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Title: Adaptively Reevaluated Bayesian Localization (ARBL). A Novel Technique for Radiological Source Localization

Abstract

Here we present a novel technique for the localization of radiological sources in urban or rural environments from an aerial platform. The technique is based on a Bayesian approach to localization, in which measured count rates in a time series are compared with predicted count rates from a series of pre-calculated test sources to define likelihood. Furthermore, this technique is expanded by using a localized treatment with a limited field of view (FOV), coupled with a likelihood ratio reevaluation, allowing for real-time computation on commodity hardware for arbitrarily complex detector models and terrain. In particular, detectors with inherent asymmetry of response (such as those employing internal collimation or self-shielding for enhanced directional awareness) are leveraged by this approach to provide improved localization. Our results from the localization technique are shown for simulated flight data using monolithic as well as directionally-aware detector models, and the capability of the methodology to locate radioisotopes is estimated for several test cases. This localization technique is shown to facilitate urban search by allowing quick and adaptive estimates of source location, in many cases from a single flyover near a source. In particular, this method represents a significant advancement from earlier methods like full-field Bayesian likelihood,more » which is not generally fast enough to allow for broad-field search in real time, and highest-net-counts estimation, which has a localization error that depends strongly on flight path and cannot generally operate without exhaustive search« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Seattle, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1224505
Report Number(s):
PNNL-SA-103899
Journal ID: ISSN 0168-9002; 400913000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment; Journal Volume: 784; Journal Issue: 1
Country of Publication:
United States
Language:
English
Subject:
07 ISOTOPE AND RADIATION SOURCES; aerial search; radiological search; gamma detection; localization; maximum likelihood; bayesian

Citation Formats

Miller, Erin A., Robinson, Sean M., Anderson, Kevin K., McCall, Jonathon D., Prinke, Amanda M., Webster, Jennifer B., and Seifert, Carolyn E. Adaptively Reevaluated Bayesian Localization (ARBL). A Novel Technique for Radiological Source Localization. United States: N. p., 2015. Web. doi:10.1016/j.nima.2015.01.038.
Miller, Erin A., Robinson, Sean M., Anderson, Kevin K., McCall, Jonathon D., Prinke, Amanda M., Webster, Jennifer B., & Seifert, Carolyn E. Adaptively Reevaluated Bayesian Localization (ARBL). A Novel Technique for Radiological Source Localization. United States. doi:10.1016/j.nima.2015.01.038.
Miller, Erin A., Robinson, Sean M., Anderson, Kevin K., McCall, Jonathon D., Prinke, Amanda M., Webster, Jennifer B., and Seifert, Carolyn E. Mon . "Adaptively Reevaluated Bayesian Localization (ARBL). A Novel Technique for Radiological Source Localization". United States. doi:10.1016/j.nima.2015.01.038.
@article{osti_1224505,
title = {Adaptively Reevaluated Bayesian Localization (ARBL). A Novel Technique for Radiological Source Localization},
author = {Miller, Erin A. and Robinson, Sean M. and Anderson, Kevin K. and McCall, Jonathon D. and Prinke, Amanda M. and Webster, Jennifer B. and Seifert, Carolyn E.},
abstractNote = {Here we present a novel technique for the localization of radiological sources in urban or rural environments from an aerial platform. The technique is based on a Bayesian approach to localization, in which measured count rates in a time series are compared with predicted count rates from a series of pre-calculated test sources to define likelihood. Furthermore, this technique is expanded by using a localized treatment with a limited field of view (FOV), coupled with a likelihood ratio reevaluation, allowing for real-time computation on commodity hardware for arbitrarily complex detector models and terrain. In particular, detectors with inherent asymmetry of response (such as those employing internal collimation or self-shielding for enhanced directional awareness) are leveraged by this approach to provide improved localization. Our results from the localization technique are shown for simulated flight data using monolithic as well as directionally-aware detector models, and the capability of the methodology to locate radioisotopes is estimated for several test cases. This localization technique is shown to facilitate urban search by allowing quick and adaptive estimates of source location, in many cases from a single flyover near a source. In particular, this method represents a significant advancement from earlier methods like full-field Bayesian likelihood, which is not generally fast enough to allow for broad-field search in real time, and highest-net-counts estimation, which has a localization error that depends strongly on flight path and cannot generally operate without exhaustive search},
doi = {10.1016/j.nima.2015.01.038},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = 1,
volume = 784,
place = {United States},
year = {Mon Jan 19 00:00:00 EST 2015},
month = {Mon Jan 19 00:00:00 EST 2015}
}