The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper will illustrate several positioning and target tracking applications, solved using the marginalized particle filter. Furthermore, we analyze several properties of practical importance, such as its computational complexity and how to cope with quantization effects.


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    Title :

    The marginalized particle filter in practice


    Contributors:
    Schon, T.B. (author) / Karlsson, R. (author) / Gustafsson, F. (author)

    Published in:

    Publication date :

    2006-01-01


    Size :

    4173037 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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