Roadmap Enhanced Improvement to the VSIMM Tracker via a Constrained Stochastic Context Free Grammar
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.
ArXiv