Research on UAV RF Signal Recognition Method Based on Deep Learning

In the field of unmanned aerial vehicle (UAV) signal recognition based on deep learning, complex electromagnetic environments pose severe challenges. Among them, interference suppression and recognition robustness under low signal-to-noise ratio (SNR) conditions are major research focuses. To tackle these issues, this paper proposes a robust radio frequency (RF) signal recognition method for UAVs. The method is built on an improved ResNet18 network. It separates the video transmission signals and control signals of UAVs, and adopts mixed-SNR training. These designs greatly strengthen the model’s ability to perform reliable recognition. First, the short-time Fourier transform (STFT) is used to convert UAV I/Q signals into time-frequency images. A refined time-frequency masking strategy is then adopted. This strategy separates and suppresses interference from control signals according to their bandwidth differences from video transmission signals, thus improving signal quality. Second, a squeeze-and-excitation (SE) channel attention module is embedded into each residual block of ResNet18. This structure enables the model to concentrate on critical features. The classification head is also rebuilt into a three-layer fully connected network to strengthen nonlinear transformation and complex feature interaction. Given the performance variation of the model across different SNR levels, a mixed-SNR training strategy is adopted. Fine grid search experiments are conducted to identify the optimal data distribution for maximizing recognition performance. Experimental results on the public DroneRFb-DIR dataset show that the improved ResNet18 model achieves a 3.07% increase in classification accuracy. The time-frequency masking method brings an extra performance gain of 1.08%. Although the mixed-SNR strategy causes a slight accuracy drop at the high SNR of 20 dB, it improves recognition accuracy by 8% to 53.01% within the low-SNR range of −10 dB to 10 dB. It also effectively reduces misclassification between different UAV types and enhances the model’s robustness. The proposed method provides a feasible solution for accurate and stable UAV signal recognition in complex electromagnetic scenarios.