Network Detection of Radiation Sources Using Localization-Based Approaches

Chase Qishi Wu; Mark Lee Berry; Kayla Marie Grieme; Satyabrata Sen; Nageswara S.V. Rao; Richard R. Brooks; Guthrie Cordone

doi:10.1109/TII.2019.2891253

Network Detection of Radiation Sources Using Localization-Based Approaches

Chase Qishi Wu, Mark Lee Berry, Kayla Marie Grieme, Satyabrata Sen, Nageswara S.V. Rao, Richard R. Brooks, Guthrie Cordone

Autonomous and Complex Systems

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

Radiation source detection is an important 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 propose 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 illustrate 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.

Original language	English
Article number	8604088
Pages (from-to)	2308-2320
Number of pages	13
Journal	IEEE Transactions on Industrial Informatics
Volume	15
Issue number	4
DOIs	https://doi.org/10.1109/TII.2019.2891253
State	Published - Apr 2019

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

Manuscript received September 13, 2018; accepted December 24, 2018. Date of publication January 7, 2019; date of current version April 3, 2019. This work was supported in part by the U.S. Department of Homeland Security, Domestic Nuclear Detection Office, under competitively awarded contract No. IAA HSHQDC-13-X-B0002, in part by the Mathematics of Complex, Distributed, Interconnected Systems Program, Office of Advanced Computing Research, U.S. Department of Energy, and in part by Oak Ridge National Laboratory managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Paper no. TII-18-2392. (Corresponding author: Chase Qishi Wu.) C. Q. Wu, M. L. Berry, and K. M. Grieme are with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail:, [email protected]; [email protected]; kmg38@njit. edu).

Funders	Funder number
U.S. Department of Energy	DE-AC05-00OR22725
U.S. Department of Homeland Security
Oak Ridge National Laboratory
Domestic Nuclear Detection Office	IAA HSHQDC-13-X-B0002
UT-Battelle	TII-18-2392
Office of Advanced Research Computing, Rutgers, The State University of New Jersey

Keywords

Radiation source detection (RSD)
sensor networks
source localization

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10.1109/TII.2019.2891253

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abstract = "Radiation source detection is an important 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 propose 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 illustrate 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.",

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AU - Brooks, Richard R.

AU - Cordone, Guthrie

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Network Detection of Radiation Sources Using Localization-Based Approaches

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