BEAMFORMING Sensor Signal Processing for Defence Applications

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1 BEAMFORMING Sensor Signal Processing for Defence Applications

2 Communications and Signal Processing Editors: Prof. A. Manikas & Prof. A. G. Constantinides (Imperial College London, UK) Vol. 1: Vol. 2: Vol. 3: Vol. 4: Vol. 5: Joint Source-Channel Coding of Discrete-Time Signals with Continuous Amplitudes by Norbert Goertz Quasi-Orthogonal Space-Time Block Code by Chau Yuen, Yong Liang Guan and Tjeng Thiang Tjhung Multi-Carrier Techniques for Broadband Wireless Communications: A Signal Processing Perspective by C-C Jay Kuo, Michele Morelli and Man-On Pun Audio Visual Person Tracking: A Practical Approach by Fotios Talantzis and Anthony G Constantinides Beamforming: Sensor Signal Processing for Defence Applications by Thanassis Manikas

3 Communications and Signal Processing Vol. 5 BEAMFORMING Sensor Signal Processing for Defence Applications editor Athanassios Manikas Imperial College London, UK ICP Imperial College Press

4 Published by Imperial College Press 57 Shelton Street Covent Garden London WC2H 9HE Distributed by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore USA office: 27 Warren Street, Suite , Hackensack, NJ UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE Library of Congress Cataloging-in-Publication Data Manikas, Athanassios. Beamforming : sensor signal processing for defence applications / Thanassis Manikas, Imperial College London, UK. pages cm. -- (Communications and signal processing ; volume 5) Includes bibliographical references and index. ISBN (hardcover : alk. paper) 1. Radar transmitters. 2. Antenna radiation patterns. 3. Beam optics. 4. Radar--Military applications. I. Title. TK6587.M '348--dc British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Copyright 2015 by Imperial College Press All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. Typeset by Stallion Press enquiries@stallionpress.com Printed in Singapore

5 To Professor Laurence Frank Turner (Emeritus)

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7 Preface In recognition of the strategic importance of sensor signal processing for the UK Ministry of Defence (MOD), the University Defence Research Centre (UDRC) in Signal Processing was established in 2009 as a joint venture between the MOD and the Engineering and Physical Science Research Council (EPSRC). The UDRC Phase-1 ran until 2013 and incorporated 12 major UK universities led by Imperial College London. It has grown into a dynamic research centre and forum, enabling the cross-fertilisation of ideas and fostering a wider community of practice in signal processing. This book presents a collection of research contributions from the UDRC Phase-1 that address a number of topics broadly concerned with beamforming which is fundamental to many civilian applications but also to the capabilitities of many defence systems. It is composed of eight chapters, the first five of which are concerned with various radar research problems and applications. In particular: Chapter 1 considers the recent work and advances in the area of space-time beamforming algorithms and their application to radar systems. Furthermore, it describes the most successful space-time adaptive processing (STAP) beamforming algorithms that exploit lowrank and sparsity properties as well as the use of prior knowledge to improve the performance of STAP algorithms in radar systems. Chapter 2 is concerned with look-down airborne radars and the employment of STAP beamforming. The focus of this chapter is on the non-homogeneity of STAP training data caused by the forward-looking radar platform as well as on robust beamforming. In Chapters 3 and 4 synthetic aperture radar (SAR) and multi-input multi-output (MIMO) vii

8 viii Beamforming: Sensor Signal Processing for Defence Applications radar are respectively investigated, while in Chapter 5 different types of ship wake waves and a number of common wake wave detection algorithms for two-dimensional SAR imagery are studied. Chapter 6 is related to ocean-towed arrays which find applications in a variety of areas such as defence, oil and gas exploration and geological and marine life studies. Here the major challenge is dealing with receiver positional uncertainties resulting from the array s flexible structure in combination with the ship s turning manoeuvers or water currents. Finally, the last two chapters are about handling array uncertainties with Chapter 7 dealing with geometrical and electrical uncertainties and Chapter 8 considering pointing error uncertaities and robustification issues. Thanassis Manikas London 2015 UDRC Technical Lead ( ) a.manikas@imperial.ac.uk

9 Acknowledgments I would like to thank all authors of the chapters for their contribution to this special book. I also wish to express my gratitude to Thibaud Gabillard, Zexi Fang and He Ren for reading various parts of the manuscript. I am grateful to all UDRC colleagues from the Defence Science and Technology Laboratory (Dstl), especially Paul Thomas (Sensors & Countermeasures) and Bob Elsley (ISR Sensing & Processing) for their support and excellent collaboration in various aspects of the UDRC. Furthermore, I would also like to thank Nick Goddard (Naval Systems) for providing the real data set which was collected from a passive towed array during a trial in the Southwestern Approaches to the UK. At Imperial College Press, I would like to thank my editor Thomas Stottor for his help and for showing a remarkable amount of patience with my slipping deadlines. ix

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11 Contents Preface Acknowledgments List of Notations 1. Space-Time Adaptive Beamforming Algorithms for Airborne Radar Systems 1 Rodrigo de Lamare vii ix xvii 1.1 Introduction Pulsed Doppler radar: System and signal models Conventional beamforming Low-rank beamforming algorithms Eigenvalue-decomposition-based algorithms Krylov subspace-based algorithms Joint iterative optimization (JIO)-based algorithms Joint interpolation, decimation and filtering (JIDF)-based algorithms Sparsity-aware beamforming algorithms Knowledge-aided beamforming algorithms Simulations Concluding remarks References xi

