A Gaussian Mixture PHD Filter for Jump Markov System Models

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dc.contributor.author Pasha, Syed en_US
dc.contributor.author Vo, Ba-Ngu en_US
dc.contributor.author Hoang, Tuan en_US
dc.contributor.author Ma, Wing-Kin en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T10:56:15Z
dc.date.available 2012-02-02T10:56:15Z
dc.date.issued 2009 en_US
dc.identifier 2010003557 en_US
dc.identifier.citation Pasha Syed et al. 2009, 'A Gaussian Mixture PHD Filter for Jump Markov System Models', IEEE-Inst Electrical Electronics Engineers Inc, vol. 45, no. 3, pp. 919-936. en_US
dc.identifier.issn 0018-9251 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15292
dc.description.abstract The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets. en_US
dc.language en_US
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/TAES.2009.5259174 en_US
dc.title A Gaussian Mixture PHD Filter for Jump Markov System Models en_US
dc.parent IEEE Transactions On Aerospace And Electronic Systems en_US
dc.journal.volume 45 en_US
dc.journal.number 3 en_US
dc.publocation Piscataway en_US
dc.identifier.startpage 919 en_US
dc.identifier.endpage 936 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 090600 en_US
dc.personcode 0000068793 en_US
dc.personcode 0000068794 en_US
dc.personcode 110708 en_US
dc.personcode 0000068796 en_US
dc.percentage 100 en_US
dc.classification.name Electrical and Electronic Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity ISI:000270225500008 en_US
dc.description.keywords The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed en_US


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