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

Lan Jiang, Sumeetpal Singh, Sinan Yildirim

Research output: Contribution to journalArticle (Academic Journal)

12 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number1053-587X
Pages (from-to)5733-5745
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume63
Issue number21
DOIs
Publication statusPublished - 9 Jul 2015

Keywords

  • Multi-target tracking
  • Particle Markov Chain Monte Carlo
  • Particle Gibbs
  • model learning
  • state estimation
  • reversible jump MCMC

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