Title :
Tracking of multiple contaminant clouds
Author :
Septier, François ; Carmi, Avishy ; Godsill, Simon
Author_Institution :
Eng. Dept., Cambridge Univ., Cambridge, UK
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;
Conference_Titel :
Information Fusion, 2009. FUSION '09. 12th International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
978-0-9824-4380-4
