A recognition method of radio fuze signal based on supervised contrastive learning

Frequency modulated continuous wave (FMCW) Doppler fuze is easy to be interfered with on the battlefield, resulting in an early explosion and loss of damage ability. To improve the anti-jamming ability of FMCW Doppler fuze against information-based jamming and realize the distinction between multiple jamming signals and target echoes, this paper proposed a method of target and jamming signal classification and recognition based on supervised contrastive learning. Firstly, the backbone network was constructed by residual network and self-attention mechanism. Then, the contrastive learning loss function was improved by introducing labels, and supervised contrastive learning was realized. Finally, an intermediate frequency signal was used to build the dataset, and the network was trained by supervised comparative learning, so as to realize the classification and recognition of the target and jamming signal. The simulation results show that this method can realize the recognition of multiple jamming types and target echoes, and the recognition rate can reach 98.7%. In the low signal-to-noise ratio (SNR) environment, the recognition effect is better. In the SNR environment of −18 dB, the recognition rate is still 91.81%, which is higher than the 86.12% recognition rate of ordinary residual networks.