Abstract
The effects of data measurement on source parameter estimation are studied. The concept of mutual information is applied to find the optimal location for each sensor to improve accuracy of the overall estimation process. For validation purposes, an advection - diffusion simulation code, called SCIPUFF, is used as a modeling testbed to study the effects of using dynamic data measurement. Bayesian inference framework is utilized for model-data fusion using stationary and mobile sensor networks, where in mobile sensors, the proposed approach is used to locate data observation sensors. As our numerical simulations show, using the proposed approach leads to a considerably better estimate of parameters comparing with stationary sensors.
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This material is based upon work supported by the National Science Foundation under award number CMMI- 1054759 and AFOSR grant number FA9550-11-1-0012.
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Madankan, R., Singla, P., Singh, T. (2015). Parameter Estimation of Atmospheric Release Incidents Using Maximal Information Collection. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_28
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DOI: https://doi.org/10.1007/978-3-319-25138-7_28
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