@article{c18b504b32754fe8ae9ef0800ea27006,
title = "Network Detection of Radiation Sources Using Localization-Based Approaches",
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.",
keywords = "Radiation source detection (RSD), sensor networks, source localization",
author = "Wu, {Chase Qishi} and Berry, {Mark Lee} and Grieme, {Kayla Marie} and Satyabrata Sen and Rao, {Nageswara S.V.} and Brooks, {Richard R.} and Guthrie Cordone",
note = "Funding Information: 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:, chase.wu@njit.edu; mlb32@njit.edu; kmg38@njit. edu). Funding Information: 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. Publisher Copyright: {\textcopyright} 2018 IEEE.",
year = "2019",
month = apr,
doi = "10.1109/TII.2019.2891253",
language = "English (US)",
volume = "15",
pages = "2308--2320",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "4",
}