Signal modulation waveform recognition method based on STF-Net

Signal modulation waveform recognition is one of the key technologies in the field of spatial spectrum cognition and an important means to realize the monitoring and control of spectrum resources of low-orbit satellites. In view of the problems of large number of parameters and high computational complexity in the current modulation waveform recognition methods based on deep learning, a lightweight signal modulation waveform recognition method based on space-time fusion network (STF-Net) is proposed. The signal is preprocessed into dual-channel data in the form of time domain and frequency domain, and the signal spatial features are extracted and feature redundancy is reduced by convolutional neural network (CNN). Then, the long short-term memory network (LSTM) is used to extract the timing information and output the recognition result. The experimental results show that when the signal-to-noise ratio is greater than 0 dB, the average recognition accuracy of the modulation waveform of the proposed method reaches 91.79%; compared with the same method, the number of parameters of the proposed method is reduced 96%and the efficiency is increased by 2.7 times.