Date of Award

12-2020

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Joseph A. Curro, PhD

Abstract

The goal of this thesis is to determine the efficacy of employing Machine Learning (ML) to solve Joint Urgent Operational Need (JUON) CC-0575, which aims to develop a Common Operating Picture (COP) of the Global Positioning System (GPS) Electromagnetic Interference (EMI) environment. With the growing popularity of Artificial Neural Networks (ANNs), ML solutions are quickly gaining traction in businesses, academia and government. This in turn allows for problem solutions that were previously inconceivable using the classical programming paradigm. This thesis proposes a method to develop a COP of the battlefield via ANN ingestion of multiple-source signals and sensors. We conduct three separate experiments with varying amounts of EMI interference sources (single, double, and triple jammer datasets). The type of ANN developed to address this problem is a Conditional Neural Process (CNP) with residual connections. The model is developed to provide the estimated EMI environment as well as a measure of confidence in its estimates, as the specific application of this model could lead to loss of life in the event the model estimates are taken as truth. The model resulted in an EMI estimator that was neutral on the single jammer test data set, yet aggressive on the multiple jammer test data sets.

AFIT Designator

AFIT-ENG-MS-20-D-006

DTIC Accession Number

AD1126994

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