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    A Particle Marginal Metropolis-Hastings Multi-Target Tracker

    Access Status
    Fulltext not available
    Authors
    Vu, T.
    Vo, Ba-Ngu
    Evans, R.
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Vu, 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.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2014.2329270
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/34528
    Collection
    • Curtin Research Publications
    Abstract

    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.

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