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

Journal Article


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.

Authors


  •   Huang, Chunfeng (external author)
  •   Hsing, Tailen (external author)
  •   Cressie, Noel A.
  •   Ganguly, Auroop (external author)
  •   Protopopescu, Vladmir (external author)
  •   Rao, Nageswara (external author)

Publication Date


  • 2010

Citation


  • Huang, C., Hsing, T., Cressie, N. A., Ganguly, A. R., Protopopescu, V. A. & Rao, N. S. (2010). Bayesian source detection and parameter estimation of a plume model based on sensor network measurements. Applied Stochastic Models in Business and Industry, 26 (4), 331-348.

Scopus Eid


  • 2-s2.0-77956373165

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2280

Number Of Pages


  • 17

Start Page


  • 331

End Page


  • 348

Volume


  • 26

Issue


  • 4

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.

Authors


  •   Huang, Chunfeng (external author)
  •   Hsing, Tailen (external author)
  •   Cressie, Noel A.
  •   Ganguly, Auroop (external author)
  •   Protopopescu, Vladmir (external author)
  •   Rao, Nageswara (external author)

Publication Date


  • 2010

Citation


  • Huang, C., Hsing, T., Cressie, N. A., Ganguly, A. R., Protopopescu, V. A. & Rao, N. S. (2010). Bayesian source detection and parameter estimation of a plume model based on sensor network measurements. Applied Stochastic Models in Business and Industry, 26 (4), 331-348.

Scopus Eid


  • 2-s2.0-77956373165

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2280

Number Of Pages


  • 17

Start Page


  • 331

End Page


  • 348

Volume


  • 26

Issue


  • 4