Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets

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dc.contributor.authorLi, Peng
dc.contributor.authorGe, Hongwei
dc.contributor.authorYang, Jinlong
dc.coverage.issue2cs
dc.coverage.volume26cs
dc.date.accessioned2017-07-25T08:20:36Z
dc.date.available2017-07-25T08:20:36Z
dc.date.issued2017-06cs
dc.description.abstractUse of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, when targets of various sizes are spaced closely together and performing maneuvers, estimation errors will occur because measurement partitioning algorithms fail to provide the correct partitions. Specifically, the sub-partitioning algorithm fails to handle cases in which targets are of different sizes, while other partitioning approaches are sensitive to target maneuvers. This paper presents an improved partitioning algorithm for a GIW-PHD filter in order to solve the above problems. The sub-partitioning algorithm is improved by considering target extension information and by employing Mahalanobis distances to distinguish among measurement cells of different sizes. Thus, the improved approach is not sensitive to either differences in target sizes or target maneuvering. Simulation results show that the use of the proposed partitioning approach can improve the tracking performance of a GIW-PHD filter when target are spaced closely together.en
dc.formattextcs
dc.format.extent573-580cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2017 vol. 26, č. 2, s. 573-580. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2017.0573en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/69274
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2017/17_02_0573_0580.pdfcs
dc.rightsCreative Commons Attribution 4.0 International Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectTarget trackingen
dc.subjectextended targeten
dc.subjectfilteringen
dc.subjectGIW PHD filteren
dc.subjectmeasurement partitionen
dc.titleAdaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targetsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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