Contents. List of Contributors. Section A Suppression of clutter in moving radar 1. Part I Space-slow time processing for airborne MTI radar 3

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1 Preface Glossary List of Contributors xix xxi xxxi Section A Suppression of clutter in moving radar 1 Part I Space-slow time processing for airborne MTI radar 3 1 Space time adaptive processing for manoeuvring airborne radar 5 Peter G. Richardson 1.1 Introduction STAP fundamentals Clutter angle-doppler relationships Straight and level flight Effect of variations in platform orientation Clutter suppression in forward-looking radar Mainlobe clutter suppression Sidelobe clutter suppression Slow moving target detection under conditions of manoeuvre Effects of platform manoeuvre Motion compensation Jammer rejection under conditions of manoeuvre Mainlobe clutter filtering requirements Advantages of using STAP Summary 33 Klem: fm 2004/7/5 page v #5

2 vi Contents 2 Non-linear and adaptive two-dimensional FIR filters for STAP: theory and experimental results 37 Pierfrancesco Lombardo and Fabiola Colone 2.1 Introduction Adaptive linear filters AR-based FIR filters Non-linear combination of non-adaptive filters Filter bank design Detection threshold and performance AR-based non-linear detector Non-linear combination of adaptive AR-based two-dimensional FIR filters Conclusions Acknowledgments Appendix: ML estimation of two-dimensional AR parameters 69 3 Space time techniques for SAR 73 Alfonso Farina and Pierfrancesco Lombardo 3.1 Summary Description of the problem and state of the art Model of MSAR echoes Aberrations due to target motion Space time frequency representation Processing schemes Taxonomy of processing schemes for MSAR MTI + PD DPCA Along-track interferometry (ATI)-SAR Processor in the space time frequency domain Optimum processing for MSAR Conclusions Acknowledgments STAP: an efficient, affordable approach for clutter suppression 123 Hong Wang, Richard A. Schneible, Russell D. Brown and Yuhong Zhang 4.1 Definition of the difference ( ) beams STAP algorithms Analytical performance formulas of -STAP SINR potential Probabilities of detection and false alarm A real-data demonstration of -STAP 131 Klem: fm 2004/7/5 page vi #6

3 vii 4.5 Desired -beam characteristics Mathematical equivalence of subarray and -STAP Summary Advantages of the -STAP approach Limitations of -STAP Potential applications of -STAP STAP with omnidirectional antenna arrays 149 Richard Klemm 5.1 Introduction Preliminaries on STAP antennas The circular ring array concept Array configurations for 360 coverage Four linear arrays Displaced circular rings Circular planar array with randomly distributed elements Octagonal planar array Discussion Directivity patterns Range-ambiguous clutter Effect of array tilt Side-looking linear and rectangular arrays Omnidirectional arrays Conclusions 169 Part II Space-slow time processing for space-based MTI radar SAR-GMTI concept for RADARSAT Christoph H. Gierull and Chuck Livingstone 6.1 Introduction Background Addition of MTI modes to spaceborne SAR RADARSAT-2 moving object detection experiment Analysis of SAR-GMTI modes for RADARSAT Background Statistical models of measured signals SCNR optimum processing SAR displaced phase centre antenna SAR along-track interferometry SAR-STAP scheme for RADARSAT Detection Parameter estimation 201 Klem: fm 2004/7/5 page vii #7

4 viii Contents 6.4 Conclusions List of symbols STAP simulation and processing for spaceborne radar 207 Tim J. Nohara and Peter Weber 7.1 Introduction Spaceborne radar applications and design Spaceborne MTI radar applications Spaceborne MTI radar design STAP processing for SBR Typical GMTI signal processing Extension to other modes Other issues Simulation and processing for SBR User interface Model the radar Model the environment Generate the signals Model the processing Evaluate the results Discussion and conclusions Techniques for range-ambiguous clutter mitigation in space-based radar systems 235 Stephen M. Kogon and Michael Zatman 8.1 Introduction Moving target detection with SBR STAP for SBR systems Clutter characteristics of pulse-doppler waveforms in SBR Clutter Doppler ambiguities Clutter range ambiguities Impact of range-ambiguous clutter on STAP performance Range-ambiguous clutter mitigation techniques with pulse-doppler waveforms PRF diversity Aperture trade offs Long single pulse phase-encoded waveforms Properties of long single pulse phase-encoded waveform (LSPW) Integrated sidelobe clutter levels STAP simulations Summary 260 Klem: fm 2004/7/5 page viii #8

