In view of the problem that ensemble empirical mode decomposition (EEMD) threshold denoising method was difficult to achieve ideal results

an improved complementary ensemble empirical mode decomposition–multi-resolution singular value decomposition (CEEMD–MRSVD) denoising method was proposed. Noisy signal was processed by CEEMD

which overcame the shortcomings of poor timeliness of EEMD and EMD mode mixing. Then

the signal-dominated and noise-dominated components were distinguished by the correlation characteristics of signal and noise. According to the different noise intensity

a multi-resolution singular value location was proposed to strategically optimize the noise-dominated and signal-dominated components. Finally

the Savitzky–Golay smoothing filter was used to remove the rough details of the signal and the extracted signals were reconstructed to achieve the denoising purpose. Results showed that

this method can not only eliminated noise interference effectively

but also reduced the loss of useful details.