Presents the results obtained in our research about application of modern nonlinear filtering techniques to GPS based position estimation. The stand-alone GPS based position estimation problem using GPS pseudo-range and Doppler shifts measurements are described. A model for position and velocity estimation are developed. The model is nonlinear and has variable measurement number for coping with an arbitrary number of satellites. Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. In this work the use of an alternative filter: the unscented Kalman filter (UKF) is proposed. The first experimental results that comprise the comparison of estimation results obtained with a simple model using different filters are then presented. Future research directions are also discussed.


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    Titel :

    Investigation on nonlinear filtering algorithms for GPS


    Beteiligte:
    Mao, X. (Autor:in) / Wada, M. (Autor:in) / Hashimoto, H. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    398705 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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