Intent Inference and Syntactic Tracking with GMTI Measurements

TL;DR

This paper introduces syntactic filtering algorithms that analyze target trajectories using stochastic context free grammars and switched mode models to assist human operators in identifying suspicious movement patterns in GMTI radar data.

Contribution

It develops novel Bayesian filtering algorithms for stochastic context free grammars applied to target trajectory analysis in GMTI radar systems.

Findings

Algorithms successfully extract syntactic structures from real radar data.

Enhanced detection of anomalous spatial trajectories.

Validated with experimental data from X-band radar.

Abstract

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).