Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN
Juan Zhang, Yong Li, Junping Yin · IET Radar Sonar & Navigation · 2017
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.