Bayesian metropolis methods applied to sensor networks for radiation source localization

Jason M. Hite; John K. Mattingly; Kathleen L. Schmidt; Razvan Stefanescu; Ralph Smith

doi:10.1109/MFI.2016.7849519

Bayesian metropolis methods applied to sensor networks for radiation source localization

Jason M. Hite, John K. Mattingly, Kathleen L. Schmidt, Razvan Stefanescu, Ralph Smith

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	389-393
Number of pages	5
ISBN (Electronic)	9781467397087
DOIs	https://doi.org/10.1109/MFI.2016.7849519
State	Published - Jul 2 2016
Externally published	Yes
Event	2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016 - Baden-Baden, Germany Duration: Sep 19 2016 → Sep 21 2016

Publication series

Name	IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume	0

Conference

Conference	2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
Country/Territory	Germany
City	Baden-Baden
Period	09/19/16 → 09/21/16

Access to Document

10.1109/MFI.2016.7849519

Cite this

Hite, J. M., Mattingly, J. K., Schmidt, K. L., Stefanescu, R., & Smith, R. (2016). Bayesian metropolis methods applied to sensor networks for radiation source localization. In 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016 (pp. 389-393). Article 7849519 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI.2016.7849519

Hite, Jason M. ; Mattingly, John K. ; Schmidt, Kathleen L. et al. / Bayesian metropolis methods applied to sensor networks for radiation source localization. 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 389-393 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems).

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title = "Bayesian metropolis methods applied to sensor networks for radiation source localization",

abstract = "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.",

author = "Hite, {Jason M.} and Mattingly, {John K.} and Schmidt, {Kathleen L.} and Razvan Stefanescu and Ralph Smith",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016 ; Conference date: 19-09-2016 Through 21-09-2016",

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doi = "10.1109/MFI.2016.7849519",

language = "English",

series = "IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems",

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Hite, JM, Mattingly, JK, Schmidt, KL, Stefanescu, R & Smith, R 2016, Bayesian metropolis methods applied to sensor networks for radiation source localization. in 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016., 7849519, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 389-393, 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016, Baden-Baden, Germany, 09/19/16. https://doi.org/10.1109/MFI.2016.7849519

Bayesian metropolis methods applied to sensor networks for radiation source localization. / Hite, Jason M.; Mattingly, John K.; Schmidt, Kathleen L. et al.
2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 389-393 7849519 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Hite JM, Mattingly JK, Schmidt KL, Stefanescu R, Smith R. Bayesian metropolis methods applied to sensor networks for radiation source localization. In 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 389-393. 7849519. (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems). doi: 10.1109/MFI.2016.7849519

Bayesian metropolis methods applied to sensor networks for radiation source localization

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