Cognitive Radar: The Knowledge-aided Fully Adaptive Approach

181 Pages·2010·2.23 MB·English

by  J. R. Guerci

Cognitive Radar
The Knowledge-Aided Fully Adaptive Approach
For a complete listing of theArtechHouse Radar Library,
turn to the back of this book.
Cognitive Radar
The Knowledge-Aided Fully Adaptive Approach
Joseph R. Guerci
artechhouse.com
Library of Congress Cataloging-in-Publication Data
British Library Cataloguing in Publication Data
ISBN: 978-1-59693-364-4
Cover design by Vicki Kane
© 2010ARTECHHOUSE, INC.
685 Canton Street
Norwood, MA 02062
All rights reserved. Printed and bound in the United States of America. No
part of this book may be reproduced or utilized in any form or by any means,
electronic or mechanical, including photocopying, recording, or by any infor-
mation storage and retrieval system, without permission in writing from the
publisher.
All terms mentioned in this book that are known to be trademarks or serv-
ice marks have been appropriately capitalized.ArtechHouse cannot attest to
the accuracy of this information. Use of a term in this book should not be
regarded as affecting the validity of any trademark or service mark.
10 9 8 7 6 5 4 3 2 1
Contents
Preface 9
Chapter 1
Introduction 13
1.1 Why “Cognitive” Radar? 13
1.2 Functional Elements and Characteristics of a Cognitive
Radar Architecture 14
1.2.1 Adaptive Transmit Capability 17
1.2.2 Knowledge-Aided Processing 23
1.3 Book Organization 30
References 31
Chapter 2
Optimum Multi-Input Multioutput (MIMO) Radar 35
2.1 Introduction 35
2.2 Jointly Optimizing the Transmit and Receive Functions
Case I: MaximizingSINR 36
Example 2.1 Multipath Interference 42
2.3 Jointly Optimizing the Transmit and Receive Functions
Case II: Maximizing Signal-to-Clutter 47
Example 2.2 SidelobeTarget Suppression: “Sidelobe
Nullingon Transmit” 49
Example 2.3 Optimal Pulse Shape for Maximizing SCR 51
5
6 CognitiveRadar:TheKnowledge-AidedFullyAdaptiveApproach
Example 2.4 Optimum Space-TimeMIMOProcessing for
Clutter Suppression in AirborneMTIRadar 54
2.4 OptimumMIMOTarget Identification 62
Example 2.5 Two-Target Identification Example 64
Example 2.6 MultitargetIdentification Example 69
2.5 Constrained OptimumMIMORadar 69
Example 2.7 Prenullingon Transmit 71
Example 2.8 Relaxed Projection Example 74
Example 2.9 Nonlinear FM (NLFM) to Achieve Constant
Modulus 77
Example 2.10 Matched Subspace Example 84
Appendix 2.A: Infinite Duration (Steady State) Case 86
References 87
Chapter 3
Adaptive Multi-Input Multioutput (MIMO) Radar 89
3.1 Introduction 89
3.2 Transmit-Independent Channel Estimation 90
Example 3.1 Adaptive Multipath Interference Mitigation 91
3.3 DynamicMIMOCalibration 93
Example 3.2MIMOCohere-on-Target 93
3.4 Transmit-Dependent Channel Estimation 96
Example 3.3 STAP-on-Transmit(STAP-Tx) Example 97
Example 3.4 DDMAMIMOSTAPClutter Mitigation
Example forGMTIRadar 102
3.5 Theoretical Performance Bounds of theDDMAMIMO
STAPApproach 104
References 110
Chapter 4
Introduction to Knowledge-Aided (KA) Adaptive
Radar 113
4.1 The Need for KA Radar 113
Contents 7
4.2 Introduction to KA Radar: Back to “Bayes-ics” 118
4.2.1 Indirect KA Radar: Intelligent Training and
Filter Selection 121
Example 4.1 Intelligent Filter Selection: Matching
the AdaptiveDoFs(ADoFs) to the Available
Training Data 123
4.2.2 Direct KA Radar: Bayesian Filtering and Data
Prewhitening 127
Example 4.2 Using Past Observations as a
Prior Knowledge Source 131
4.3 Real-Time KA Radar: The DARPAKASSPERProject 135
4.3.1 Solution: Look-Ahead Scheduling 137
Example 4.3 Balancing Throughput in aKASSPER
HPECArchitecture 141
4.3.2 Examples of a KA Architectures Developed by
the DARPA/AFRLKASSPERProject 144
4.4 KA Radar Epilogue 153
References 154
Chapter 5
Putting It All Together 159
5.1 Cognitive Radar: The Fully Adaptive Knowledge-Aided
Approach 159
Example 5.1 A Cognitive Radar Architecture 160
5.1.1 Informal Operational Narrative for aGMTIRadar 162
5.2 Areas for Future Research and Development 164
References 165
About the Author 167
Index 169

Preface
CognitiveRadar:TheKnowledge-AidedFullyAdaptiveApproachbrings
togetherforthefirsttimeinbookformmajornewdevelopmentsin
optimal and adaptive multi-input, multioutput (MIMO) radar and
knowledge-aided (KA) processing yielding an entirely new
dynamic radar architecture that possessesunprecedented capabili-
tiesforadaptationinchallengingreal-worldenvironments.Despite
the cutting edge nature of the material, the book is written in an
accessible style consistent with the author’s previously
well-received Space-Time Adaptive Processing for Radar (Artech
House), and includes many illustrative examples that help the
reader with a number of diverse applications from optimizing
detection of weak targets in complex interference backgrounds, to
target identification.
Thematerialinthisbookwasalongtimeinthemaking—andas
such there are many to acknowledge. Origins include the author’s
own early research in “matched illumination” for enhanced detec-
tion and identification dating back to the late 80’s and early 90’s,
as well as earlier work by Gjessing and Farina (see Chapter 2
for further details and references), along with over 20 years of
development of knowledge-based and expert systems research
pioneered by researchers at the Air Force Research Laboratory
(AFRL) and latter at the Defense Advanced Research Projects
Agency(DARPA),wheretheauthor ledmajor newradardevelop-
ments (see Chapter 4).
9