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dc.contributor.authorLi, P.
dc.contributor.authorXu, C.
dc.contributor.authorWang, W.
dc.contributor.authorSu, S.
dc.date.accessioned2020-10-14T07:07:55Z
dc.date.available2020-10-14T07:07:55Z
dc.date.issued2020-09cs
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 3, s. 529-539. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/195214
dc.description.abstractMeasurement-outliers caused by non-linear observation model or random disturbance will lead to the accuracy decline of a target tracking filter. This paper proposes a robust probability hypothesis density (PHD) filter to handle the measurement-outlier problem based on Student’s T Kalman (TK) filtering technique and Variational Bayesian (VB) method. First, the non-standard measurement noise is considered to follow the Student’s T distribution. Second, the TK filtering technique is employed to update the target states. Third, the posterior likelihood is updated by the VB approach. Simulation results show that the proposed method can reduce the optimal subpattern assignment (OSPA) error in the non-standard observation scenarios with measurement-outliers, compared with other typical multiple target tracking filters.en
dc.formattextcs
dc.format.extent529-539cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2020/20_03_0529_0539.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMultiple target trackingen
dc.subjectPHD filteren
dc.subjectStudent’s T Kalmanen
dc.subjectVariational Bayesianen
dc.subjectnon-linear filter.en
dc.titleRobust Student’s T Distribution Based PHD/CPHD Filter for Multiple Targets Tracking Using Variational Bayesian Approachen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume29cs
dc.identifier.doi10.13164/re.2020.0529en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International license