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    Assessment of vessel route information use in Bayesian non-linear filtering

    Access Status
    Fulltext not available
    Authors
    Battistello, G.
    Ulmke, M.
    Papi, Francesco
    Podt, M.
    Boers, Y.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Battistello, G. and Ulmke, M. and Papi, F. and Podt, M. and Boers, Y. 2012. Assessment of vessel route information use in Bayesian non-linear filtering, in 15th International Conference on Information Fusion, FUSION 2012, pp. 447-454.
    Source Title
    15th International Conference on Information Fusion, FUSION 2012
    ISBN
    9780982443859
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/16655
    Collection
    • Curtin Research Publications
    Abstract

    Bayesian non-linear filtering is considered in this paper for the state vector estimation of manoeuvring targets at sea. Innovative schemes based on the Extended Kaiman Filter and the Particle Filter are derived by the introduction of a priori vessel route information. Such contextual input drives the selection of the manoeuvre model to be used for target state prediction. This aims at coping with significant measurement gaps suffered by coastal sensors - due to their limited spatial coverage or temporal revisit. The capabilities of the context-aided techniques are assessed for realistic scenarios that include typical vessel manoeuvres. The Kullback-Leibler Divergence is adopted as performance metric. The analysis demonstrates that the use of the a priori information yields dramatic improvements in highly non-linear conditions for target tracking, and the Particle Filter outperforms the Extended Kaiman filter approach in the exploitation of the route information. © 2012 ISIF (Intl Society of Information Fusi).

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