Tracking of multiple contaminant clouds

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.