Deep Neural Network Design for Radar Applications

419 Pages·2021·30.447 MB·English

by  Sevgi Zubeyde Gurbuz

Deep Neural Network
Design for Radar
Applications
Relatedtitlesonradar:
AdvancesinBistaticRadarWillisandGriffiths
AirborneEarlyWarningSystemConcepts,3rdEditionLong
BistaticRadar,2ndEditionWillis
DesignofMulti-FrequencyCWRadarsJankiraman
DigitalTechniquesforWidebandReceivers,2ndEditionTsui
ElectronicWarfarePocketGuideAdamy
FoliagePenetrationRadar:DetectionandcharacterisationofobjectsundertreesDavis
FundamentalsofGroundRadarforATCEngineersandTechniciansBouwman
FundamentalsofSystemsEngineeringandDefenseSystemsApplicationsJeffrey
IntroductiontoElectronicWarfareModelingandSimulationAdamy
IntroductiontoElectronicDefenseSystemsNeri
IntroductiontoSensorsforRangingandImagingBrooker
MicrowavePassiveDirectionFindingLipsky
MicrowaveReceiverswithElectronicWarfareApplicationsTsui
Phased-ArrayRadarDesign:ApplicationofradarfundamentalsJeffrey
PocketRadarGuide:Keyfacts,equations,anddataCurry
PrinciplesofModernRadar,Volume1:BasicprinciplesRichards,ScheerandHolm
PrinciplesofModernRadar,Volume2:AdvancedtechniquesMelvinandScheer
PrinciplesofModernRadar,Volume3:ApplicationsScheerandMelvin
PrinciplesofWaveformDiversityandDesignWicksetal.
PulseDopplerRadarAlabaster
RadarCrossSectionMeasurementsKnott
RadarCrossSection,2ndEditionKnottetal.
RadarDesignPrinciples:Signalprocessingandtheenvironment,2ndEditionNathanson
etal.
RadarDetectionDiFrancoandRubin
RadarEssentials:AconcisehandbookforradardesignandperformanceCurry
RadarFoundationsforImagingandAdvancedConceptsSullivan
RadarPrinciplesfortheNon-Specialist,3rdEditionToomayandHannan
TestandEvaluationofAircraftAvionicsandWeaponsSystemsMcShea
UnderstandingRadarSystemsKingsleyandQuegan
UnderstandingSyntheticApertureRadarImagesOliverandQuegan
RadarandElectronicWarfarePrinciplesfortheNon-Specialist,4thEditionHannen
InverseSyntheticApertureRadarImaging:Principles,algorithmsandapplicationsChen
andMartorella
Stimson’sIntroductiontoAirborneRadar,3rdEditionBaker,GriffithsandAdamy
TestandEvaluationofAvionicsandWeaponSystems,2ndEditionMcShea
Angle-of-ArrivalEstimationUsingRadarInterferometry:Methodsandapplications
Holder
Biologically-InspiredRadarandSonar:LessonsfromnatureBalleri,GriffithsandBaker
TheImpactofCognitiononRadarTechnologyFarina,DeMaioandHaykin
NovelRadarTechniquesandApplications,Volume1:Realaperturearrayradar,imaging
radar,andpassiveandmultistaticradarKlemm,Nickel,Gierull,Lombardo,Griffithsand
Koch
NovelRadarTechniquesandApplications,Volume2:Waveformdiversityandcognitive
radar,andtargettrackinganddatafusionKlemm,Nickel,Gierull,Lombardo,Griffithsand
Koch
RadarandCommunicationSpectrumSharingBluntandPerrins
SystemsEngineeringforEthicalAutonomousSystemsGillespie
ShadowingFunctionFromRandomlyRoughSurfaces:Derivationandapplications
BourlierandLi
PhotoforRadarNetworksandElectronicWarfareSystemsBogoni,LaghezzaandGhelfi
MultidimensionalRadarImagingMartorella
RadarWaveformDesignBasedonOptimizationTheoryCui,DeMaio,FarinaandLi
Micro-DopplerRadarandItsApplicationsFioranelli,Griffiths,RitchieandBalleri
MaritimeSurveillancewithSyntheticApertureRadarDiMartinoandAntonioIodice
ElectronicScannedArrayDesignWilliams
AdvancedSparsity-DrivenModelsandMethodsforRadarApplicationsGangLi
Deep Neural Network
Design for Radar
Applications
Edited by
Sevgi Zubeyde Gurbuz
TheInstitutionofEngineeringandTechnology
PublishedbySciTechPublishing,animprintofTheInstitutionofEngineeringand
Technology,London,UnitedKingdom
TheInstitutionofEngineeringandTechnologyisregisteredasaCharityin
England&Wales(no.211014)andScotland(no.SC038698).
