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    Mixture truncated unscented Kalman filtering

    Access Status
    Fulltext not available
    Authors
    Garcia Fernandez, Angel
    Morelande, M.
    Grajal, J.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Garcia Fernandez, A. and Morelande, M. and Grajal, J. 2012. Mixture truncated unscented Kalman filtering, pp. 479-486.
    Source Title
    15th International Conference on Information Fusion, FUSION 2012
    ISBN
    9780982443859
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/63304
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi).

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