Date of Award

12-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering (Holcomb Dept. of)

Committee Member

Dr. Carl Baum, Committee Chair

Committee Member

Dr. Harlan Russell

Committee Member

Dr. Robert Schalkoff

Abstract

Finding illicit nuclear sources in an urban environment plays an important role in reconnaissance research for domestic security. This work focuses on the problem of estimating the locations of radioactive sources that potentially exist in a three-dimensional search space using a network of sensors. The design of an estimation system to provide consistent surveillance is considered using drones equipped with sensors, Global Position System (GPS) devices and basic communication capabilities. In many environments, sensors cannot be placed in close proximity to the source, which makes estimating the location of a nuclear source especially difficult. In this work, we apply maximum-likelihood estimation (MLE) to estimate a radioactive point source using radiation data modeled as a Poisson process. The joint multivariate density for the sensors is maximized in order to estimate the location and strength of the radioactive source. In contrast to previous work, this work models background radiation levels as unknown, and the background radiation level is estimated simultaneously with the location and strength of the source. Two models for the background radiation are considered, uniform radiation and radiation that emanates from a diffuse source on the ground. It is shown that if data is collected from a sufficient number of locations, estimating the background radiation levels introduces little degradation over the idealistic performance that would result if the background radiation levels were perfectly known.

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