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    Gaussian MAP Filtering Using Kalman Optimization

    Access Status
    Fulltext not available
    Authors
    García-Fernández, Angel
    Svensson, Lennart
    Date
    2015
    Type
    Journal Article
    
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    Citation
    García-Fernández, A. and Svensson, L. 2015. Gaussian MAP Filtering Using Kalman Optimization. IEEE Transactions on Automatic Control. 60 (5): pp. 1336-1349.
    Source Title
    IEEE Transactions on Automatic Control
    DOI
    10.1109/TAC.2014.2372909
    ISSN
    0018-9286
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/6909
    Collection
    • Curtin Research Publications
    Abstract

    © 1963-2012 IEEE. This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents.

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