Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

TL;DR

This paper introduces a syntactic tracking method using constrained stochastic context free grammar (CSCFG) and a novel particle filtering algorithm to improve anomalous trajectory detection on roadmaps based on natural language processing models.

Contribution

It generalizes previous syntactic tracking work by incorporating CSCFG for better modeling of road-confined patterns and presents a new particle filtering approach for enhanced target pattern estimation.

Findings

Significant improvement in target tracking accuracy with the proposed method.

Effective modeling of road-specific directions and names using CSCFG.

Successful application to simulated GMTI radar data.

Abstract

The aim of syntactic tracking is to classify spatio-temporal patterns of a target's motion using natural language processing models. In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained grammar facilitates modeling specific directions and road names in a roadmap. We present a novel particle filtering algorithm that exploits the CSCFG model for estimating the target's patterns. This meta-level algorithm operates in conjunction with a base-level tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show substantial improvement in target tracking accuracy.