Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution

Automatically recognizing intra-pulse modulation of radar signals is a significant survival technique in electronic intelligence systems. To avoid the dependence on feature selection and realize the intelligent intra-pulse modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper develops a novel intra-pulse modulation recognition method based on the high-order spectrums of radar signals. Automatic soft thresholding is implemented in the deep residual network to adaptively eliminate redundant information in the process of feature learning and improve the learning effect of valuable features in distribution images of corresponding third-order spectrums. The extensive simulations compared with the other four methods further reveal the excellent classification performance of the proposed method. The proposed approach still achieves an overall probability of successful recognition of 93.5% for eight kinds of modulation signals, even when the SNR is just -8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

Automated summary

The study introduces a novel radar intra-pulse modulation recognition method based on high-order spectrums, which uses automatic soft thresholding to improve learning effectiveness in the feature learning process. The method demonstrates excellent classification performance and robustness under low signal-to-noise ratios.