Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition

Abstract

A new algorithm is presented for generating the conditional mean estimates of functions of target positions, orientations and type in recognition, and tracking of an unknown number of targets and target types. Taking a Bayesian approach, a posterior measure is defined on the tracking/target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model, and a prior based on airplane dynamics. The Newtonian force equations governing rigid body dynamics are utilized to form the prior density on airplane motion. The conditional mean estimates are generated using a random sampling algorithm based on jump-diffusion processes for empirically generating MMSE estimates of functions of these random target positions, orientations, and type under the posterior measure. Results are presented on target tracking and identification from an implementation of the algorithm on a networked Silicon Graphics workstation and DECmpp/MasPar parallel machine.