We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.


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    Titel :

    Non-linear filtering based on observations from Gaussian processes


    Beteiligte:
    Gustafsson, F (Autor:in) / Saha, S (Autor:in) / Orguner, U (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2011


    Format / Umfang :

    1086578 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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