This paper illustrates that the combination of the unscented transform and numerical integration can provide significantly more accurate stochastic predictions of the future state of a nonlinear system for potentially less computation time than similar Kalman-like routines. Within the context of this paper, this improvement is shown in the vehicular path prediction environment, where computation power and memory are kept at an affordable level.


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    Titel :

    Stochastic Path Prediction using the Unscented Transform with Numerical Integration


    Beteiligte:
    Caveney, Derek (Autor:in)


    Erscheinungsdatum :

    01.09.2007


    Format / Umfang :

    1336024 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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