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Title:Implementation and simulation of mobile sensor networks for nuclear radiation detection
Author(s):Zhao, Jifu
Director of Research:Uddin, Rizwan
Doctoral Committee Chair(s):Uddin, Rizwan
Doctoral Committee Member(s):Abbaszadeh, Shiva; Brunner, Robert J.; Huff, Kathryn D.; Sullivan, Clair J.
Department / Program:Nuclear, Plasma, & Rad Engr
Discipline:Nuclear, Plasma, Radiolgc Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Nuclear radiation detection
Mobile sensor network
Anomaly detection
Source localization
Abstract:From preventing the threat of nuclear weapons proliferation to monitoring the transportation of special nuclear materials, nuclear radiation detection plays an important role in national security applications. However, changing background radiation, shielding effects, and short collection time make radiation detection a challenging problem. Anomaly detection, source localization, and isotope identification are three major parts of radiation detection. The concept of mobile radiation sensor networks, which utilize multiple mobile radiation detectors, has been proposed to solve these problems. This work mainly focuses on developing and testing methodologies for anomaly detection and radioactive source localization using mobile sensor networks. A collection of techniques and analyses for radiation detection are presented and evaluated. More specifically, in this work, a mobile sensor network simulation system is first developed to simulate the scenario where multiple radiation detectors move around a city. Based on the simulated data, the performance characteristics of mobile sensor networks for radiation detection are studied and quantified. Next, focusing on geospatial modeling of radiation count data, Poisson kriging is proposed to estimate the background radiation level and perform anomalous source detection. The proposed method is validated using simulated source data injected in measured background radiation data and results indicate that the proposed algorithm can detect the anomalous radiation source with 90% accuracy under certain conditions. Additionally, source localization techniques based on maximum likelihood estimation are explored in detail. Simulation and experimental results show that source localization error can be reduced to be within 3 meters. Lastly, an exploratory study of spectrum-based anomaly detection techniques is presented. The performance of different machine learning techniques is evaluated and compared using simulated radiation data.
Issue Date:2019-04-17
Type:Text
URI:http://hdl.handle.net/2142/105205
Rights Information:Copyright 2019 Jifu Zhao
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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