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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Dunik, Jindrich (Autor:in) / Straka, Ondrej (Autor:in) / Matousek, Jakub (Autor:in) / Brandner, Marek (Autor:in)


    Erscheinungsdatum :

    2021-12-01


    Format / Umfang :

    1976165 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Maximum filter flow predictor

    STEINERT ALAN H | Europäisches Patentamt | 2019

    Freier Zugriff

    Maximum Filter Flow Predictor

    STEINERT ALAN H | Europäisches Patentamt | 2017

    Freier Zugriff


    Weighted Integrate-And-Dump Filter

    Sadr, Ramin | NTRS | 1989