Modern Radar Detection Theory

395 Pages·2016·12.808 MB·English

by  De Maio, Antonio

Modern Radar
Detection Theory

Modern Radar
Detection Theory
Edited by
Antonio De Maio
Dipartimento di Ingegneria Elettrica e delle Tecnologie
dell’Informazione, Università degli Studi di Napoli
“Federico II”
Maria Sabrina Greco
Dipartimento di Ingegneria dell’Informazione,
Università di Pisa
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Contents
1 Introduction to Radar Detection 1
AntonioDeMaio,MariaS.Greco,andDaniloOrlando
1.1 HistoricalBackgroundandTerminology 1
1.2 Symbols 5
1.3 DetectionTheory 6
1.3.1 SignalandInterferenceModels 7
1.3.2 BasicConcepts 9
1.3.3 DetectorDesignCriteria 11
1.3.4 CFARPropertyandInvarianceinDetectionTheory 13
1.4 Organization,Use,andOutlineoftheBook 14
1.5 References 16
References 17
2 Radar Detection inWhite Gaussian Noise:A GLRT Framework 21
ErnestoConte,AntonioDeMaio,andGuolongCui
2.1 Introduction 21
2.2 ProblemFormulation 22
2.3 ReductionbySufficiency 24
2.4 OptimumNPDetectorandExistenceoftheUMPTest 26
2.4.1 CoherentCase 26
2.4.2 Non-coherentCase 27
2.5 GLRTDesign 28
2.6 PerformanceAnalysis 32
2.6.1 CoherentCase 32
2.6.2 Non-coherentCase 36
2.7 ConclusionsandFurtherReading 40
References 41
vii
viii Contents
3 Subspace Detection forAdaptive Radar: Detectors
and PerformanceAnalysis 43
RamS.Raghavan,ShawnKraut,andChristD.Richmond
3.1 Introduction 43
3.2 IntroductiontoSignalDetectioninInterferenceandNoise 45
3.2.1 DetectingaKnownSignalinColoredGaussianNoise 46
3.2.2 DetectingaKnownSignalwithUnknownPhaseinZero-Mean
ColoredGaussianNoise 47
3.3 SubspaceSignalModelandInvariantHypothesisTests 48
3.3.1 SubspaceSignalModel 49
3.3.2 ARationaleforSubspaceSignalModel 49
3.3.3 HypothesisTest 51
3.3.4 MaximumInvariantsforSubspaceSignalDetection
inInterferenceandNoise 53
3.4 AnalyticalExpressionsforP andP 54
D FA
3.4.1 P andP forSubspaceGLRT 54
D FA
3.4.2 P andP forSubspaceAMFTest 55
D FA
3.4.3 P andP forSubspaceACETest 56
D FA
3.5 PerformanceResultsofAdaptiveSubspaceDetectors 57
3.6 SummaryandConclusions 70
Appendix3.A 71
Appendix3.B 74
Appendix3.C 75
Appendix3.D 79
References 80
4 Two-Stage Detectors for Point-LikeTargets in Gaussian
Interference with Unknown Spectral Properties 85
AntonioDeMaio,ChengpengHao,andDaniloOrlando
4.1 Introduction:PrinciplesofDesign 85
4.2 Two-StageArchitectureDescription,PerformanceAnalysis,
andComparisons 91
4.2.1 TheAdaptiveSidelobeBlanker 93
4.2.2 ModificationsoftheASBtowardsRobustness:
TheSubspace-BasedASB 97
4.2.3 ModificationsoftheASBtowardsSelectivity 104
4.2.4 ModificationsoftheASBtowardsbothSelectivityandRobustness 117
4.2.5 SelectiveTwo-StageDetectors 125
Contents ix
4.3 Conclusions 128
References 129
5 Bayesian Radar Detection in Interference 133
PuWang,HongbinLi,andBrahamHimed
5.1 Introduction 133
5.2 GeneralSTAPSignalModel 134
5.3 KA-STAPModels 136
5.3.1 Knowledge-AidedHomogeneousModel 136
5.3.2 BayesianGLRT(B-GLRT)andBayesianAMF(B-AMF) 138
5.3.3 SelectionofHyperparameter 140
5.3.4 ExtensionstoPartiallyHomogeneousand
Compound-GaussianModels 144
5.4 Knowledge-AidedTwo-LayeredSTAPModel 147
5.5 Knowledge-AidedParametricSTAPModel 151
5.6 Summary 159
Appendix5.A 159
Appendix5.B 160
References 162
6 Adaptive Radar Detection for Sample-Starved
GaussianTraining Conditions 165
YuriI.AbramovichandBenA.Johnson
6.1 Introduction 165
6.2 ImprovingAdaptiveDetectionUsingEL-SelectedLoading 168
6.2.1 SingleAdaptiveFilterFormedwithSecondaryData,Followed
byAdaptiveThresholdingUsingPrimaryData 168
6.2.2 DifferentAdaptiveProcessperTestCellwithCombined
AdaptiveFilteringandDetectionUsingSecondaryData 170
6.2.3 Observations 209
6.3 ImprovingAdaptiveDetectionUsingCovarianceMatrixStructure 212
6.3.1 Background:TVAR(m)ApproximationofaHermitian
CovarianceMatrix,MLModelIdentificationandOrder
Estimation 215
6.3.2 PerformanceAnalysisofTVAR(m)-BasedAdaptiveFiltersand
AdaptiveDetectorsforTVAR(m)orAR(m)Interferences 218
6.3.3 SimulationResultsofTVAR(m)-BasedAdaptiveDetectors
forTVAR(m)orAR(m)Interferences 223
6.3.4 Observations 237