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    Iterated statistical linear regression for Bayesian updates

    Access Status
    Fulltext not available
    Authors
    Garcia Fernandez, Angel
    Svensson, L.
    Morelande, M.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    Garcia Fernandez, A. and Svensson, L. and Morelande, M. 2014. Iterated statistical linear regression for Bayesian updates, 17th International Conference on Information Fusion (FUSION).
    Source Title
    FUSION 2014 - 17th International Conference on Information Fusion
    Source Conference
    17th International Conference on Information Fusion (FUSION)
    ISBN
    9788490123553
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/53890
    Collection
    • Curtin Research Publications
    Abstract

    © 2014 International Society of Information Fusion.This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. In the update step, the sigma-point KF approximations are equivalent to performing the statistical linear regression (SLR) of the (nonlinear) measurement function with respect to the prior PDF. In this paper, we indicate that the SLR of the measurement function with respect to the posterior is expected to provide better results than the SLR with respect to the prior. The resulting filter is referred to as the posterior linearisation filter (PLF). In practice, the exact PLF update is intractable but can be efficiently approximated by carrying out iterated SLRs based on sigma-point approximations. On the whole, the resulting filter, the iterated PLF (IPLF), is expected to outperform all sigma-point KF approximations as demonstrated by numerical simulations.

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