  We propose a new Bayesian tracking and parameter learning algorithm for
non-linear non-Gaussian multiple target tracking (MTT) models. We design a
Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior
distribution of the target states, birth and death times, and association of
observations to targets, which constitutes the solution to the tracking
problem, as well as the model parameters. In the numerical section, we present
performance comparisons with several competing techniques and demonstrate
significant performance improvements in all cases.
