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    Square root receding horizon information filters for nonlinear dynamic system models

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Jeon, M.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Kim, D.Y. and Jeon, M. 2013. Square root receding horizon information filters for nonlinear dynamic system models. IEEE Transactions on Automatic Control. 58 (5): pp. 1284-1289.
    Source Title
    IEEE Transactions on Automatic Control
    DOI
    10.1109/TAC.2012.2223352
    ISSN
    0018-9286
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55620
    Collection
    • Curtin Research Publications
    Abstract

    New nonlinear filtering algorithms are designed based on a receding horizon strategy, i.e., a finite impulse response (FIR) structure, and square root information filtering to achieve high accuracy and good performance in empirical error covariance tests. The new nonlinear receding horizon filters reduce approximation errors in nonlinear filtering by considering a set of recent observations with non-informative initial conditions. By applying information filtering, we are able to manage the non-informative initial conditions, and thus propose the square root version of the algorithm as a means of retaining the positive definiteness of the error covariance. Based on the proposed strategy, we then implement known nonlinear filtering frameworks. Simulation results confirm that the new nonlinear receding horizon filters outperform existing nonlinear filters in well-known nonlinear examples. © 2012 IEEE.

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