A Frequency Identification Method for Differential Frequency-Hopping Signals Based on the Super-Resolution Reconstruction of Time–Frequency Images

The frequency identification technology of differential frequency-hopping (DFH) signals is the key to decoding at the receiver. Aiming to improve frequency identification accuracy under low signal-to-noise ratio (SNR) conditions, a method based on super-resolution image reconstruction technology is proposed for the instantaneous frequency identification of DFH signals. Firstly, the time–frequency image of the DFH signal is obtained using short-time Fourier transform (STFT). Then, a U-Net neural network with an attention mechanism is designed to suppress noise and interference components in the time–frequency image and reconstruct a super-resolution time–frequency image. Furthermore, based on the correlation between adjacent hop signals in accordance with the frequency transfer function, a ResNet neural network is designed to identify frequencies from the super-resolution time–frequency image of DFH signals. Simulation results demonstrate that the designed U-Net neural network can effectively suppress noise and interference components and reconstruct high-quality super-resolution time–frequency images. Comparative experimental results show that the proposed ResNet neural network can significantly improve the identification accuracy of DFH signals under low-SNR conditions. Specifically, the identification accuracy can reach more than 90% when the low SNR is not less than −10 dB, which is a significant improvement compared with other methods. Ablation experiment results indicate that the attention mechanism can improve model performance by 3.74%.