Implementation and simulation of mobile sensor networks for nuclear radiation detection

Zhao, Jifu

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

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

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

Copyright and License Information

Copyright 2019 Jifu Zhao

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