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

Abstract

In order to improve the de-noising effect of the pulsar signal, an empirical mode decomposition (EMD) denoising algorithm based on predicting of noise mode cell is put forward. The core of the proposed method is as follows: firstly, the noisy pulsar signal is decomposed into a group intrinsic mode function (IMF) by EMD, and predicting the noise mode cell according to the IMF coefficients statistics and local minimum mean square error criteria, the selected noise mode cells are set to zero; Then the IMF which was processed according to noise mode cell prediction is denoised by optimal mode cell proportion shrinking, so as to the aim for removing the noise and retaining the signal details .experimental results show that, compared to the SureShrink wavelet threshold algorithm, BayesShrink wavelet threshold algorithm and the EMD mode cell proportion shrinking algorithm, the proposed method performances better 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 and relative error of the peak value.