Research Article: Identifying anomalous nuclear radioactive sources using Poisson kriging and mobile sensor networks

Date Published: May 1, 2019

Publisher: Public Library of Science

Author(s): Jifu Zhao, Zhe Zhang, Clair J. Sullivan, Tayyab Ikram Shah.


Nuclear security is a critical concept for public health, counter-terrorism efforts, and national security. Nuclear radioactive materials should be monitored and secured in near real-time to reduce potential danger of malicious usage. However, several challenges have arose to detect the anomalous radioactive source in a large geographical area. Radiation naturally occurs in the environment. Therefore, a non-zero level of radiation will always exist with or without an anomalous radioactive source present. Additionally, radiation data contain high levels of uncertainty, meaning that the measured radiation value is significantly affected by the velocity of the detector and background noise. In this article, we propose an innovative approach to detect anomalous radiation source using mobile sensor networks combined with a Poisson kriging technique. We validate our results using several experiments with simulated radioactive sources. As results, the accuracy of the model is extremely high when the source intensity is high or the anomalous source is close enough to the detector.

Partial Text

Nuclear weapons, bombs, as well as radiological dispersal devices are threats to national security and human health. However, detecting anomalous radioactive sources over a large geographical area has several challenges. First, radiation naturally occurs in the ground, building materials, and cosmic rays. Therefore, a non-zero level of radiation will always exist, which presents the problem of detecting a radioactive source with a low signal-to-noise ratio (SNR). Here, the radiation source is the anomalous radiation signal and the background radiation is the noise.

In this research work, two types of data were generated: background radiation data and radioactive source data. Background radiation data were measured using radiation detectors and radioactive source data were simulated using GADRAS (Gamma Detector Response and Analysis Software, developed and maintained by Sandia National Laboratories [30, 31]).

The goal of the project is to detect anomalous radioactive sources (e.g., nuclear bombs or weapons) using mobile sensor networks. There are several innovations involved in this research work. The data streams that are collected through continuous interaction between time and space require real-time (or near real-time) analytics and response. In contrast, most spatial analysis methods and computing framework have not pursued this goal, thus reducing their effectiveness in decision support contexts and motivating research conducted to improve the performance of data-intensive geospatial analysis. In this research work, we developed an intelligent mobile sensor network, in which the radiation streaming data collected using mobile sensors in every second are automatically transferred to the cloud in the form of geo-tagged streaming data in near real-time. In the second step, we developed a novel spatial algorithm based on Poisson kriging to detect the anomalous radiation source. We have conducted experiments with simulated radioactive sources to test the proposed method’s performance. The results indicate that the proposed algorithm can correctly capture the spatial distribution of nuclear radiation levels and find the anomalous radiation source with extremely high accuracy under certain conditions.




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