations Examples Postreceive/Transmit Beamforming SINR Performance Transmit-Receive Spectrum Conclusion 316 Acknowledgment 316 References MIMO as a Distributed Radar System 319 H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy 8.1 Introduction Systems Signal Model Spatial MIMO System 325

xi 8.2.3 Netted Radar Systems 325 8.2.4 Decentralized Radar Network (DRN) 327 8.3 Performance 329 8.3.1 False-Alarm Rate (FAR) 329 8.3.2 Probability of Detection (P d ) 336 8.3.3 Jamming Tolerance 348 8.

8 xi Netted Radar Systems Decentralized Radar Network (DRN) Performance False-Alarm Rate (FAR) Probability of Detection (P d ) Jamming Tolerance Coverage Conclusions 359 Acknowledgment 361 References 361 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas 365 Hana Godrich, Alexander M. Haimovich, and Rick S. Blum 9.1 Background MIMO Radar Concept Signal Model Spatial Decorrelation Other Multiple Antenna Radars Noncoherent МГМО Radar Applications Diversity Gain Moving-Target Detection Coherent MIMO Radar Applications Ambiguity Function CRLB MLE Target Localization BLUE Target Localization GDOP Discussion Chapter Summary 399 Appendix 9A Deriving the FTM 400 Appendix 9B Deriving the CRLB on the Location Estimate Error 403 Appendix 9C MLE of Time Delays Error Statistics 405 Appendix 9D Deriving the Lowest GDOP for Special Cases 407 9D.1 Special Case: N x N MIMO 407 9D.2 Special Case: 1 x N MIMO 408 9D.3 General Case: M x N МГМО 408 Acknowledgments 408 References 408

Xll 10 SpaceTime Coding for MEMO Radar 411 Antonio De Maio and Marco Lops 10.1 Introduction 411 10.2 System Model 415 10.3 Detection In MEMO Radars 417 10.3.1 Full-Rank Code Matrix 419 10.3.2 Rank 1 Code Matrix 420 10.

9 Xll 10 SpaceTime Coding for MEMO Radar 411 Antonio De Maio and Marco Lops 10.1 Introduction System Model Detection In MEMO Radars Full-Rank Code Matrix Rank 1 Code Matrix Spacetime Code Design Chernoff-Bound-Based (CBB) Code Construction SCR-Based Code Construction Mutual-Information-Based (MIB) Code Construction The Interplay Between STC and Detection Performance Numerical Results Adaptive Implementation Conclusions 441 Acknowledgment 442 References 442 INDEX 445

MIMO RADAR SIGNAL PROCESSING

MIMO RADAR SIGNAL PROCESSING Edited by JIAN LI PETRE STOICA MIMO RADAR SIGNAL PROCESSING MIMO RADAR SIGNAL PROCESSING Edited by JIAN LI PETRE STOICA Copyright # 2009 by John Wiley & Sons, Inc. All rights