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).
Intent Inference and Syntactic Tracking with GMTI Measurements
IEEE Transactions on Aerospace and Electronic Systems ; 47 , 4 ; 2824-2843
01.10.2011
3451051 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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