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Proceedings Paper

Experience and lessons learned from the Army RCO Blind Signal Classification Competition
Author(s): Peng Wang; Manuel Vindiola; John S. Hyatt; Michael S. Lee
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Paper Abstract

The Army Rapid Capabilities Office (RCO) sponsored a Blind Signal Classification Competition seeking algorithms to automatically identify the modulation schemes of RF signal from complex-valued IQ (in-phase quadrature) samples. Traditional spectrum sensing technology uses energy detection to detect the existence of RF signals but the RCO competition further aimed to detect the modulation scheme of signals without prior information. Machine Learning (ML) technologies have been widely used for blind signal classification problem. Traditional ML methods usually have two stages where the first stage is to manually extract the features of the IQ symbols by subject matter experts and the second stage is to feed the features to an ML algorithm (e.g., a support vector machine) to develop the classifier. The state-of-art technology is to apply deep learning technologies such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) directly to the complex-value IQ symbols to train a multi-class classifier. Our team, dubbed Deep Dreamers, participated in the RCO competition and placed 3rd out of 42 active teams across industry, academia, and government. In this work we share our experience and lessons learned from the competition. Deep learning methods such as CNN, Residual Neural Network (ResNet), and Long Short-Term Memory (LSTM) are the fundamental neural network layers we used to develop a multi-class classifier. None of our individual models were able to achieve a competitively high ranking in the competition. The key to our success was to use ensemble learning to average the outputs of multiple diverse classifiers. In order for ensemble methods to be more accurate than any of its base models; the base learners have to be as accurate as possible. We found that while ResNet was more accurate than the LSTM; the LSTM was less sensitive to deviations in the test set.

Paper Details

Date Published: 10 May 2019
PDF: 13 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061J (10 May 2019); doi: 10.1117/12.2518447
Show Author Affiliations
Peng Wang, U.S. Army Research Lab. (United States)
Manuel Vindiola, U.S. Army Research Lab. (United States)
John S. Hyatt, U.S. Army Research Lab. (United States)
Michael S. Lee, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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