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Title: Network Detection of Radiation Sources Using Localization-Based Approaches

Abstract

Radiation source detection is a critical problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We introduce the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results demonstrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [2]; ORCiD logo [2];  [3];  [3]
  1. New Jersey Inst. of Technology, Newark, NJ (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Clemson Univ., SC (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1546549
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Industrial Informatics
Additional Journal Information:
Journal Volume: 15; Journal Issue: 4; Journal ID: ISSN 1551-3203
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Radiation source detection (RSD); sensor networks; source localization

Citation Formats

Wu, Chase Qishi, Berry, Mark Lee, Grieme, Kayla Marie, Sen, Satyabrata, Rao, Nageswara S. V., Brooks, Richard R., and Cordone, Guthrie. Network Detection of Radiation Sources Using Localization-Based Approaches. United States: N. p., 2019. Web. doi:10.1109/TII.2019.2891253.
Wu, Chase Qishi, Berry, Mark Lee, Grieme, Kayla Marie, Sen, Satyabrata, Rao, Nageswara S. V., Brooks, Richard R., & Cordone, Guthrie. Network Detection of Radiation Sources Using Localization-Based Approaches. United States. doi:10.1109/TII.2019.2891253.
Wu, Chase Qishi, Berry, Mark Lee, Grieme, Kayla Marie, Sen, Satyabrata, Rao, Nageswara S. V., Brooks, Richard R., and Cordone, Guthrie. Mon . "Network Detection of Radiation Sources Using Localization-Based Approaches". United States. doi:10.1109/TII.2019.2891253.
@article{osti_1546549,
title = {Network Detection of Radiation Sources Using Localization-Based Approaches},
author = {Wu, Chase Qishi and Berry, Mark Lee and Grieme, Kayla Marie and Sen, Satyabrata and Rao, Nageswara S. V. and Brooks, Richard R. and Cordone, Guthrie},
abstractNote = {Radiation source detection is a critical problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We introduce the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results demonstrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.},
doi = {10.1109/TII.2019.2891253},
journal = {IEEE Transactions on Industrial Informatics},
number = 4,
volume = 15,
place = {United States},
year = {2019},
month = {1}
}

Journal Article:
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This content will become publicly available on January 7, 2020
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