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Secondary Surveillance Radar Signal Processing Based on Two-channel Deep Residual Network

The secondary surveillance radar system is the main surveillance method for national defense and domestic civil aviation. It will be interfered by noise during the transmission process, affecting the clarity of signal transmission, and also leading to a reduction in the stability and reliability of radio transmission. Although the traditional denoising method is effective, there is still a lot of noise remaining, which affects the signal detection. This paper proposes a secondary radar signal processing method based on the two-channel deep residual neural network (2CD-NET), which can effectively extract the deep features of the secondary radar signal and predict the response timing signal. It is verified by experiments that under the normal working noise environment of the secondary radar, the two-channel deep residual neural network has strong noise suppression performance. When the signal-to-noise ratio is 10dB, the signal-to-noise ratio of the test set can reach 99.9S%. When the signal-to-noise ratio is 5dB, the test set accuracy rate can reach 94.71%.