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This paper proposes a new Bayesian tracking and parameter learning algorithm for non-linear and non-Gaussian multiple target tracking (MTT) models. A Markov chain Monte Carlo (MCMC) algorithm is designed 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. The numerical section presents performance comparisons with several competing techniques and demonstrates significant performance improvements in all cases.

Bayesian Tracking and Parameter Learning for Non-Linear Multiple Target Tracking Models