Syntactic track-before-detect

Abstract

In this paper, a track before detect approach utilizing trajectory shape constraints is proposed to track dimly lit targets. The shape of the target trajectory is modeled syntactically using stochastic context-free grammar (SCFG) models that arise in natural language processing. These scale-invariant models are subsequently used in enhancing the track before detect algorithm. Stochastic context-free grammars are a generalization of Markov chains (regular grammars) and can model complex spatial patterns with long range dependencies. A novel particle filter based syntactic tracker is proposed and numerical results are presented to show significant improvement over conventional jump Markov models in track before detect.