Journal
IET RADAR SONAR AND NAVIGATION
Volume 12, Issue 2, Pages 244-249Publisher
WILEY
DOI: 10.1049/iet-rsn.2017.0265
Keywords
feature extraction; time-frequency analysis; frequency modulation; neural nets; computer vision; modulation classification method; frequency modulation signals; time-frequency distribution; signal modulation classification; blind modulation classification method; convolutional neural network; time-frequency map; CNN-based algorithm; computer vision area; signal recognition; feature extraction strategy
Funding
- National Natural Science Foundation of China [U1430103, 11601150]
- Fundamental Research Funds for the Central Universities
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Signal modulation classification is an important research subject in both military and civilian field. This study proposed a novel blind modulation classification method based on the time-frequency distribution and convolutional neural network (CNN). This is the first attempt to treat the time-frequency map as a picture and use an outstanding (CNN-based) algorithm in computer vision area for signal recognition. The combination offers a novel feature extraction strategy, to some extent, which also conforms to intuition. Simulation results show that the method proposed in this study is efficient and robust and enables a high degree of automation for extracting features, training weights and making decisions. Additionally, a remarkable performance emerges with small samples and repeated training, which distinguishes this method from many other classification methods.
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