| Citation: | HA H，GAO X，YAO X J，et al. Signal modulation waveform recognition method based on STF-Net[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（9）：3150-3160 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0467
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Signal modulation waveform recognition is one of the key technologies in the field of spectrum cognition and an important means to achieve monitoring and control of spectrum resources for low-orbit satellites. To address the issues of high parameter count and computational complexity in existing deep learning-based modulation waveform recognition methods, a lightweight signal modulation waveform recognition method based on space-time fusion network (STF-Net) is proposed. The method first preprocesses the signals into dual-channel data in the time-frequency domain. It then utilizes convolutional neural network (CNN) to extract signal spatial features and reduce feature redundancy. Long short-term memory (LSTM) is employed to capture temporal information and output recognition results. Experimental results show that the proposed method achieves an average recognition accuracy of 91.79% for modulation waveforms when the signal-to-noise ratio is greater than 0dB. Compared with equivalent methods, the proposed method reduces the parameter count by 96% and improves efficiency by 2.7 times.
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