Beamforming: Sensor Signal Processing for Defence Applications (Communications and Signal Processing)

__Beamforming: Sensor Signal Processing for Defence Applications__ presents a range of important research contributions concerned with sensor array signal processing and, in particular, with the superresolution beamformers fundamental to many civilian and defence applications. Both space and space-time (STAP) beamforming algorithms and their application to radar systems are considered with emphasis given to "look-down" airborne radars, synthetic aperture radar (SAR), arrayed MIMO radar and a number of common wake-wave detection algorithms for two-dimensional SAR imagery. Furthermore, ocean towed arrays, which find applications in a variety of areas such as defence, oil and gas exploration, and geological and marine life studies, are also considered paying particular attention to receiver positional uncertainties resulting from the array's flexible structure. Array geometrical and electrical uncertainties, design of auto-calibration algorithms, beamforming "pointing" error uncertainties and robustification issues are also presented. This book is self-contained and unified in its presentation, and comprehensively covers some of the classic and fundamental models of beamforming for sensor signal processing. It is suitable as an advanced textbook for graduate students and researchers in the area of signal processing, as well as a reference book for engineers in the defence industry. Readership: Postgraduate students and researchers working in the area of signal processing as well researchers working in the defence industry. The UDRC runs a series of short courses in signal processing for PhD students and industrial researchers and this book is recommended reading Contents 12 Preface 8 Acknowledgments 10 List of Notations 18 1. Space-Time Adaptive Beamforming Algorithms for Airborne Radar Systems 20 1.1 Introduction 20 1.2 Pulsed Doppler radar: System and signal models 22 1.3 Conventional beamforming 24 1.4 Low-rank beamforming algorithms 25 1.4.1 Eigenvalue-decomposition-based algorithms 27 1.4.2 Krylov subspace-based algorithms 28 1.4.3 Joint iterative optimization (JIO)-based algorithms 29 1.4.4 Joint interpolation, decimation and filtering (JIDF)-based algorithms 31 1.5 Sparsity-aware beamforming algorithms 34 1.6 Knowledge-aided beamforming algorithms 36 1.7 Simulations 38 1.8 Concluding remarks 43 References 43 2. Transmit Beamforming for Forward-Looking Space-Time Radars 48 2.1 Introduction 48 2.2 Principles of STAP 51 2.2.1 Array response vectors 51 2.2.2 Scatterer response 53 2.2.3 Clutter 53 2.2.4 Optimum STAP receiver processing 54 2.2.5 Side-looking radar 58 2.2.6 Forward-looking radar 58 2.3 Adaptive transmit diversity STAP 60 2.3.1 Signal model 61 2.3.2 Space-time illumination patterns 62 2.3.3 3D Doppler compensation 66 2.4 Ambiguous range transmit nulling 68 2.4.1 Angular location of ambiguous ranges 71 2.4.2 Beampattern design 72 2.4.3 Simulation results 75 2.5 Summary 79 References 80 3. Digital Beamforming for Synthetic Aperture Radar 82 3.1 SAR radarmain parameters 83 3.2 SISO SAR 87 3.2.1 Stripmap SAR 87 3.2.2 ScanSAR 93 3.2.3 Spotlight SAR 94 3.2.4 Discrete time modelling 95 3.3 SIMO SAR 97 3.3.1 SIMO SAR system mathematical modelling 98 3.3.2 Discrete time modelling 100 3.4 Beamforming in the elevation and cross-range direction using SIMO SAR 101 3.4.1 SIMO SAR parameter design 102 3.4.2 Beamforming in the elevation direction 105 3.4.3 Beamforming in the cross-range direction 108 3.4.3.1 SIMO SAR examples 117 3.5 Target parameter estimation using SIMO SAR 120 3.5.1 Round trip delay estimation 120 3.5.2 Joint direction of arrival and slant range estimation 124 3.5.3 Joint direction of arrival and power estimation 131 3.6 Summary and conclusions 134 References 135 4. Arrayed