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 

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
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.