An important foundation for various vehicular applications is a reliable environment recognition. In this context, the simultaneous estimation of the state and the existence of an unknown number of objects under difficult detection conditions is a particular challenge. In this paper, we propose an algorithm for tracking extended objects under clutter. We propose an extended measurement model which enables the estimation of the object width using a standard Kalman filter implementation without the need for clustering the data. As this implies multiple observations generated by one object and additional clutter observations, the generalized probabilistic data association with a state-depended cardinality model is utilized. The proposed algorithm is evaluated with simulated data of a radar-based vehicle tracking system.


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    Titel :

    Radar-based extended object tracking under clutter using generalized probabilistic data association


    Beteiligte:
    Adam, Christian (Autor:in) / Schubert, Robin (Autor:in) / Wanielik, Gerd (Autor:in)


    Erscheinungsdatum :

    2013-10-01


    Format / Umfang :

    357388 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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