Compressed Sensing in Radar Signal Processing

396 Pages·2019·15.738 MB·English

by  Antonio De Maio (editor), Yonina C. Eldar (editor), Alexander M. Haimovich (editor)

CompressedSensinginRadarSignalProcessing
Learnaboutthemostrecenttheoreticalandpracticaladvancesinradarsignalprocessing
using tools and techniques from compressive sensing. Providing a broad perspective
that fully demonstrates the impact of these tools, the accessible and tutorial-like
chapterscovertopicssuchasclutterrejection,CFARdetection,adaptivebeamforming,
randomarraysforradar,space–timeadaptiveprocessing,andMIMOradar.Eachchapter
includescoverageoftheoreticalprinciples,adetailedreviewofcurrentknowledge,and
discussion of key applications, and also highlights the potential benefits of using
compressed sensing algorithms. A unified notation and numerous cross-references
between chapters make it easy to explore different topics side by side. Written by
leading experts from both academia and industry, this is the ideal text for researchers,
graduatestudents,andindustryprofessionalsworkinginsignalprocessingandradar.
Antonio De Maio is a professor in the Department of Electrical Engineering and
Information Technology at the University of Naples Federico II, and a Fellow of
theIEEE.
Yonina C.Eldar is a professor at the Weizmann Institute of Science. She has authored
and edited several books, including Sampling Theory: Beyond Bandlimited Systems
andCompressedSensing:TheoryandApplications(CambridgeUniversityPress,2015;
2012).SheisaFellowoftheIEEEandEURASIP,andamemberoftheIsraelNational
AcademyofScienceandHumanities.
Alexander M. Haimovich is a distinguished professor in the Department of Electrical
andComputerEngineeringattheNewJerseyInstituteofTechnology,andaFellowof
theIEEE.

Compressed Sensing in Radar
Signal Processing
Editedby
ANTONIO DE MAIO
UniversityofNaplesFedericoII
YONINA C. ELDAR
WeizmannInstituteofScience
ALEXANDER M. HAIMOVICH
NewJerseyInstituteofTechnology
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www.cambridge.org
Informationonthistitle:www.cambridge.org/9781108428293
DOI:10.1017/9781108552653
©CambridgeUniversityPress2020
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Firstpublished2020
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LibraryofCongressCataloging-in-PublicationData
Names:DeMaio,Antonio,1974–editor.|Eldar,YoninaC.,editor.|
Haimovich,AlexanderM.,1954–editor.
Title:Compressedsensinginradarsignalprocessing/editedbyAntonioDeMaio,
UniversityofNaplesFedericoII,YoninaC.Eldar,WeizmannInstituteofScience,
AlexanderM.Haimovich,NewJerseyInstituteofTechnology.
Description:Firstedition.|Cambridge,UnitedKingdom;NewYork,NY:
CambridgeUniversityPress,[2020]|Includesbibliographicalreferencesandindex.
Identifiers:LCCN2019014859|ISBN9781108428293(hardback)
Subjects:LCSH:Radar.|Compressedsensing(Telecommunication)
Classification:LCCTK6580.C662020|DDC621.3848/3–dc23
LCrecordavailableathttps://lccn.loc.gov/2019014859
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onradar–AH

Contents
ListofContributors pagexi
Introduction xiv
ListofSymbols xx
1 Sub-NyquistRadar:PrinciplesandPrototypes 1
KumarVijayMishraandYoninaC.Eldar
1.1 Introduction 1
1.2 PriorArtandHistoricalNotes 3
1.3 TemporalSub-NyquistRadar 5
1.4 DopplerSub-NyquistRadar 15
1.5 CognitiveSub-NyquistRadarandSpectralCoexistence 18
1.6 SpatialSub-Nyquist:ApplicationtoMIMORadar 29
1.7 Sub-NyquistSAR 39
1.8 Summary 43
References 44
2 ClutterRejectionandAdaptiveFilteringinCompressedSensingRadar 49
PeterB.Tuuk
2.1 Introduction 49
2.2 ProblemFormulation 50
2.3 InterferenceSources 53
2.4 SignalProcessingTreatmentofClutter 55
2.5 MeasurementCompression 58
2.6 EstimatingInterferenceStatisticsfromCompressedMeasurements 59
2.7 MitigatingClutterinCompressedSensingEstimation 66
2.8 Summary 68
References 69
3 RFIMitigationBasedonCompressiveSensingMethodsforUWBRadarImaging 72
TianyiZhang,JiayingRen,JianLi,DavidJ.Greene,JeremyA.Johnston,andLamH.Nguyen
3.1 Introduction 72
3.2 RPCAforRFIMitigation 75
3.3 CLEAN-BICforRFIMitigation 82
vii
viii Contents
3.4 EnhancedAlgorithmsforRFIMitigation 91
3.5 PerformanceEvaluations 92
3.6 Conclusions 101
3.7 Acknowledgment 102
References 102
4 CompressedCFARTechniques 105
LauraAnitoriandArianMaleki
4.1 Introduction 105
4.2 RadarSignalModel 105
4.3 ClassicalRadarDetection 106
4.4 CSRadarDetection 110
4.5 ComplexApproximateMessagePassing(CAMP)Algorithm 112
4.6 TargetDetectionUsingCAMP 115
4.7 AdaptiveCAMPAlgorithm 118
4.8 SimulationResults 120
4.9 ExperimentalResults 127
4.10 Conclusions 131
References 132
5 Sparsity-BasedMethodsforCFARTargetDetectioninSTAPRandomArrays 135
HaleyH.KimandAlexanderM.Haimovich
5.1 Introduction 135
5.2 STAPRadarConcepts 137
5.3 STAPDetectionProblem 145
5.4 CompressiveSensingCFARDetection 148
5.5 NumericalResults 157
5.6 Summary 161
References 162
6 FastandRobustSparsity-BasedSTAPMethodsforNonhomogeneousClutter 165
XiaopengYang,YuzeSun,XuchenWu,TengLong,andTanpanK.Sarkar
6.1 Introduction 165
6.2 SignalModels 166
6.3 SparsityPrincipleAnalysisofSTAP 168
6.4 FastandRobustSparsity-BasedSTAPMethods 172
6.5 Conclusions 190
References 190
7 Super-ResolutionRadarImagingviaConvexOptimization 193
ReinhardHeckel
7.1 Introduction 193