@article{HA2025, 
author = {Hui HA and Xiang GAO and Xiujuan YAO and Jiangyin FU and Wei LI and Xiaoyan ZHANG},
title = {Signal modulation waveform recognition method based on STF-Net},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {9},
pages = {3150-3160},
keywords = {modulation waveform recognition, deep learning, STF-Net, CNN, LSTM},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0467},
doi = {10.13700/j.bh.1001-5965.2023.0467},
abstract = {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.}
}