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    Distributed fusion of multitarget densities and consensus PHD/CPHD filters

    Access Status
    Fulltext not available
    Authors
    Battistelli, G.
    Chisci, L.
    Fantacci, C.
    Farina, A.
    Mahler, Ronald
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Battistelli, G. and Chisci, L. and Fantacci, C. and Farina, A. and Mahler, R. 2015. Distributed fusion of multitarget densities and consensus PHD/CPHD filters.
    Source Title
    Proceedings of SPIE - The International Society for Optical Engineering
    DOI
    10.1117/12.2176948
    ISBN
    9781628415902
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55333
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 SPIE. The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network.

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