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Bayesian source detection and parameter estimation of a plume model based on sensor network measurements

✍ Scribed by Chunfeng Huang; Tailen Hsing; Noel Cressie; Auroop R. Ganguly; Vladimir A. Protopopescu; Nageswara S. Rao

Publisher
John Wiley and Sons
Year
2010
Tongue
English
Weight
527 KB
Volume
26
Category
Article
ISSN
1524-1904

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✦ Synopsis

Abstract

We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple‐source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd.

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