Tracking of multiple contaminant clouds

François Septier, Avishy Carmi, Simon Godsill

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

In this paper, we address the problem of detection and tracking of multiple contaminant clouds. We develop a stochastic extension of the Gaussian puff model to characterize evolution of the average atmospheric pollutant concentration. To perform the sequential inference on this difficult problem, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm. Numerical simulations illustrate the ability of the algorithm to detect and track multiple contaminant clouds.

Original languageEnglish
Title of host publication2009 12th International Conference on Information Fusion, FUSION 2009
Pages1280-1287
Number of pages8
StatePublished - 18 Nov 2009
Externally publishedYes
Event2009 12th International Conference on Information Fusion, FUSION 2009 - Seattle, WA, United States
Duration: 6 Jul 20099 Jul 2009

Publication series

Name2009 12th International Conference on Information Fusion, FUSION 2009

Conference

Conference2009 12th International Conference on Information Fusion, FUSION 2009
Country/TerritoryUnited States
CitySeattle, WA
Period6/07/099/07/09

Keywords

  • Bayesian inference
  • Contaminant cloud
  • Environmental imaging
  • Sequential MCMC
  • Tracking

Fingerprint

Dive into the research topics of 'Tracking of multiple contaminant clouds'. Together they form a unique fingerprint.

Cite this