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    Adaptive unscented Gaussian likelihood approximation filter

    Access Status
    Fulltext not available
    Authors
    Garcia Fernandez, Angel
    Morelande, M.
    Grajal, J.
    Svensson, L.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Garcia Fernandez, A. and Morelande, M. and Grajal, J. and Svensson, L. 2015. Adaptive unscented Gaussian likelihood approximation filter. Automatica. 54: pp. 166-175.
    Source Title
    Automatica
    DOI
    10.1016/j.automatica.2015.02.005
    ISSN
    0005-1098
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/54227
    Collection
    • Curtin Research Publications
    Abstract

    This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman filter (UKF) is not and the other way round. As a result, we propose the adaptive UGLAF (AUGLAF), which selects the best approximation to the posterior (UKF or UGLAF) based on the Kullback-Leibler divergence. This enables AUGLAF to outperform both the UKF and UGLAF.

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