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dc.contributor.authorVu, T.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorEvans, R.
dc.identifier.citationVu, T. and Vo, B. and Evans, R. 2014. A Particle Marginal Metropolis-Hastings Multi-Target Tracker. IEEE Transactions on Signal Processing. 62 (15): pp. 3953-3964.

We propose a Bayesian multi-target batch processing algorithm capable of tracking an unknown number of targets that move close and/or cross each other in a dense clutter environment. The optimal Bayes multitarget tracking problem is formulated in the random finite set framework and a particle marginal Metropolis-Hastings (PMMH) technique which is a combination of the Metropolis-Hastings (MH) algorithm and sequential Monte Carlo methods is applied to compute the multi-target posterior distribution. The PMMH technique is used to design a high-dimensional proposal distributions for the MH algorithm and allows the proposed batch process multi-target tracker to handle a large number of tracks in a computationally feasible manner. Our simulations show that the proposed tracker reliably estimates the number of tracks and their trajectories in scenarios with a large number of closely spaced tracks in a dense clutter environment albeit, more expensive than online methods.

dc.titleA Particle Marginal Metropolis-Hastings Multi-Target Tracker
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Signal Processing
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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