Clutter adaptive tracking of multiaspect targets in IRAR imagery

We present in this paper a clutter adaptive, multiframe Bayesian algorithm for joint detection and tracking of a multi-aspect target in cluttered image sequences. The target template is randomly translated, rotated, scaled and sheared from frame to frame. Tracking performance studies with a sequence generated from real data infrared airbone radar (IRAR) imagery show a reduction in the steady-state position estimation error and in the target acquisition time when the Bayes detector/tracker is compared to the association of a bank of matched filter detectors and a linearized Kalman-Bucy tracker.