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Title: Localization of a radioactive source in an urban environment using Bayesian Metropolis methods [Bayesian Metropolis methods for source localization in an urban environment]

Abstract

Here, we present a method for localizing an unknown source of radiation in an urban environment using a distributed detector network. This method employs statistical parameter estimation techniques, relying on an approximation for the response of a detector to the source based on a simplified model of the underlying transport phenomena, combined with a Metropolis-type sampler that is modified to propagate the effect of fixed epistemic uncertainties in the material macroscopic cross sections of objects in the scene. We apply this technique to data collected during a measurement campaign conducted in a realistic analog for an urban scene using a network of six mobile detectors. Our initial results are able to localize the source to within approximately 8 m over a scene of size 300 m 200 m in two independent trials with 30 min count times, including a characterization of the uncertainty associated with the poorly known macroscopic cross sections of objects in the scene. In these measurements, the nearest detectors were between 20 m to 30 m from the source, and recorded count rates between approximately 3 and 13 times background. A few detectors had line-of-sight to the source, while the majority were obscured by objects present inmore » the scene. After extending our model to account for the orientation of the detectors and correcting for anomalies in the measurement data we were able to further improve the localization accuracy to approximately 2 m in both trials.« less

Authors:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [2];  [2];  [2]; ORCiD logo [2];  [2]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
OSTI Identifier:
1484142
Alternate Identifier(s):
OSTI ID: 1636261
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 915; Journal Issue: C; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Source localization; Bayesian parameter estimation; Sensor networks

Citation Formats

Hite, Jason, Mattingly, John, Archer, Daniel E., Willis, Michael J., Rowe, Andrew J., Bray, Kayleigh B., Carter, Jr., Jake, and Ghawaly, James M. Localization of a radioactive source in an urban environment using Bayesian Metropolis methods [Bayesian Metropolis methods for source localization in an urban environment]. United States: N. p., 2018. Web. doi:10.1016/j.nima.2018.09.032.
Hite, Jason, Mattingly, John, Archer, Daniel E., Willis, Michael J., Rowe, Andrew J., Bray, Kayleigh B., Carter, Jr., Jake, & Ghawaly, James M. Localization of a radioactive source in an urban environment using Bayesian Metropolis methods [Bayesian Metropolis methods for source localization in an urban environment]. United States. https://doi.org/10.1016/j.nima.2018.09.032
Hite, Jason, Mattingly, John, Archer, Daniel E., Willis, Michael J., Rowe, Andrew J., Bray, Kayleigh B., Carter, Jr., Jake, and Ghawaly, James M. Tue . "Localization of a radioactive source in an urban environment using Bayesian Metropolis methods [Bayesian Metropolis methods for source localization in an urban environment]". United States. https://doi.org/10.1016/j.nima.2018.09.032. https://www.osti.gov/servlets/purl/1484142.
@article{osti_1484142,
title = {Localization of a radioactive source in an urban environment using Bayesian Metropolis methods [Bayesian Metropolis methods for source localization in an urban environment]},
author = {Hite, Jason and Mattingly, John and Archer, Daniel E. and Willis, Michael J. and Rowe, Andrew J. and Bray, Kayleigh B. and Carter, Jr., Jake and Ghawaly, James M.},
abstractNote = {Here, we present a method for localizing an unknown source of radiation in an urban environment using a distributed detector network. This method employs statistical parameter estimation techniques, relying on an approximation for the response of a detector to the source based on a simplified model of the underlying transport phenomena, combined with a Metropolis-type sampler that is modified to propagate the effect of fixed epistemic uncertainties in the material macroscopic cross sections of objects in the scene. We apply this technique to data collected during a measurement campaign conducted in a realistic analog for an urban scene using a network of six mobile detectors. Our initial results are able to localize the source to within approximately 8 m over a scene of size 300 m 200 m in two independent trials with 30 min count times, including a characterization of the uncertainty associated with the poorly known macroscopic cross sections of objects in the scene. In these measurements, the nearest detectors were between 20 m to 30 m from the source, and recorded count rates between approximately 3 and 13 times background. A few detectors had line-of-sight to the source, while the majority were obscured by objects present in the scene. After extending our model to account for the orientation of the detectors and correcting for anomalies in the measurement data we were able to further improve the localization accuracy to approximately 2 m in both trials.},
doi = {10.1016/j.nima.2018.09.032},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = C,
volume = 915,
place = {United States},
year = {Tue Sep 25 00:00:00 EDT 2018},
month = {Tue Sep 25 00:00:00 EDT 2018}
}

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Figures / Tables:

Table 1 Table 1: Model parameters.

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