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    Nonlinear filtering update phase via the single point truncated unscented Kalman filter

    Access Status
    Fulltext not available
    Authors
    Garcia Fernandez, Angel
    Morelande, M.
    Grajal, J.
    Date
    2011
    Type
    Conference Paper
    
    Metadata
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    Citation
    Garcia Fernandez, A. and Morelande, M. and Grajal, J. 2011. Nonlinear filtering update phase via the single point truncated unscented Kalman filter.
    Source Title
    Fusion 2011 - 14th International Conference on Information Fusion
    ISBN
    9781457702679
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/53626
    Collection
    • Curtin Research Publications
    Abstract

    A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kahnan filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter. © 2011 IEEE.

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