Multiframe Bayesian tracking of cluttered targets with random motion

We present in this paper a multiframe Bayesian algorithm for the detection and tracking of heavily cluttered rigid bodies with random translational and rotational motion. Monte Carlo simulations with synthetic targets and clutter show that the proposed algorithm achieves substantial performance gains over the common association of a maximum likelihood position estimator and a linearized Kalman-Bucy filter