Modulation classification method for frequency modulation signals based on the time-frequency distribution and CNN

作者：Zhang, Juan; Li, Yong; Yin, Junping^*

来源：IET Radar Sonar and Navigation, 2018, 12(2): 244-249.

DOI：10.1049/iet-rsn.2017.0265

• 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

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.

• 出版日期2018-2
• 单位北京应用物理与计算数学研究所; 北京师范大学