Knowledge Exploitation for Human Micro-Doppler Classification

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dc.authoridHendeby, Gustaf/0000-0002-1971-4295|Gurbuz, Sevgi Zubeyde/0000-0001-7487-9087|Gurbuz, Ali Cafer/0000-0001-8923-0299
dc.contributor.authorKarabacak, Cesur
dc.contributor.authorGurbuz, Sevgi Z.
dc.contributor.authorGurbuz, Ali C.
dc.contributor.authorGuldogan, Mehmet B.
dc.contributor.authorHendeby, Gustaf
dc.contributor.authorGustafsson, Fredrik
dc.date.accessioned2025-10-24T18:09:22Z
dc.date.available2025-10-24T18:09:22Z
dc.date.issued2015
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractMicro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.
dc.description.sponsorshipSAAB; EU FP7 Project [PIRG-GA-2010-268276]; TUBITAK Career [113E105]
dc.description.sponsorshipThis work was supported in part by SAAB and funding from Security Link, by the EU FP7 Project No. PIRG-GA-2010-268276, and by TUBITAK Career No. 113E105.
dc.identifier.doi10.1109/LGRS.2015.2452311
dc.identifier.endpage2129
dc.identifier.issn1545-598X
dc.identifier.issn1558-0571
dc.identifier.issue10
dc.identifier.startpage2125
dc.identifier.urihttps://doi.org/10.1109/LGRS.2015.2452311
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3589
dc.identifier.volume12
dc.identifier.wosWOS:000359576400024
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Geoscience And Remote Sensing Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20251023
dc.subjectClassification; human micro-Doppler; knowledge-based signal processing; motion capture (MOCAP)
dc.titleKnowledge Exploitation for Human Micro-Doppler Classification
dc.typeArticle

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