©TheInstitutionofEngineeringandTechnology2021
Firstpublished2020
ThispublicationiscopyrightundertheBerneConventionandtheUniversalCopyright
Convention.Allrightsreserved.Apartfromanyfairdealingforthepurposesofresearch
orprivatestudy,orcriticismorreview,aspermittedundertheCopyright,Designsand
PatentsAct1988,thispublicationmaybereproduced,storedortransmitted,inany
formorbyanymeans,onlywiththepriorpermissioninwritingofthepublishers,orin
thecaseofreprographicreproductioninaccordancewiththetermsoflicencesissued
bytheCopyrightLicensingAgency.Enquiriesconcerningreproductionoutsidethose
termsshouldbesenttothepublisherattheundermentionedaddress:
TheInstitutionofEngineeringandTechnology
MichaelFaradayHouse
SixHillsWay,Stevenage
Herts,SG12AY,UnitedKingdom
www.theiet.org
Whiletheauthorsandpublisherbelievethattheinformationandguidancegiveninthis
workarecorrect,allpartiesmustrelyupontheirownskillandjudgementwhenmaking
useofthem.Neithertheauthorsnorpublisherassumesanyliabilitytoanyoneforany
lossordamagecausedbyanyerrororomissioninthework,whethersuchanerroror
omissionistheresultofnegligenceoranyothercause.Anyandallsuchliability
isdisclaimed.
Themoralrightsoftheauthorstobeidentifiedasauthorsofthisworkhavebeen
assertedbytheminaccordancewiththeCopyright,DesignsandPatentsAct1988.
BritishLibraryCataloguinginPublicationData
AcataloguerecordforthisproductisavailablefromtheBritishLibrary
ISBN978-1-78561-852-9(hardback)
ISBN978-1-78561-853-6(PDF)
TypesetinIndiabyMPSLimited
PrintedintheUKbyCPIGroup(UK)Ltd,Croydon
Tomymother:
mybestfriend,
hero,and
guardianangel.
Youhadmanyaccomplishments,
butyourgreatestwassomehow
turningthiswildgirlintoanengineer.
Foreverliveinmyheartyouwill.

Contents
Abouttheeditor xv
Acknowledgements xvii
Prologue:perspectivesondeeplearningofRFdata 1
SevgiZubeydeGurbuzandEricS.Mason
P.1 TheneedfornovelDNNarchitectures 3
P.2 Physics–awareMLthatexploitsRFdatarichness 5
P.3 RFsensingproblemsthatcanbenefitfromDL 7
P.4 Overviewofthisbook 8
PartI Fundamentals 9
1 Radarsystems,signals,andphenomenology 11
SevgiZubeydeGurbuz,ShunqiaoSun,andDavidTahmoush
1.1 Physicsofelectromagneticscattering 11
1.1.1 Effectsofpropagationmedium 14
1.1.2 Radarcrosssection 14
1.1.3 Radarrangeequation 16
1.2 Basicradarmeasurementsandwaveforms 18
1.2.1 Continuouswaveradar 18
1.2.2 PulsedCWradar 20
1.2.3 RangeandDopplerresolution 21
1.2.4 Pulsed-Dopplerradar 22
1.2.5 Frequency-modulatedcontinuouswave(FMCW)radar 22
1.2.6 Ambiguityfunctionsandrange-Dopplercoupling 24
1.3 Realandsyntheticapertureradarprocessing 25
1.3.1 Movingtargetindication 26
1.3.2 Arrayprocessingtechniques 26
1.3.3 Themicro-Dopplereffectandtime–frequencyanalysis 28
1.3.4 Syntheticapertureradar 29
1.4 Radardatadenoisingformachinelearning 31
1.5 Radardatarepresentationsformachinelearning 32
1.5.1 State-of-the-artautomotiveradar 33
1.5.2 AutomotiveradarwithMIMOradartechnology 34
1.5.3 High-resolutionautomotiveradarandpointclouds 36
