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    Variational Bayesian cubature kalman filter for bearing-only tracking in glint noise environment

    Variational Bayesian cubature kalman filter for bearing-only tracking in glint noise environment


    The problem of state estimation of nonlinear systems in glint noise environment is considered that will deteriorate the tracking performance. In this paper, a cubature kalman filter algorithm based on variational Bayesian learning is proposed. The t-logical-scale distribution is used to model the glint noise and the Gamma distribution is as the prior distribution of the three parameters of the t-logical-scale distribution. The cubature kalman filter and the variational Bayesian learning are combined to estimate the state of system and parameters in the recursive procedure. The simulation results demonstrate that the proposed algorithm has a stronger robustness and a lower computation.

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    Titel :

    Variational Bayesian cubature kalman filter for bearing-only tracking in glint noise environment

    Beteiligte:
    Tianli Ma, (Autor:in) / Xinmin Wang, (Autor:in) / Rong Xie, (Autor:in) / Qi Bian, (Autor:in)
    Erschienen in:

    2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC) ; 232-237

    Erscheinungsdatum :

    2016-08-01

    Format / Umfang :

    102581 byte

    ISBN :

    978-1-4673-8318-9 , 978-1-5090-0165-1

    DOI :

    https://doi.org/10.1109/CGNCC.2016.7828789

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

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