This article deals with the Bayesian state estimation of the nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Chapman–Kolmogorov equation, which governs the prediction step of the point-mass filter and predictor, using the convolution. A novel density-weighted convolution is proposed, which provides an accurate predictive probability density function even for models with small state noise, where the standard solution fails. Two implementations of the solution are proposed, theoretically analyzed, and evaluated in a numerical study.


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    Title :

    Accurate Density-Weighted Convolution for Point-Mass Filter and Predictor


    Contributors:


    Publication date :

    2021-12-01


    Size :

    1976165 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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