Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6967
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dc.contributor.authorKarabacak, Cesur-
dc.contributor.authorGürbüz, Sevgi Z.-
dc.contributor.authorGürbüz, Ali C.-
dc.contributor.authorGüldoğan, Mehmet B.-
dc.contributor.authorHendeby, Gustaf-
dc.contributor.authorGustafsson, Fredrik-
dc.date.accessioned2021-09-11T15:44:35Z-
dc.date.available2021-09-11T15:44:35Z-
dc.date.issued2015en_US
dc.identifier.issn1545-598X-
dc.identifier.issn1558-0571-
dc.identifier.urihttps://doi.org/10.1109/LGRS.2015.2452311-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6967-
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.en_US
dc.description.sponsorshipSAAB; EU FP7 ProjectEuropean Commission [PIRG-GA-2010-268276]; TUBITAK CareerTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [113E105]en_US
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.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Geoscience And Remote Sensing Lettersen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjecthuman micro-Doppleren_US
dc.subjectknowledge-based signal processingen_US
dc.subjectmotion capture (MOCAP)en_US
dc.titleKnowledge Exploitation for Human Micro-Doppler Classificationen_US
dc.typeArticleen_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.volume12en_US
dc.identifier.issue10en_US
dc.identifier.startpage2125en_US
dc.identifier.endpage2129en_US
dc.authorid0000-0001-7487-9087-
dc.authorid0000-0002-1971-4295-
dc.authorid0000-0001-8923-0299-
dc.identifier.wosWOS:000359576400024en_US
dc.identifier.scopus2-s2.0-85027953351en_US
dc.institutionauthorGürbüz, Ali Cafer-
dc.identifier.doi10.1109/LGRS.2015.2452311-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
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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