Estimating a CBRN atmospheric release in a complex environment using Gaussian Processes

Adrien Ickowicz 1 François Septier 1 Patrick Armand 2
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : In this paper, we present a new methodology for the estimation and the prediction of the concentration of pollutant in a complex environment. We take benefit of a semi-parametric formulation of the problem to perform a faster and more efficient estimation of the pollutant cloud. In a first part, we present how we use the Gaussian process to model the interactions between position and time given the observations. Then, we introduce the expansion as a function of the observations through the time, and we construct an estimator of the time of release from it within change-point detection framework. Then, we use this time estimate to obtain the position (or more likely, a confidence region of the position) of the source. Several simulations are provided in a complex city scenario that demonstrate the accuracy of the proposed technique.
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Submitted on : Monday, July 30, 2012 - 11:12:58 AM
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  • HAL Id : hal-00721740, version 1



Adrien Ickowicz, François Septier, Patrick Armand. Estimating a CBRN atmospheric release in a complex environment using Gaussian Processes. Proc. Int. Conf. on Information Fusion (FUSION 2012), Jul 2012, Singapore, Singapore. pp.1846-1853. ⟨hal-00721740⟩



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