Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7094
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dc.contributor.authorKarabacak, C.-
dc.contributor.authorGürbüz, S. Z.-
dc.contributor.authorGüldoğan, M. B.-
dc.contributor.authorGürbüz, A. C.-
dc.date.accessioned2021-09-11T15:45:30Z-
dc.date.available2021-09-11T15:45:30Z-
dc.date.issued2013en_US
dc.identifier.citationConference on Active and Passive Signatures IV -- MAY 01-02, 2013 -- Baltimore, MDen_US
dc.identifier.isbn978-0-8194-9525-9-
dc.identifier.issn0277-786X-
dc.identifier.issn1996-756X-
dc.identifier.urihttps://doi.org/10.1117/12.2017709-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7094-
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.en_US
dc.description.sponsorshipSPIEen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofActive And Passive Signatures Iven_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecthuman micro-Doppleren_US
dc.subjecthuman classificationen_US
dc.subjectdata fusionen_US
dc.subjectradar networksen_US
dc.titleMulti-Aspect Angle Classification of Human Radar Signaturesen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesProceedings of SPIEen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume8734en_US
dc.authorid0000-0001-8923-0299-
dc.identifier.wosWOS:000323335100007en_US
dc.identifier.scopus2-s2.0-84881143727en_US
dc.institutionauthorGürbüz, Ali Cafer-
dc.identifier.doi10.1117/12.2017709-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conferenceConference on Active and Passive Signatures IVen_US
dc.identifier.scopusquality--
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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