Multi-Aspect Angle Classification of Human Radar Signatures

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dc.authoridGurbuz, Sevgi Zubeyde/0000-0001-7487-9087|Gurbuz, Ali Cafer/0000-0001-8923-0299;
dc.contributor.authorKarabacak, C.
dc.contributor.authorGurbuz, S. Z.
dc.contributor.authorGuldogan, M. B.
dc.contributor.authorGurbuz, A. C.
dc.date.accessioned2025-10-24T18:09:23Z
dc.date.available2025-10-24T18:09:23Z
dc.date.issued2013
dc.departmentMalatya Turgut Özal Üniversitesi
dc.descriptionConference on Active and Passive Signatures IV -- MAY 01-02, 2013 -- Baltimore, MD
dc.description.abstractThe human micro-Doppler signature is a unique signature caused by the time-varying motion of each point on the human body, which can be used to discriminate humans from other targets exhibiting micro-Doppler, such as vehicles, tanks, helicopters, and even other animals. Classification of targets based on micro-Doppler generally involves joint time-frequency analysis of the radar return coupled with extraction of features that may be used to identify the target. Although many techniques have been investigated, including artificial neural networks and support vector machines, almost all suffer a drastic drop in classification performance as the aspect angle of human motion relative to the radar increases. This paper focuses on the use of radar networks to obtain multi-aspect angle data and thereby ameliorate the dependence of classification performance on aspect angle. Knowledge of human walking kinematics is exploited to generate a fuse spectrogram that incorporates estimates of model parameters obtained from each radar in the network. It is shown that the fused spectrogram better approximates the truly underlying motion of the target observed as compared with spectrograms generated from individual nodes.
dc.description.sponsorshipSPIE
dc.identifier.doi10.1117/12.2017709
dc.identifier.isbn978-0-8194-9525-9
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-84881143727
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.1117/12.2017709
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3621
dc.identifier.volume8734
dc.identifier.wosWOS:000323335100007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpie-Int Soc Optical Engineering
dc.relation.ispartofActive And Passive Signatures Iv
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20251023
dc.subjecthuman micro-Doppler; human classification; data fusion; radar networks
dc.titleMulti-Aspect Angle Classification of Human Radar Signatures
dc.typeConference Object

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