Machine Learning (ML) for Signal Detection
Loading...
Authors
Kragh, Frank E.
Miller, Donna L.
Subjects
Signals Intelligence
SIGINT
Communications Intelligence
COMINT
neural networks
machine learning
ML
generative adversarial networks
radio communications
SIGINT
Communications Intelligence
COMINT
neural networks
machine learning
ML
generative adversarial networks
radio communications
Advisors
Date of Issue
2021
Date
2021
Publisher
Monterey, California: Naval Postgraduate School
Monterey, California. Naval Postgraduate School.
Monterey, California. Naval Postgraduate School.
Language
en_US
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 and generative adversarial networks (GANs) to classify received signals based on their down-converted (but not demodulated) in-phase and quadrature (I&Q) samples and evaluate their probability of being of interest. The approach will use a generative adversarial network (GAN) to train a discriminator neural network that will determine the likelihood that a received signal is of interest. The discriminator can then be used to identify signals of interest as they are received.
Type
Poster
Description
NPS NRP Project Poster
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
Naval Special Warfare Command (NAVSPECWARCOM)
N2/N6 - Information Warfare
N2/N6 - Information Warfare
Funding
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
Citation
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.