Bayesian tracking and parameter learning for non-linear multiple target tracking models
This addresses tracking challenges in domains like surveillance or robotics, but it appears incremental as it builds on existing MCMC methods for MTT.
The paper tackled the problem of tracking multiple targets in non-linear non-Gaussian models by proposing a Bayesian algorithm for state estimation and parameter learning, demonstrating significant performance improvements compared to competing techniques.
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.