Lie group parametrization for dynamics based prior in ATR

In recent automated target recognition (ATR) literature, there is increasing utilization of motion dynamics for tracking and recognizing moving targets. The dynamics along with the sensor models provide a Bayesian framework for conditional mean estimation of scene parameters. Previously, the authors have presented a random sampling algorithm for empirical generation of estimates based on jump-diffusion processes. Here we describe a different parameterization for simplifying the derivation of a more informative prior, from Newtonian mechanics, on the target configurations