A Blind Source Separation Method Using Denoising Strategy Based on ICEEMDAN and Improved Wavelet Threshold. (28th February 2022)

• Record Type: Journal Article
• Authors: Feng, Lu; Li, Jiong; Li, Changqing; Liu, Yang
• Other Names: Crippa Paolo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-02-28
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/3035700
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Traditional blind source separation (BSS) methods often want no additional noise effects. But in practice, noise is ubiquitous, and there are even cases with low signal-to-noise ratios (SNR). Unfortunately, these blind source separation methods have a significant impact on system performance in the presence of noise. This study proposes a blind source separation method to eliminate noise and extract signal information to the maximum extent. The method is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and improved wavelet threshold (IWT) hierarchical decomposition to improve the signal separation effect. First, the signal is decomposed via ICEEMDAN to obtain the noise-dominated and information-dominated intrinsic mode function (IMF) components. Then, the noise-dominated IMF components are again decomposed by ICEEMDAN, and each component's scale function is obtained via detrended fluctuation analysis (DFA). Next, the noise-dominated IMF components generated in the second decomposition round undergo an improved wavelet threshold denoising to obtain useful signal information. Finally, the information-dominated IMF components generated in the two rounds of decomposition and the resulting signal of wavelet denoising yield a reconstructed signal, which is then decomposed using the BSS algorithm to obtain the source signal. The experimental results demonstrate that the proposed method can accurately extract signal information and can better separate the source signal than the existing algorithm.