5 ix Part III Processing architectures Parallel processing architectures for STAP 265 Alfonso Farina and Luca Timmoneri 9.1 Summary and introduction Baseline systolic algorithm Lattice and vectorial lattice algorithms Inverse QRD-based algorithms Experiments with general purpose parallel processors Experiments with VLSI-based CORDIC board Modern signal processing technology overview and its impact on real-time STAP Processing of recorded live data Systolic algorithm for live data processing Data files used in the data reduction experiments Performance evaluation Detection of vehicular traffic Concluding remarks Appendix A: Givens rotations and systolic implementation of sidelobe canceller Appendix B: lattice working principle Appendix C: the CORDIC algorithm Appendix D: the SLC implementation via CORDIC algorithm Appendix E: an example of existing processors for STAP 293 Part IV Clutter inhomogeneities STAP in heterogeneous clutter environments 305 William L. Melvin 10.1 Introduction Adaptivity with finite sample support STAP performance metrics Covariance matrix errors Classes of space time clutter heterogeneity General simulation characteristics Amplitude heterogeneity Clutter discretes Range-angle varying clutter RCS Clutter edges Spectral heterogeneity CNR-induced spectral mismatch Targets in the secondary data Joint angle-doppler mismatch and clutter heterogeneity 337 Klem: fm 2004/7/5 page ix #9

6 x Contents 10.8 Site-specific examples of clutter heterogeneity Measured multichannel airborne radar data Site-specific simulation STAP techniques in heterogeneous environments Data-dependent training techniques Minimal sample support STAP Clutter discretes Targets in training data Covariance matrix tapers Knowledge-aided space time processing Summary Acknowledgments Adaptive weight training for post-doppler STAP algorithms in non-homogeneous clutter 359 Stephen M. Kogon 11.1 Introduction Training of STAP algorithms Post-Doppler STAP algorithms Phase and power-selected training for STAP Experimental results Example of phase/power selection STAP results Experimental versus theoretical STAP performance Summary Application of deterministic techniques to STAP 375 Jeffrey T. Carlo, Tapan K. Sarkar, Michael C. Wicks and Magdalena Salazar-Palma 12.1 Introduction Direct data domain least-squares (D 3 LS) approach, one dimension D 3 LS approach with main beam constraints A D 3 LS approach with main beam constraints for space time adaptive processing Space time D 3 LS eigenvalue processor Space time D 3 LS forward processor Space time D 3 LS backward processor Space time D 3 LS forward backward processor Determining the degrees of freedom An airborne radar example Simulation setup Case I: single constraint space time example Case II: multiple constraint space time example 403 Klem: fm 2004/7/5 page x #10

7 xi 12.7 Conclusions List of variables Robust techniques in space time adaptive processing 413 Keith F. McDonald and Rick S. Blum 13.1 Introduction Initial development of space time adaptive processing (STAP) algorithms Hypothesis testing problem Real-world detection environments Non-homogeneity causes and impact on performance Signal contamination Non-homogeneity detection Knowledge-based signal processing Analysis of degraded performance due to non-homogeneity Antenna array errors Deviation from Gaussian assumption Jamming and terrain scattered interference Constraining detection schemes Two-stage processors Three-dimensional STAP Reduction in computational complexity Reduced-rank methods and covariance matrix tapers Techniques implementing limited reference cells Low complexity approaches to STAP Conclusions 443 Section B Miscellaneous space time processing applications 463 Part V Ground target tracking with STAP radar Ground target tracking with STAP radar: the sensor 467 Richard Klemm 14.1 Introduction Properties of the STAP radar sensor Processing techniques Array properties Summary of the data output provided by the STAP radar The scenario SNIR and P d of a moving target System aspects 480 Klem: fm 2004/7/5 page xi #11

8 xii Contents 14.4 Degrading effects Bandwidth effects Doppler ambiguities Range ambiguities STAP radar under jamming conditions Issues in convoy tracking Convoy detection by range-only information Convoy detection by azimuth variance analysis Summary Ground target tracking with STAP radar: selected tracking aspects 501 Wolfgang Koch 15.1 Introduction Discussion of an idealised scenario Summary of observations Tracking preliminaries Coordinate systems Target dynamics model GMTI sensor model GMTI characteristics Convoy resolution Doppler ambiguities Measurements GMTI data processing Prediction Data processing Filtering process Realisation aspects Discussion Retrodiction Effect of Doppler ambiguities Road map information Modelling of roads Densities on roads Quantitative discussion Simulation parameters Numerical results List of variables 537 Klem: fm 2004/7/5 page xii #12

9 xiii Part VI Space-fast time techniques Superresolution and jammer suppression with broadband arrays for multifunction radar 543 Ulrich Nickel 16.1 Introduction Broadband array signal model and beamforming Received signal and notation Digital beamforming with subarray outputs Influence of channel imperfections Superresolution with broadband arrays Spatial-only processing of broadband data Space and time processing methods Conclusions on broadband superresolution Jammer suppression with broadband arrays General principles of adaptive interference suppression Spatial-only adaptation Space and time adaptation Final remarks 595 Part VII Over-the-horizon radar applications Stochastically constrained spatial and spatio temporal adaptive processing for non-stationary hot clutter cancellation 603 Yuri I. Abramovich, Stuart J. Anderson, Alexei Y. Gorokhov and Nicholas K. Spencer 17.1 Overview SC STAP fundamentals and supervised training applications SC STAP algorithm: analytic solution SC STAP algorithm: operational routines SC STAP algorithm: efficiency analysis by simulation results SC STAP algorithm: efficiency analysis by real data processing Summary SC STAP unsupervised training applications Operational routine for unsupervised training Operational SC STAP algorithm: simulation and real data processing results Summary 664 Klem: fm 2004/7/5 page xiii #13