MIMO Radar: Multi-target Parameter Estimation for Beamforming 138 4.1 Introduction 139 4.2 Arrayed MIMO radar received signal model 140 4.3 Space arrayed MIMO radar: Target echoes arriving with equal delays 145 4.3.1 Least squares 146 4.3.2 Capon’s method 147 4.3.3 Amplitude and phase estimation (APES) 148 4.3.4 Discussion 149 4.3.5 Comparative studies and computer simulation results 149 4.3.5.1 Finite averaging effects 153 4.3.5.2 Noise effects (variable levels of PTσ2n) 153 4.4 Arrayed MIMO radar: Target echoes with different delays 156 4.4.1 Spatiotemporal arrayed MIMO radar: Doppler, delay, DOA and path gains estimation 157 4.4.1.1 Subspace partitioning and delay estimation 158 4.4.1.2 Joint DOA-Doppler estimation 161 4.4.1.3 Complex fading coefficients estimation 163 4.4.1.4 Algorithm summary — spatiotemporal arrayed MIMO 164 4.4.2 Iterative adaptive approach (IAA) 164 4.4.3 Simulation studies 166 4.4.3.1 Simulated environment 1: Stationary targets 167 4.4.3.2 Simulated environment 2: Moving targets 169 4.4.4 Complexity analysis 173 4.5 Conclusions 173 4.A Appendix: Equivalent two-stage estimation 174 References 176 5. Beamforming for Wake Wave Detection and Estimation — An Overview — 178 5.1 Introduction 178 5.2 Types of ship wake waves 180 5.2.1 Ship-generated surface wakes 180 5.2.2 Turbulent wakes 183 5.2.3 Ship-generated internal wake waves 183 5.3 Environmental conditions and SAR parameters for wake wave imaging 184 5.4 Detection approaches for wake waves 185 5.4.1 Pre-processing stage 185 5.4.2 Transformstage 186 5.4.3 Post-processing 189 5.5 Estimation of parameters from ship wake waves 191 5.5.1 Parameter estimation from Kelvin envelope 191 5.5.2 Parameter estimation from stern waves 192 5.5.3 Parameter estimation from turbulent wake 193 5.6 SAR for ocean applications 194 5.6.1 Interferometric SAR 194 5.6.2 SAR interferometry configurations for ocean applications 198 5.7 Summary and conclusions 203 References 204 6. Towed Arrays: Channel Estimation, Tracking and Beamforming 208 6.1 Introductory concepts and classification 208 6.2 Family of instrument-based calibration techniques 210 6.3 Family of data-based calibration techniques 211 6.3.1 Pilot calibration 212 6.3.2 Self-calibration 212 6.3.3 Auto-calibration 214 6.3.4 Robustification against uncertainties 214 6.4 Towed array signalmodel 215 6.5 Synthetic data generation and BellHop framework 217 6.6 Subspace pilot calibration techniques 222 6.7 Robustification techniques: The H∞ state space model 224 6.8 Experimental evaluation of techniques and discussion 227 6.8.1 Experiments with subspace pilot calibration 227 6.8.2 Experiments with H∞-based robustification technique 228 6.8.2.1 Experimental results using synthetic towed array data 228 6.8.2.2 Experimental results using real towed array data fromsea trials 231 6.9 Conclusions 235 References 236 7. Array Uncertainties and Auto-calibration 240 7.1 Introduction 240 7.2 Signal model 244 7.2.1 Array manifold vector 248 7.2.2 Changing the array reference point 249 7.2.2.1 Geometric case 250 7.2.2.2 Approximate case 250 7.3 Array auto-calibration 252 7.3.1 Measurement phase 254 7.3.2 Array shape estimation phase 256 7.3.3 Complex gain estimation phase 260 7.4 Performance evaluation 263 7.4.1 Small aperture array 263 7.4.2 Large aperture array 268 7.4.3 A representative example of the effects of uncertainties on a large aperture array 270 7.5 Summary and conclusions 279 References 279 8. Robust Beamforming to Pointing Errors 282 8.1 Introduction 283 8.2 Estimation of the linear combination vector using signal subspace 286 8.3 Estimation of the desired signal manifold via vector space projections (VSP) 290 8.4 Desired signal power estimation 293 8.5 Interference cancellation beamformer 294 8.6 Performance analysis in the presence of pointing errors 296 8.7 Simulation results 298 8.8 