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    Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking

    Access Status
    Fulltext not available
    Authors
    Papi, Francesco
    Bocquel, M.
    Podt, M.
    Boers, Y.
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Papi, F. and Bocquel, M. and Podt, M. and Boers, Y. 2014. Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking. IEEE Transactions on Signal Processing. 62 (12): pp. 3143-3152.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2014.2321731
    ISSN
    1053-587X
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/29075
    Collection
    • Curtin Research Publications
    Abstract

    In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy. © 2014 IEEE.

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