Kyle Logue
The Aerospace Corporation

Esteban Valles
The Aerospace Corporation

Andres Vila
The Aerospace Corporation

Alex Utter
The Aerospace Corporation

Darren Semmen
The Aerospace Corporation

Eugene Grayver
The Aerospace Corporation

Sebastian Olsen
The Aerospace Corporation

Donna Branchevsky
The Aerospace Corporation

Abstract

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. Feature generation, where raw signal information is transformed prior to attempting classification is a key part of this process. A big question that researchers face is whether to let the deep learning system infer the relevant features or build expert features based on expected signal characteristics. In this paper, we present novel signal feature extraction methods for use in signal classification via ML. The deep learning and combined approaches are discussed in a simultaneous publication. Expert features were utilized via ensemble leaning and shallow neural networks to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. The features include both standard statistical measurements such as variance and kurtosis, as well as measurements tailored for specific waveform families. We discuss the best statistical descriptors along with a ranked list of signal features and discuss individual feature importance. We then demonstrate our implementation of these features and discuss effectiveness in estimating different modulation classes. The methods discussed when combined with deep learning are capable of correctly classifying waveforms at -10 dB SNR with over 63\% accuracy and signals at +10 dB SNR with over 95\% accuracy from an Army RCO provided training set.

Keywords

modulation, feature extraction, neural networks, machine learning, decision trees, wireless communication, signals intelligence, feature importance

DOI

10.25080/Majora-7ddc1dd1-002

Bibtex entry

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Copyright © 2019 The Aerospace Corporation This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.