This article derives a nonlinear regression Huber-based divided difference filtering algorithm using a nonlinear regression approach for dynamic state estimation problems with non-Gaussian noises and outliers. In this approach, the nonlinear measurement model is directly used without linear or statistically linear approximation and the Huber-based divided difference filtering problem is solved using a Gauss–Newton approach. This new proposed filter method is then applied to a benchmark problem of estimating the trajectory of an entry body from discrete-time range data measured by a radar tracking station. Simulation results demonstrate the superior performance of the proposed filter as compared to the previous filter algorithms in the presence of non-Gaussian uncertainties.


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    Titel :

    Nonlinear regression Huber-based divided difference filtering


    Beteiligte:


    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.50 Luftfahrzeugtechnik / 55.60 Raumfahrttechnik
    Lokalklassifikation TIB:    275/7040



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