Automated Target Tracking and Recognition Using Jump-Diffusion Processes.
Michael I. Miller, Anuj Srivastava · 1995
Abstract : This report presents our work, supported under the research grant ARO DAAL03-92-G-0141, on the development of an algorithm for generating the conditional mean estimates of functions of target positions, orientation 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 the 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 of 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 and DECmpp/MasPar parallel machines.