文章基本信息

标题：A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy
本地全文：下载
作者：Yu-xing Li ; Shang-bin Jiao ; Xiang Gao 等
期刊名称：Defence Technology
印刷版ISSN：2214-9147
出版年度：2021
卷号：17
期号：5
页码：1625-1635
DOI：10.1016/j.dt.2020.09.001
语种：English
出版社：Elsevier B.V.
摘要：AbstractFeature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.
关键词：Feature extraction;Empirical mode decomposition;Empirical wavelet transform;Permutation entropy;Reverse dispersion entropy