Bayesian smoothing and filtering for multiframe, multiaspect target detection and tracking

We introduce a new Bayesian algorithm for joint multiframe detection and tracking of multiaspect targets that move randomly in cluttered digital image sequences. Two versions of the algorithm are derived: a batch Bayes smoother and an on-line Bayes filter. Performance results with a simulated image sequence generated from real infrared airborne radar (IRAR) data show an improvement over the association of a bank of correlation detectors and a Kalman-Bucy tracker in a scenario with a heavily cluttered multiaspect target.