University of Illinois Urbana-Champaign

Implementation and simulation of mobile sensor networks for nuclear radiation detection

Zhao, Jifu

Content Files
ZHAO-DISSERTATION-2019.pdf

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https://hdl.handle.net/2142/105205

Description

Title
Implementation and simulation of mobile sensor networks for nuclear radiation detection
Author(s)
Zhao, Jifu
Issue Date
2019-04-17
Director of Research (if dissertation) or Advisor (if thesis)
Uddin, Rizwan
Doctoral Committee Chair(s)
Uddin, Rizwan
Committee Member(s)
  • Abbaszadeh, Shiva
  • Brunner, Robert J.
  • Huff, Kathryn D.
  • Sullivan, Clair J.
Department of Study
Nuclear, Plasma, & Rad Engr
Discipline
Nuclear, Plasma, Radiolgc Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2019-08-23T20:47:29Z
Keyword(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.
Graduation Semester
2019-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/105205
Copyright and License Information
Copyright 2019 Jifu Zhao

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Nuclear, Plasma, and Radiological Engineering