Multi-target tracking with PHD filter using Doppler-only measurements

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dc.authoridOrguner, Umut/0000-0002-7670-5635
dc.contributor.authorGuldogan, Mehmet B.
dc.contributor.authorLindgren, David
dc.contributor.authorGustafsson, Fredrik
dc.contributor.authorHabberstad, Hans
dc.contributor.authorOrguner, Umut
dc.date.accessioned2025-10-24T18:09:00Z
dc.date.available2025-10-24T18:09:00Z
dc.date.issued2014
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractIn this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation results show that both PHD filter implementations successfully track multiple targets using only Doppler shift measurements. Moreover, as a proof-of-concept, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Experimental study results reveal that it is possible to track multiple ground targets using acoustic Doppler shift measurements in a passive multi-static scenario. We observed that the GM-PHD is more effective, efficient and easy to implement than the SMC-PHD filter. (C) 2014 Elsevier Inc. All rights reserved.
dc.description.sponsorshipELLIIT joint research program; Swedish Foundation for Strategic Research (SSF); Swedish Defence Research Agency (FOI); Swedish Governmental Agency for Innovation Systems (VINNOVA).
dc.description.sponsorshipThe authors gratefully acknowledge fundings from the ELLIIT joint research program, Swedish Foundation for Strategic Research (SSF) in the center MOVIII and the Swedish Defence Research Agency (FOI) Center for Advanced Sensors, Multisensors and Sensor Networks (FOCUS) funded by the Swedish Governmental Agency for Innovation Systems (VINNOVA).
dc.identifier.doi10.1016/j.dsp.2014.01.009
dc.identifier.endpage11
dc.identifier.issn1051-2004
dc.identifier.issn1095-4333
dc.identifier.scopus2-s2.0-84897659149
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.dsp.2014.01.009
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3411
dc.identifier.volume27
dc.identifier.wosWOS:000334822800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Press Inc Elsevier Science
dc.relation.ispartofDigital Signal Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20251023
dc.subjectRandom sets; Multi-target tracking; Probability hypothesis density filter; Doppler measurements; Gaussian mixture; Sequential Monte Carlo
dc.titleMulti-target tracking with PHD filter using Doppler-only measurements
dc.typeArticle

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