2016 conference paper
Bayesian metropolis methods applied to sensor networks for radiation source localization
2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 389–393.
TL;DR:
An application of statistical techniques to the localization of an unknown gamma source in an urban environment is presented and Markov Chain Monte Carlo is applied to generate a full posterior probability density estimating the source location and intensity based on counts reported from a distributed detector network.
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UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
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Source: NC State University Libraries
Added: August 6, 2018
We present an application of statistical techniques to the localization of an unknown gamma source in an urban environment. By formulating the problem as a task of Bayesian parameter estimation, we are able to apply Markov Chain Monte Carlo (MCMC) to generate a full posterior probability density estimating the source location and intensity based on counts reported from a distributed detector network. To facilitate the calibration procedure, we employ a simplified photon transport model with low computational cost and test the proposed methodology in a simulated urban environment, with calibration data generated using the radiation transport code MCNP. The Bayesian methodology is able to identify the source location and intensity along with providing a full posterior density.