Bayesian tracking and parameter learning for non-linear multiple target tracking models
Applications
2015-10-28 v1 Computation
Machine Learning
Abstract
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.
Cite
@article{arxiv.1410.2046,
title = {Bayesian tracking and parameter learning for non-linear multiple target tracking models},
author = {Lan Jiang and Sumeetpal S. Singh and Sinan Yıldırım},
journal= {arXiv preprint arXiv:1410.2046},
year = {2015}
}