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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Deep Learning Methods for Automotive
Radar Signal Processing
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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TECHNISCHE UNIVERSITÄT MÜNCHEN
Professur für Höchstfrequenztechnik
Deep Learning Methods for Automotive
Radar Signal Processing
Rodrigo Pérez González
Vollständiger Abdruck der von der Fakultät für Elektrotechnik und In-
formationstechnik der Technischen Universität München zur Erlangung
des akademischen Grades eines
Doktor-Ingenieurs
genehmigten Dissertation.
.d Vorsitzender: Prof. Dr.-Ing. Klaus Diepold
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vre Prüfer der Dissertation:
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er s 1. Prof. Dr.-Ing. habil. Erwin Biebl
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illiv Die Dissertation wurde am 11.11.2020 bei der Technischen Universität
u
C .1 München eingereicht und durch die Fakultät für Elektrotechnik und In-
2
0 formationstechnik am 01.03.2021 angenommen.
2
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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BibliografischeInformationderDeutschenNationalbibliothek
Die Deutsch eiNationalbibliot hekiverzei chnet idieseiPub lik ati oniinider
Deutschen Nationalbibliografie;idetaillierteibibliografischeiDaten
sindimInternetiüberhttp://dnb.d-nb.deiabrufbar.
1.iAufl.i-iGöttingen:iCuvillier,i2021
Zugl.:(TU) München, Univ., Di ss., 2021
CUVILLIERiVERLAG,iGöttingeni2021
Nonnenstiegi8,i37075iGöttingen
Telefon:i0551-54724-0
Telefax:i0551-54724-21
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Abstract
The automotive industry is currently working towards bringing autonomous vehicles
totheroad. Asthelevelofautomatedfunctionsincrease,so dotherequirementsfor
thevehicle’ssensorsandtheircorrespondingalgorithms.Forfullyautomateddrivingto
becomeareality,futuresensorsystemsmustbeabletonotonlyreliablyandaccurately
capture the vehicle’s environment,but also to provide semantic information. Due to
theircostandallweatherrobustness,radarsensorsareanattractivecandidateforthis
task.
Inthiswork,deeplearningmethods,meanttoenhance—orevenreplace—theclassical
radarsignalprocessing chain,are developedandevaluatedin the contextofautomo-
tiveapplications.Firstasystemthatclassifiespedestrians,cyclistsandcarsbasedon
single-frameradarmeasurementsispresented.Itisanovelsystem,whichdemonstrates,
that single-frame classification on the raw radar range-Doppler-angle spectrum with
convolutionalneuralnetworksisfeasible.Thesecondclassificationapproachpresented
inthisworkconsistsofperformingapreviousdetectionsteptoextractregionsofinterest
of the range-Doppler-angle spectrum and then classifying them with a convolutional
neuralnetwork,thusenablingmulti-targetscenarios.
Athirdsystemthatreplacesthedetection,clustering,objectcreationandclassification
tasksisalsopresented.ItemploystheYouOnlyLookOnce(YOLO)objectdetection
system—originallycreatedforimageobjectdetection—ontheradarspectrum.Inone
singlestep,thenetworkprovidesaboundingboxaroundtheobjects’rangeandvelocity
profiles and provides a class prediction. The fourth and last system, demonstrates
thepossibilityofusingartificialneuralnetworksdirectlyonthebasebandtime-domain
signalsforrangedetection.Forthispurpose,simulatedtargetsareemployedtotrainand
evaluatethenetwork.Furthermore,theapproachisvalidatedwithrealmeasurements.
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Acknowledgements
This thesis is the result of my work of four years on a joint project of the Associate
ProfessorshipofMicrowaveEngineeringoftheTechnicalUniversityofMunichandthe
BMWAG.Iwouldliketotakeamomenttoextendmygratitudetoallthepeople,who
contributedtoitscompletion.
First,IwouldliketosincerelythankProf.Dr.Bieblforgivingmetheopportunityto
workonthisveryinterestingandchallengingproject.Iamverythankfulfortheliberty
Ienjoyedwhileworkingonthisproject,aswellasforhisreadinesstohelpandexcellent
professionaladvice.
AtBMWIwouldliketoextendmygratitudetoDr.RalphRasshofer,whotogether
withProfessorBieblfirstconceivedtheconceptofthisproject.Likewise,Iwouldlike
tothankDr. FalkSchubertformentoringmethroughoutthewholedoctoralprocess.
Hisfrequentfeedback,whichoftenwentbeyondtechnicalaspects,helpedmegreatlyto
steermyfocusintherightdirection.Furthermore,IwouldliketothankDr.QingRao
andNickEngelhardtfortheircollaboration.
To Dr. Florian Pfeiffer from the company Perisens I extend my gratitude for the
supportinregardstohighfrequencymeasurementsandradomedesign.
I am also very grateful to have shared this time with my colleagues at the Asso-
ciateProfessorshipofMicrowaveEngineeringDr.AloisAscher,ChristianBuchberger,
BrunaCruz,ThomasEder,Dr.MaximilianEngelhardt,Dr.PhilippEschlwech,Michael
Hani,Dr.FabianHarrer,OnurKepenek,VeraKurz,MiroslavLach,FelixRutzandDr.
NilsWaldmann.ThepleasingandfriendlyworkingatmosphereissomethingIvalued
dearly.FurthermoreI’dliketoespeciallythankJanaMögelforhergreatsupportonall
organisationalmattersandcountlessproofreadings.
Finally,Iwouldliketothankallmyfriends,myparentsClaudiaandEduardoand
mybrothersEmilioandCristóbalfortheirunconditionalsupportwhichhasmotivated
meallalongmyacademicpath.
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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Contents
1 Introduction 1
1.1 GoalsandContentsofthisWork . . . . . . . . . . . . . . . . . . . . . 4
2 Radar Fundamentals 7
2.1 ContinuousWaveRadar . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Mono-FrequentContinuousWaveRadar . . . . . . . . . . . . . . . . . 9
2.3 LinearFrequencyModulatedContinuousWaveRadar . . . . . . . . . . 11
2.4 ChirpSequenceFrequencyModulatedContinuousWaveRadar. . . . . 13
2.5 TargetDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5.1 CellAveragingCFAR . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.2 OrderStaticsCFAR . . . . . . . . . . . . . . . . . . . . . . . . 19
2.6 PhasedArrays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.7 RadarSystemConsiderations . . . . . . . . . . . . . . . . . . . . . . . 22
2.7.1 ExperimentalRadarBoard. . . . . . . . . . . . . . . . . . . . . 22
2.7.2 WaveformDesign . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.7.3 CasingandRadomeDesign . . . . . . . . . . . . . . . . . . . . 25
3 Machine Learning Fundamentals 29
3.1 SupervisedLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 ArtificialNeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 MultilayerPerceptrons . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.2 ConvolutionalNeuralNetworks . . . . . . . . . . . . . . . . . . 33
3.3 TrainingofArtificialNeuralNetworks. . . . . . . . . . . . . . . . . . . 35
3.4 ActivationFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 LossFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.6 EvaluationMetrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
.d
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gir llA 44..11..12 APeudteosmtroibanilses .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 4447
.ga 4.1.3 Cyclists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
lreV 4.2 SingleFrameVulnerableRoadUsersClassification. . . . . . . . . . . . 51
reilliv 4.2.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
uC 4.2.2 SystemConcept. . . . . . . . . . . . . . . . . . . . . . . . . . . 52
.1 4.2.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
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Deep Learning Methods for Automotive Radar Signal Processing, Cuvillier Verlag, 2021. ProQuest Ebook Central,
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