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

Syntactic tracking aims to classify a target's spatio-temporal trajectory by using natural language processing models. This paper proposes constrained stochastic context-free grammar (CSCFG) models for target trajectories confined to a roadmap. We present a particle filtering algorithm that exploits the CSCFG model structure to estimate the target's trajectory. This metalevel algorithm operates in conjunction with a base-level target tracking algorithm. Extensive numerical results using simulated ground moving target indicator radar measurements show useful improvement in both trajectory classification and target state (both coordinates and velocity) estimation.