Information-theoretic joint probabilistic data association filter

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dc.contributor.authorHe, Shaoming
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-03-08T16:15:31Z
dc.date.available2021-03-08T16:15:31Z
dc.date.issued2021-03-03
dc.description.abstractThis article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback–Leibler divergence to approximate the posterior Gaussian mixture probability density function. Theoretical analysis of mean performance and error covariance performance with ideal detection probability is presented to provide insights of the proposed approach. Extensive empirical simulations are undertaken to validate the performance of the proposed multitarget tracking algorithm.en_UK
dc.identifier.citationHe S, Shin HS, Tsourdos A. (2020) Information-theoretic joint probabilistic data association filter. IEEE Transactions on Automatic Control, Volume 66, Issue 3, March 2021, pp. 1262-1269en_UK
dc.identifier.issn0018-9286
dc.identifier.urihttps://doi.org/10.1109/TAC.2020.2989766
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16454
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectInformation-theoretic approachen_UK
dc.subjectjoint probabilistic data associationen_UK
dc.subjectmultiple target trackingen_UK
dc.titleInformation-theoretic joint probabilistic data association filteren_UK
dc.typeArticleen_UK

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