Radar signal recognition method based on improved residual neural network
In the case of low signal to noise ratio,radar signal feature extraction is difficult,resulting in low recognition accuracy,a radar signal modulation recognition method based on improved residual neural network is proposed.Firstly,the time-frequency analysis method is used to transform the time-domain signal into a two-dimensional time-frequency image.Then,the image is preprocessed by graying,Gaussian filtering,bilinear interpolation,normalization,etc.,as the input of the deep learning model.Finally,an improved residual neural network is built,which uses space and channel reconstruction units to reduce feature redundancy and improve feature extraction accuracy,thereby improving radar signal recognition accuracy under low signal to noise ratio.Simulation results show that when the signal to noise ratio is-8 dB,the overall recognition accuracy of the proposed method for 12 typical radar signals reaches 96.67％,which has good noise robustness and anti-confusion ability.
emitter signal recognitiontime-frequency analysisdeep learningresidual neural network
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