Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

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dc.contributor.authorŠauša, Erikcs
dc.contributor.authorRajmic, Pavelcs
dc.contributor.authorHlawatsch, Franzcs
dc.coverage.issueFebruary 2024cs
dc.coverage.volume215cs
dc.date.issued2024-02-01cs
dc.description.abstractThe likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.en
dc.formattextcs
dc.format.extent1-13cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSIGNAL PROCESSING. 2024, vol. 215, issue February 2024, p. 1-13.en
dc.identifier.doi10.1016/j.sigpro.2023.109259cs
dc.identifier.issn0165-1684cs
dc.identifier.orcid0000-0002-8381-4442cs
dc.identifier.other184719cs
dc.identifier.researcheridA-3467-2013cs
dc.identifier.scopus14024654600cs
dc.identifier.urihttp://hdl.handle.net/11012/214460
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofSIGNAL PROCESSINGcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S016516842300333Xcs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0165-1684/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectTarget trackingen
dc.subjectParticle filteren
dc.subjectLikelihood consensusen
dc.subjectSplinesen
dc.subjectOrthogonal matching pursuiten
dc.subjectOMPen
dc.subjectSparsityen
dc.subjectPDA filteren
dc.titleDistributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0en
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-184719en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:34en
sync.item.modts2025.01.17 15:15:43en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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