Conditional-mean estimation via jump-diffusion processes inmultiple target tracking/recognition
Miller, M.I.; Srivastava, A.; Grenander, U.
Signal Processing, IEEE Transactions on
Volume 43, Issue 11, Nov 1995 Page(s):2678 - 2690
Digital Object Identifier 10.1109/78.482117
Summary: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
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