Since impulse noise has obvious pulse characteristic,the traditional denoising methods based on Gaussian hypothesis can't effectively filter impulse noise.To solve this problem,a LFM signal denoising method based on DCNN under impulsive noise environment is proposed.Firstly,the LFM signal and random impulse noise are generated,and the data sets with different general-ized SNR are constructed and input into DCNN network for training and testing.Furthermore,the denoising ability of the model is verified from three aspects:time-domain waveform,FRFT spectrum and time-frequency distribution.Finally,the denoised LFM signal is subjected to fractional Fourier transform,and the parameters of LFM signal are estimated by searching the peak points in the fractional spectrum.The simulation results show that this method can not only effectively remove the random impulse noise in the noisy signal,but also keep the time domain characteristics,fractional spectrum characteristics and time-frequency characteris-tics of LFM signals,and thus improves the noise robustness of the parameter estimation method.Compared with the traditional methods based on nonlinear transform,this method can still effectively maintain the fractional spectral characteristics and time-fre-quency characteristics of the signal at low GSNR,and has better denoising performance and generalization ability.
关键词
脉冲噪声/深度卷积神经网络/线性调频信号/分数阶傅里叶变换
Key words
impulsive noise/deep convolution neural network/linear frequency modulation signal/fractional Fourier transform
维普
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