In conventional target tracking systems, human operators use the estimated target tracks to make higher level inference of the target behaviour/intent. The work presented here develops syntactic filtering algorithms that assist human operators by extracting spatial patterns from target tracks to identify suspicious/anomalous spatial trajectories. The targets' spatial trajectories are modeled by a stochastic context free grammar (SCFG) and a switched mode state space model. Bayesian filtering algorithms for SCFGs are presented for extracting the syntactic structure and illustrated for a ground moving target indicator (GMTI) radar example. The performance of the algorithms is tested with the experimental data collected using DRDC Ottawa's X-band Wideband Experimental Airborne Radar (XWEAR).


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    Titel :

    Intent Inference and Syntactic Tracking with GMTI Measurements


    Beteiligte:
    Wang, A. (Autor:in) / Krishnamurthy, V. (Autor:in) / Balaji, B. (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    3451051 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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