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    Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

    Access Status
    Fulltext not available
    Authors
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Phung, D.
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Vo, B. and Vo, B.T. and Phung, D. 2014. Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter. IEEE Transactions on Signal Processing. 62 (24): pp. 6554-6567.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2014.2364014
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/7899
    Collection
    • Curtin Research Publications
    Abstract

    An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.

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