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).
@article{arxiv.1104.4376,
title = {Intent Inference and Syntactic Tracking with GMTI Measurements},
author = {Alex Wang and Vikram Krishnamurthy and Bhashyam Balaji},
journal= {arXiv preprint arXiv:1104.4376},
year = {2011}
}