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    Kullback–Leibler divergence approach to partitioned update Kalman filter

    Access Status
    Fulltext not available
    Authors
    Raitoharju, M.
    Garcia Fernandez, Angel
    Piché, R.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Raitoharju, M. and Garcia Fernandez, A. and Piché, R. 2017. Kullback–Leibler divergence approach to partitioned update Kalman filter. Signal Processing. 130: pp. 289-298.
    Source Title
    Signal Processing
    DOI
    10.1016/j.sigpro.2016.07.007
    ISSN
    0165-1684
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/54513
    Collection
    • Curtin Research Publications
    Abstract

    Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of the second order extended Kalman filter, so that it can be used with any Kalman filter extension such as the unscented Kalman filter. To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of the measurement, which is theoretically more sound than the nonlinearity measure used in the original partitioned update Kalman filter. Results show that the use of the proposed partitioned update filter improves the estimation accuracy.

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