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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    Title :

    Stochastic Path Prediction using the Unscented Transform with Numerical Integration


    Contributors:


    Publication date :

    2007-09-01


    Size :

    1336024 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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