10 xiv Contents 17.4 SC STAP convergence analysis Introduction Conditional loss factor η 1 analysis: LSMI versus SMI for SC SAP Conditional loss factor η 1 analysis: LSMI for SC STAP Conditional loss factor η 2 analysis: exact PDF for a single stochastic constraint Conditional loss factor η 2 analysis: approximate PDF for multiple stochastic constraints List of variables 690 Part VIII Applications in acoustics and seismics Space time adaptive matched field processing (STAMP) 701 Yung P. Lee 18.1 Introduction Adaptive matched field processing (MFP) Wideband narrowband feedback loop white-noise-constrained method (FLWNC) MFP examples Space time adaptive matched field processing (STAMP) Forward sector processing simulation geometry Summary Space time signal processing for surface ship towed active sonar 715 Dirk Maiwald, Stephan Benen and Helmut Schmidt-Schierhorn 19.1 Introduction Narrowband multiple ping processing Data model Fully adaptive CW processing Partially adaptive processing techniques FM processing Image processing background Echogram image enhancement Automatic echogram detection Experimental results Sonar system description CW pulse sea data analysis Echogram sea data analysis (ACTAS) Echogram enhancement Automatic echogram detection 730 Klem: fm 2004/7/5 page xiv #14

11 xv 20 EM and SAGE algorithms for towed array data 733 Pei Jung Chung and Johann F. Böhme 20.1 Introduction Signal model EM and SAGE algorithms EM algorithm SAGE algorithm Fast EM and SAGE algorithms Recursive EM and SAGE algorithms Recursive EM algorithm Recursive SAGE algorithm Experimental results EM and SAGE algorithms Recursive EM and SAGE algorithms Conclusions The common reflection surface (CRS) stack a data-driven space time adaptive seismic reflection imaging procedure 755 Jürgen Mann, Eric Duveneck, Steffen Bergler and Peter Hubral 21.1 Introduction Seismic reflection imaging The seismic wavefield Acquisition of reflection seismic data Seismic reflection processing Common reflection surface stack Classic data-driven approaches Second-order traveltime approximations Physical interpretation of the coefficients Implementation Practical aspects A synthetic data example CRS attributes and velocity model estimation Conclusions Glossary List of variables Specific terminology 779 Part IX Space time techniques in communications STAP for space/code/time division multiple access systems 785 Christoph M. Walke 22.1 Introduction System model 789 Klem: fm 2004/7/5 page xv #15

12 xvi Contents 22.3 Time domain linear joint detection Zero forcing block linear equalisation Minimum mean square error block linear equalisation Frequency-domain linear joint detection Block-diagonal FD system model FD ZF-BLE and MMSE-BLE Performance of FD joint detection Exploitation of spatial and frequency diversity Intracell interference cancellation Intra- and intercell interference cancellation Conclusions List of variables Variables with roman/calligraphic letters Variables with calligraphic letters Variables with greek letters Underwater communication with vertical receiver arrays 827 Johann F. Böhme and Rolf Weber 23.1 Introduction The underwater acoustic channel Transmission loss and ambient noise Sound speed variability Multipath propagation Doppler effect Summary Underwater acoustic communications a brief overview Incoherent digital receivers Coherent digital receivers Spatial temporal receiver architecture Communication over channels with ISI Multichannel digital receiver Signal model Multichannel equalisation Multichannel constant modulus algorithm Blind stochastic gradient descent algorithms The constant modulus algorithm Experimental results Super-exponential blind equalisation Iterative Shalvi Weinstein algorithm Recursive Shalvi Weinstein algorithm Adaptive implementation Experimental results Concluding remarks 853 Klem: fm 2004/7/6 page xvi #16

13 xvii 24 Reduced-rank interference suppression and equalisation for GPS and downlink CDMA 857 Wilbur L. Myrick and Michael D. Zoltowski 24.1 Reduced-rank interference suppression and equalisation Motivation for reduced-rank MMSE processing Understanding the multistage Wiener filter Lattice structure of the MSWF MSWF related to Wiener Hopf filter weights Application of MSWF to CDMA downlink Introduction Data and channel model Edge of cell/soft hand-off Chip-level MMSE estimator Performance examples Application of MSWF to GPS jammer suppression Introduction Power minimisation and joint space time preprocessing Space time filter characteristics Data and channel model Dimensionality reduction techniques Performance examples Summary of concepts involving reduced-rank filtering Introduction to space time coding 883 Sumeet Sandhu, Dhananjay Gore, Rohit Nabar and Arogyaswami Paulraj 25.1 Introduction Multiple antenna channel model Benefits of smart antenna technology Array gain Diversity gain Multiplexing gain Interference reduction Background on space time codes Space time trellis codes Linear space time block codes New design criteria Error performance Capacity performance Unified design Receiver design Modulation and coding for MIMO Concluding remarks 906 Index 909 Klem: fm 2004/7/6 page xvii #17