12 xii Beamforming: Sensor Signal Processing for Defence Applications 2. Transmit Beamforming for Forward-Looking Space-Time Radars 29 Mathini Sellathurai and David Wilcox 2.1 Introduction Principles of STAP Array response vectors Scatterer response Clutter Optimum STAP receiver processing Side-looking radar Forward-looking radar Adaptive transmit diversity STAP Signalmodel Space-time illumination patterns D Doppler compensation Ambiguous range transmit nulling Angular location of ambiguous ranges Beampatterndesign Simulation results Summary References Digital Beamforming for Synthetic Aperture Radar 63 Karen Mak and Athanassios Manikas 3.1 SAR radar main parameters SISO SAR Stripmap SAR ScanSAR Spotlight SAR Discrete time modelling SIMO SAR SIMO SAR system mathematical modelling Discrete time modelling Beamforming in the elevation and cross-range direction using SIMO SAR SIMO SAR parameter design

13 Contents xiii Beamforming in the elevation direction Beamforming in the cross-range direction SIMO SAR examples Target parameter estimation using SIMO SAR Round trip delay estimation Joint direction of arrival and slant range estimation Joint direction of arrival and power estimation Summary and conclusions References Arrayed MIMO Radar: Multi-target Parameter Estimation for Beamforming 119 Harry Commin, Kai Luo and Athanassios Manikas 4.1 Introduction Arrayed MIMO radar received signal model Space arrayed MIMO radar: Target echoes arriving with equal delays Least squares Capon s method Amplitude and phase estimation (APES) Discussion Comparative studies and computer simulation results Finite averaging effects Noise effects (variable levels of PT σ ) n Arrayed MIMO radar: Target echoes with different delays Spatiotemporal arrayed MIMO radar: Doppler, delay, DOA and path gains estimation Subspace partitioning and delay estimation Joint DOA-Doppler estimation Complex fading coefficients estimation Algorithm summary spatiotemporal arrayed MIMO

14 xiv Beamforming: Sensor Signal Processing for Defence Applications Iterative adaptive approach (IAA) Simulation studies Simulated environment 1: Stationary targets Simulated environment 2: Moving targets Complexity analysis Conclusions A Appendix: Equivalent two-stage estimation References Beamforming for Wake Wave Detection and Estimation An Overview 159 Karen Mak and Athanassios Manikas 5.1 Introduction Types of ship wake waves Ship-generated surface wakes Turbulent wakes Ship-generated internal wake waves Environmental conditions and SAR parameters for wake wave imaging Detection approaches for wake waves Pre-processing stage Transform stage Post-processing Estimation of parameters from ship wake waves Parameter estimation from Kelvin envelope Parameter estimation from stern waves Parameter estimation from turbulent wake SAR for ocean applications Interferometric SAR SAR interferometry configurations for ocean applications Summary and conclusions References

15 Contents xv 6. Towed Arrays: Channel Estimation, Tracking and Beamforming 189 Vidhya Sridhar, Marc Willerton and Athanassios Manikas 6.1 Introductory concepts and classification Family of instrument-based calibration techniques Family of data-based calibration techniques Pilot calibration Self-calibration Auto-calibration Robustification against uncertainties Towed array signal model Synthetic data generation and BellHop framework Subspace pilot calibration techniques Robustification techniques: The H state space model Experimental evaluation of techniques and discussion Experiments with subspace pilot calibration Experiments with H -based robustification technique Experimental results using synthetic towedarraydata Experimental results using real towed array data from sea trials Conclusions References Array Uncertainties and Auto-calibration 221 Marc Willerton, Evangelos Venieris and Athanassios Manikas 7.1 Introduction Signal model Array manifold vector Changing the array reference point Geometric case Approximate case Array auto-calibration Measurement phase Array shape estimation phase Complex gain estimation phase

16 xvi Beamforming: Sensor Signal Processing for Defence Applications 7.4 Performance evaluation Small aperture array Large aperture array A representative example of the effects of uncertainties on a large aperture array Summary and conclusions References Robust Beamforming to Pointing Errors 263 Jie Zhuang and Athanassios Manikas 8.1 Introduction Estimation of the linear combination vector using signal subspace Estimation of the desired signal manifold via vector space projections (VSP) Desired signal power estimation Interference cancellation beamformer Performance analysis in the presence of pointing errors Simulation results Summary and conclusions References Index 287

17 List of Notations A, a Scalar A,a Column vector A Matrix ( ) T Transpose ( ) H Hermitian transpose ( ) Conjugate A F Frobenius norm of matrix A A Euclidean norm of vector A A Absolute value Hadamard product Hadamard division Kronecker product E{ } Expectation A # Pseudoinverse of the matrix A A b Element by element power exp(a) Element by element exponential of vector A trace(a) Sum of the diagonal elements of matrix A row i {A} column vector with elements the i-th row of A diag(a) Column vector with elements the diagonal elements of matrix A diag(a) The diagonal matrix whose diagonal elements are the elements of a ln(a) Natural logarithm of a log 10 (a) Logarith of a relative to base 10 max (ξ (x)) x Maximum value of ξ (x) overallx min (ξ (x)) x Minimum value of ξ (x) overallx xvii

18 xviii Beamforming: Sensor Signal Processing for Defence Applications P A Projection operator on the subspace spanned by the columns of A P A Projection operator on the complement subspace of the subspace spanned by the columns of A 0 N N-element column vector of all zeros 1 N N-element column vector of all ones I N Identity matrix of size N N O M N Matrix of zeros of size M N Re {x} Real part of x O (M) Order of M R M N Set of real matrices of M rows and N columns C M N Set of complex matrices of M rows and N columns B Set of binary numbers N Set of natural numbers Belongs to (or element of) For every Perpendicular Is equal by definition to Angle, phase

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