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Clutter adaptive multiframe detection/tracking of random signature targets

This paper develops the two-dimensional (2D) clutter adaptive, multiframe Bayes detector/tracker for targets with random signature. We model the background Clutter and the target signature as samples of two independent, spatially correlated, 2D noncausal Gauss-Markov random fields (GMrfs). The target's motion is modeled by a 2D hidden Markov model (HMM). We study, through Monte Carlo simulations, the performance of the adaptive multiframe detector/tracker, and show that the performance of the adaptive tracker is very close to the performance of the tracker when the clutter model is perfectly known.