viii Deepneuralnetworkdesignforradarapplications
1.6 Additionalreading 39
References 40
2 Basicprinciplesofmachinelearning 43
AliCaferGurbuzandFauziaAhmad
2.1 Learningfromdata 44
2.1.1 Supervisedlearning 44
2.1.2 Unsupervisedlearning 46
2.2 IngredientsofanMLalgorithm 47
2.3 Basictechniquesofsupervisedandunsupervised
learning 48
2.3.1 Supervisedlearningapproaches 49
2.3.2 Unsupervisedlearningapproaches 53
2.4 Evaluationofamachinelearningalgorithm 54
2.4.1 GeneralworkflowofanMLalgorithm 54
2.4.2 Performancemetrics 55
2.4.3 Measuringgeneralization:training,validation,and
testsets 59
2.4.4 Overfittingandunderfitting 60
2.5 Conclusion 63
References 63
3 Theoreticalfoundationsofdeeplearning 69
StefanBrüggenwirthandSimonWagner
3.1 Introduction 69
3.2 Perceptron 71
3.3 Sigmoidperceptron 72
3.4 Multilayerperceptron 72
3.5 Gradientdescent 74
3.6 Backpropagation 75
3.7 Improvements 78
3.7.1 Activationfunctions 78
3.7.2 Momentum 80
3.7.3 Stochasticgradientdescent 80
3.7.4 Cross-entropycostfunction 81
3.7.5 Regularization 81
3.8 Theshorthistoryofdeeplearning 82
3.9 Convolutionalneuralnetworks 82
3.9.1 Structureofaconvolutionalneuralnetwork 83
3.9.2 Trainingofaconvolutionalneuralnetwork 85
3.10 Autoencoders 87
3.10.1 Datacompressionwithautoencoders 88
3.10.2 Otherapplicationsofautoencoders 91
Contents ix
3.11 Advancedtrainingtechniquesandfurtherapplicationsofdeep
learning 93
References 95
PartII Specialtopics 97
4 Radardatarepresentationforclassificationofactivitiesof
dailyliving 99
BarisErolandMoenessG.Amin
4.1 Introduction 99
4.2 Radarsignalmodelanddomainsuitability 101
4.3 Multilinearsubspacelearning 106
4.3.1 Multilinearalgebrabasicsandnotations 106
4.3.2 Multilinearprincipalcomponentanalysis(MPCA) 107
4.3.3 Multilineardiscriminantanalysis 108
4.4 Optimizationconsiderationsformultidimensionalmethods 108
4.4.1 Iterativeprojections 108
4.4.2 Initialization 109
4.4.3 Terminationcriteriaandconvergence 110
4.4.4 Numberofprojections 111
4.5 BoostingtheMPCA 114
4.6 Experimentalresults 115
4.7 Conclusion 117
References 118
5 ChallengesintrainingDNNsforclassificationofradar
micro-Dopplersignatures 123
SevgiZ.Gurbuz,MoenessG.Amin,MehmetS.Seyfiog˘lu,andBarisErol
5.1 Theoryoftrainingcomplexmodels 124
5.1.1 Bias–variancetrade-off 124
5.1.2 Requirementsontrainingsamplesupport 125
5.1.3 Machinelearningtheoryversuspractice:empiricalstudies 126
5.2 Trainingwithsmallamountsofrealdata 127
5.2.1 Unsupervisedpretraining 128
5.3 Cross-frequencytrainingusingdatafromotherradars 129
5.4 Transferlearningusingpretrainednetworks 134
5.5 Trainingwithsyntheticdatafromkinematicmodels 136
5.5.1 ModelingradarreturnwithMOCAPdata 136
5.5.2 Diversificationofsyntheticmicro-Dopplersignatures 137
5.5.3 Residuallearning 138
5.6 Trainingwithsyntheticdatageneratedbyadversarialneural
networks 141
5.6.1 WassersteinGAN 144
5.6.2 Conditionalvariationalautoencoder 145