Summary and conclusions 303 References 303 Index 306 Content: ""Contents"" ""Preface"" ""Acknowledgments"" ""List of Notations"" ""1. Space-Time Adaptive Beamforming Algorithms for Airborne Radar Systems"" ""1.1 Introduction"" ""1.2 Pulsed Doppler radar: System and signal models"" ""1.3 Conventional beamforming"" ""1.4 Low-rank beamforming algorithms"" ""1.4.1 Eigenvalue-decomposition-based algorithms"" ""1.4.2 Krylov subspace-based algorithms"" ""1.4.3 Joint iterative optimization (JIO)-based algorithms"" ""1.4.4 Joint interpolation, decimation and filtering (JIDF)-based algorithms"" ""1.5 Sparsity-aware beamforming algorithms"" ""1.6 Knowledge-aided beamforming algorithms""""1.7 Simulations"" ""1.8 Concluding remarks"" ""References"" ""2. Transmit Beamforming for Forward-Looking Space-Time Radars"" ""2.1 Introduction"" ""2.2 Principles of STAP"" ""2.2.1 Array response vectors"" ""2.2.2 Scatterer response"" ""2.2.3 Clutter"" ""2.2.4 Optimum STAP receiver processing"" ""2.2.5 Side-looking radar"" ""2.2.6 Forward-looking radar"" ""2.3 Adaptive transmit diversity STAP"" ""2.3.1 Signal model"" ""2.3.2 Space-time illumination patterns"" ""2.3.3 3D Doppler compensation"" ""2.4 Ambiguous range transmit nulling""""2.4.1 Angular location of ambiguous ranges"" ""2.4.2 Beampattern design"" ""2.4.3 Simulation results"" ""2.5 Summary"" ""References"" ""3. Digital Beamforming for Synthetic Aperture Radar"" ""3.1 SAR radarmain parameters"" ""3.2 SISO SAR"" ""3.2.1 Stripmap SAR"" ""3.2.2 ScanSAR"" ""3.2.3 Spotlight SAR"" ""3.2.4 Discrete time modelling"" ""3.3 SIMO SAR"" ""3.3.1 SIMO SAR system mathematical modelling"" ""3.3.2 Discrete time modelling"" ""3.4 Beamforming in the elevation and cross-range direction using SIMO SAR"" ""3.4.1 SIMO SAR parameter design""""3.4.2 Beamforming in the elevation direction"" ""3.4.3 Beamforming in the cross-range direction"" ""3.4.3.1 SIMO SAR examples"" ""3.5 Target parameter estimation using SIMO SAR"" ""3.5.1 Round trip delay estimation"" ""3.5.2 Joint direction of arrival and slant range estimation"" ""3.5.3 Joint direction of arrival and power estimation"" ""3.6 Summary and conclusions"" ""References"" ""4. Arrayed MIMO Radar: Multi-target Parameter Estimation for Beamforming"" ""4.1 Introduction"" ""4.2 Arrayed MIMO radar received signal model"" ""4.3 Space arrayed MIMO radar: Target echoes arriving with equal delays""""4.3.1 Least squares"" ""4.3.2 Caponâ€?s method"" ""4.3.3 Amplitude and phase estimation (APES)"" ""4.3.4 Discussion"" ""4.3.5 Comparative studies and computer simulation results"" ""4.3.5.1 Finite averaging effects"" ""4.3.5.2 Noise effects (variable levels of PTÏ?2n)"" ""4.4 Arrayed MIMO radar: Target echoes with different delays"" ""4.4.1 Spatiotemporal arrayed MIMO radar: Doppler, delay, DOA and path gains estimation"" ""4.4.1.1 Subspace partitioning and delay estimation"" "Beamforming: Sensor Signal Processing for Defence Applications deals with adaptive sensor array processing, and, in particular, superresolution beamformers and their applications to radar. In this book, both narrowband and wideband beamformers will be presented as well as space-only and spatiotemporal beamformers, which may operate in the presence of clutters and jammers. Furthermore, transmitter (Tx), receiver (Rx) and both Tx/Rx (MIMO) beamformers will be considered and their role in radar and sonar designs will be discussed. Design, integration and auto-calibration approaches incorporating off-the-shelf components will also be investigated. This book is self-contained and comprehensively covers some of the classic and fundamental models of beamforming for sensor signal processing. It is suitable as an advanced textbook for graduate students and researchers in the area of signal processing, as well as a reference book for engineers in the defence industry."-- Provided by publisher