Tracking of coordinated groups using marginalised MCMC-based Particle algorithm

François Septier 1 Sze Kim Pang 1 Simon Godsill 1 Avishy Carmi 1
1 Signal Processing Laboratory
Abstract : In this paper, we address the problem of detection and tracking of group and individual targets. In particular, we focus on a group model with a virtual leader which models the bulk or group parameter. To perform the sequential inference, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm with a marginalisation scheme using pairwise Kalman filters. Numerical simulations illustrate the ability of the algorithm to detect and track targets within groups, as well as infer both the correct group structure and the number of targets over time.
Keywords : Kalman filters Markov chain Monte Carlo algorithm coordinated group tracking particle algorithm sequential inference target detection target tracking
Document type :
Conference papers
Complete list of metadatas

Cited literature [25 references]  Display  Hide  Download
https://hal-imt.archives-ouvertes.fr/hal-00566621
Contributor : François Septier <>
Submitted on : Tuesday, April 16, 2013 - 6:16:18 PM
Last modification on : Wednesday, April 17, 2013 - 9:46:54 AM
Long-term archiving on : Wednesday, July 17, 2013 - 2:20:08 AM

File

Article.pdf
Files produced by the author(s)

Identifiers

Collections

INSTITUT-TELECOM

Citation

François Septier, Sze Kim Pang, Simon Godsill, Avishy Carmi. Tracking of coordinated groups using marginalised MCMC-based Particle algorithm. IEEE Aerospace Conference, Mar 2009, Big Sky, MT, United States. pp.1, ⟨10.1109/AERO.2009.4839491⟩. ⟨hal-00566621⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

202

Files downloads

271

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD