An analysis of the Bayesian track labelling problem

E.H. Aoki, Y. Boers, Lennart Svensson, Pranab K. Mandal, Arunabha Bagchi

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    In multi-target tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature, but its exact mathematical formulation, in terms of Bayesian statistics, has not been yet looked at in detail. Doing so, however, may help us to understand how Bayes-optimal track labelling should be performed or numerically approximated. Moreover, it can help us to better understand and tackle some practical difficulties associated with the MTT problem, in particular the so-called ``mixed labelling'' phenomenon that has been observed in MTT algorithms. In this memorandum, we rigorously formulate the optimal track labelling problem using Finite Set Statistics (FISST), and look in detail at the mixed labeling phenomenon. As practical contributions of the memorandum, we derive a new track extraction formulation with some nice properties and a statistic associated with track labelling with clear physical meaning. Additionally, we show how to calculate this statistic for two well-known MTT algorithms.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherUniversity of Twente
    Number of pages8
    Publication statusPublished - Mar 2012

    Publication series

    NameMemorandum / Department of Applied Mathematics
    PublisherUniversity of Twente, Department of Applied Mathematics
    ISSN (Print)1874-4850
    ISSN (Electronic)1874-4850


    • Particle filter
    • Track labelling
    • EWI-21663
    • Finite Set Statistics
    • Target tracking
    • IR-79947
    • METIS-296044

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