  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.
