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    MCMC-based posterior independence approximation for RFS multitarget particle filters

    Access Status
    Fulltext not available
    Authors
    García-Fernández, A.
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    García-Fernández, A. and Vo, B. and Vo, B. 2014. MCMC-based posterior independence approximation for RFS multitarget particle filters, in 17th International Conference on Information Fusion (FUSION), pp. 1-8, Jul 7-10 2014. Salamanca, Spain: IEEE.
    Source Title
    FUSION 2014 - 17th International Conference on Information Fusion
    ISBN
    9788490123553
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/17146
    Collection
    • Curtin Research Publications
    Abstract

    The objective of this paper is to approximate the unlabelled posterior random finite set (RFS) density in multitarget tracking (MTT) using particle filters (PFs). The unlabelled posterior can be equivalently represented by any labelled density that belongs to the posterior RFS family. For the limited number of particles used in practice, PFs that assume posterior independence among target states outperform those without it. Consequently, we can improve the PF approximation by aiming at the labelled density within the posterior RFS family whose target states are as independent as possible. In this paper, we focus on the case of fixed and known number of targets and propose an algorithm based on Markov chain Monte Carlo (MCMC) that pursues this aim. This algorithm can be added to any PF with posterior independence assumption.

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