A Multiframe Bayesian Algorithm for Detecting Random Signature Targets in Correlated Gauss-Markov Clutter

"We introduce in this paper an optimal Bayesian algorithm for integrated detection and tracking of Gaussian targets that move randomly in cluttered environments. We model the background clutter as a noncausal Gauss-Markov random process and incorporate the statistical descriptions of the clutter, target signature, and target motion into the design of the detector/tracker. The optimal detection performance is quantified using Monte Carlo simulations."