This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer-Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and standard deviation; consistency test) of the following nonlinear filters is compared: the extended Kalman filter (EKF), the. statistical linearization, the particle filtering, and the unscented Kalman filter (UKF). The simulation results favor the EKF; it combines the statistical efficiency with a modest computational load. This conclusion is valid when the target ballistic coefficient is a priori known.


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    Titel :

    Tracking a ballistic target: comparison of several nonlinear filters


    Beteiligte:
    Farina, A. (Autor:in) / Ristic, B. (Autor:in) / Benvenuti, D. (Autor:in)


    Erscheinungsdatum :

    2002-07-01


    Format / Umfang :

    1070097 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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