IDENTIFICATION AND CLASSIFICATION OF SIGNALS USING GENERATIVE ADVERSARIAL NETWORKS
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Authors
Ellison, Bart D.
Subjects
generative adversarial network
neural network
deep learning
neural network
deep learning
Advisors
Kragh, Frank E.
Scrofani, James W.
Date of Issue
2021-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
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Abstract
Research has shown that machine learning holds promise as a technique to improve the identification and classification of signals of interest. This study proposes the use of machine learning, specifically generative adversarial networks, to classify received signals based on their down-converted, but not demodulated, in-phase and quadrature signals and evaluate their probability of being of interest. The approach used a generative adversarial network to train a classifier convolutional neural network to determine the likelihood that a received signal is of interest. We tested the ability of a semi-supervised generative adversarial network to classify signals of interest by modulation scheme. We then tested the ability of the semi-supervised generative adversarial network to identify unique signals of interest within a dataset of a single modulation scheme. We evaluated the performance of the network on accuracy, training time, and the amount of data needed to train the network. The results proved that a semi-supervised generative adversarial network could classify a signal by modulation scheme and identify signals within a single modulation scheme.
Type
Thesis
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Series/Report No
Department
Electrical and Computer Engineering (ECE)
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Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.