  In conventional target tracking systems, human operators use the estimated
target tracks to make higher level inference of the target behaviour/intent.
This paper 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 stochastic context free
grammars 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).
