Adaptive estimation using multiple model filtering is investigated as a means of changing the field of view as well as the bandwidth of an infrared image tracker when target acceleration can vary over a wide range. The multiple models are created by tuning filters for best performance at differing conditions of exhibited target behavior and differing physical size of their respective fields of view. Probabilistically weighted averaging provides the adaptation mechanism. Each filter involves online identification of the target shape function, so that this algorithm can be used against ill-defined and/or multiple-hot-spot targets. When each individual filter has the form of an enhanced correlator/linear Kalman filter, computational loading is very low. In contrast, an extended Kalman filter processing the raw infrared data directly and assuming a nonlinear constant turn-rate dynamics model provides superior tracking capability, especially for harsh maneuvers, at the cost of a larger computational burden.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Tracker Field-of-View Variation Via Multiple Model Filtering


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.07.1985


    Format / Umfang :

    2300922 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Structural parameter calibration method for multiple field of view star tracker

    Sun, Li / Jiang, Jie / Li, Jian et al. | British Library Online Contents | 2015




    RECURSIVE FILTERING OF STAR TRACKER DATA

    Darling, Jacob E. / Houtz, Nathan / Frueh, Carolin et al. | British Library Conference Proceedings | 2016