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.

By: J. Hite n, J. Mattingly n, K. Schmidt n, R. Stelanescu & R. Smith n

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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
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.