Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this article, the measurements in a sliding window are, therefore, utilized to better distinguish them, and an adaptive method is embedded, leading to a sliding window variational outlier-robust Kalman filter based on Student’s t-noise modeling. Target tracking simulations and experiments show that the tracking accuracy and consistency of the proposed filter are superior to those of the existing state-of-the-art outlier-robust methods thanks to the improved ability to identify the outliers but at a cost of greater computational burden.


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    Titel :

    A Sliding Window Variational Outlier-Robust Kalman Filter Based on Student’s t-Noise Modeling


    Beteiligte:
    Zhu, Fengchi (Autor:in) / Huang, Yulong (Autor:in) / Xue, Chao (Autor:in) / Mihaylova, Lyudmila (Autor:in) / Chambers, Jonathon (Autor:in)


    Erscheinungsdatum :

    01.10.2022


    Format / Umfang :

    3750364 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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