Tracking of multiple contaminant clouds

Author

Septier, François ; Carmi, Avishy ; Godsill, Simon

Author_Institution

Eng. Dept., Cambridge Univ., Cambridge, UK

fYear

2009

fDate

6-9 July 2009

Firstpage

1280

Lastpage

1287

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.

Keywords

Gaussian distribution; Markov processes; Monte Carlo methods; air pollution; belief networks; contamination; environmental science computing; image processing; inference mechanisms; numerical analysis; Gaussian puff model; Markov Chain Monte Carlo-based particle algorithm; atmospheric pollutant concentration; contaminant clouds detection; contaminant clouds tracking; multiple contaminant clouds; numerical simulation; sequential inference; Atmosphere; Atmospheric modeling; Bayesian methods; Biological system modeling; Clouds; Evolution (biology); Inference algorithms; Pollution; Signal processing algorithms; Stochastic processes; Bayesian Inference; Tracking; contaminant cloud; environmental imaging; sequential MCMC;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2009. FUSION '09. 12th International Conference on

Conference_Location

Seattle, WA

Print_ISBN

978-0-9824-4380-4

Type

conf

Filename

5203759