CUED Publications database

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

Jiang, L and Singh, SS and Yildirim, S (2015) Bayesian Tracking and Parameter Learning for Non-Linear Multiple Target Tracking Models. IEEE Transactions on Signal Processing, 63. pp. 5733-5745. ISSN 1053-587X

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Abstract

© 1991-2012 IEEE. 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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:40
Last Modified: 20 Nov 2017 20:30
DOI: