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    Set-membership PHD filter

    Access Status
    Fulltext not available
    Authors
    Benavoli, A.
    Papi, Francesco
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Benavoli, A. and Papi, F. 2013. Set-membership PHD filter, 16th International Conference on Information Fusion (FUSION), Jul 9-12 2013, pp. 1722-1729. Istanbul, Turkey: IEEE .
    Source Title
    Proceedings of the 16th International Conference on Information Fusion, FUSION 2013
    Additional URLs
    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6641211&newsearch=true&queryText=Set-membership%20PHD%20filter
    ISBN
    9786058631113
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/37691
    Collection
    • Curtin Research Publications
    Abstract

    The paper proposes a novel Probability Hypothesis Density (PHD) filter for linear system in which initial state, process and measurement noises are only known to be bounded (they can vary on compact sets, e.g., polytopes). This means that no probabilistic assumption is imposed on the distributions of initial state and noises besides the knowledge of their supports. These are the same assumptions that are used in set-membership estimation. By exploiting a formulation of set-membership estimation in terms of set of probability measures, we derive the equations of the set-membership PHD filter, which consist in propagating in time compact sets that include with guarantee the targets' states. Numerical simulations show the effectiveness of the proposed approach and the comparison with a sequential Monte Carlo PHD filter which instead assumes that initial state and noises have uniform distributions.

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