A new feature extraction method for short wave signal

Shortwave signal type recognition plays a very important role in the field of non-cooperative communication, but because of the low signal-to-noise ratio of the shortwave channel, it is often very difficult to identify the shortwave signal type. In this paper, the difference between signal and noise in the wavelet transform is utilized, and the method of correlation denoising is adopted to process the wavelet transform sequence. The correlation denoising wavelet and is taken as the signal characteristics so as to effectively reduce the influence of noise on the signal. Through the experimental performance analysis of 6 kinds of 8PSK signals, the correlation denoising wavelet and feature extraction method proposed in this paper can effectively reduce the impact of noise, and still be effective under the condition of low SNR. Finally, by using the features proposed in this paper as the input of the deep learning network, the recognition accuracy of 98.5% can be achieved when the SNR is 6dB.