An enhanced empirical wavelet transform for noisy and non-stationary signal processing

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• 作者：Yue Hu ^{huyue_sjtu@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Fucai Li ; ^{fcli@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Hongguang Li ^{hgli@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chengliang Liu ^{chlliu@sjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Adaptive filtering ; Empirical wavelet transform ; Spectrum segmentation ; Non-stationary signal ; Spectrum shape
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：220-229
• 全文大小：1831 K

文摘

As an alternative method of empirical mode decomposition (EMD), the empirical Wavelet transform (EWT) method was proposed to realize the signal decomposition by constructing an adaptive filter bank. Though the EWT method has been demonstrated its effectiveness in some applications, it becomes invalid in analyzing some noisy and non-stationary signals due to its improper segmentation in the frequency domain. In this paper, an enhanced empirical wavelet transform method is proposed. This method takes advantage of the waveform in the frequency domain of a signal to eliminate drawbacks of the EWT method in the spectrum segmentation. It modifies the segmentation algorithm by adopting the envelope approach based on the order statistics filter (OSF) and applying criteria to pick out useful peaks. With these measures, the proposed method obtains a perfect segmentation in decomposing noisy and non-stationary signals. Furthermore, simulated and experimental signals are used to verify the effectiveness of the proposed method.