Empirical mode decomposition pulsar signal denoising method based on predicting of noise mode cell

In order to improve the de-noising effect of the pulsar signal, an empirical mode decomposition (EMD) denoising algorithm based on the prediction of noise mode cell is put forward. The core steps of the proposed method is as follows: firstly, the noisy pulsar signal is decomposed into a group intrinsic mode function (IMF) by EMD, and the noise mode cell is predicted according to the IMF coefficients statistics and local minimum mean square error criteria. The selected noise mode cells are set to be zero. Then the IMF which has been processed according to noise mode cell prediction is denoised by optimal mode cell proportion shrinking, for removing the noise and retaining the signal details. The experimental results show that compared with the Sure Shrink wavelet threshold algorithm, Bayes Shrink wavelet threshold algorithm and the EMD mode cell proportion shrinking algorithm, the proposed method performs well in removing the pulsar signal noise and retaining the signal details information. The proposed method can achieve a higher signal-to-noise, the lower root mean square error, error of the peak position, relative error of the peak value and phase error.