A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy
A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy
复制标题
基于经验小波变换和逆色散熵的新型信号特征提取技术
DOI：
10.1016/j.dt.2020.09.0012214-9147
复制
发表时间：
2021-10-01
影响因子：
5.1
通讯作者：
Gao, Xiang
中科院分区：
文献类型：
--
作者：
Li, Yu-xing;Jiao, Shang-bin;Gao, Xiang
特征提取是信号处理的重要组成部分，对信号检测、分类和识别具有重要意义。非线性动力学分析方法可以提取信号的非线性特征，在不同领域有着广泛的应用。我们最近提出的反向色散熵（RDE）作为一种非线性动力学分析方法，具有计算速度快、抗噪能力强等优点，比传统的排列熵（PE）和色散熵（DE）更适合于度量信号的复杂性。基于小波分析理论的经验小波变换（EWT）可以将复杂的非平稳信号分解为多个具有紧致支撑集谱的经验小波函数，具有比经验模式分解（EMD）及其改进算法更好的分解性能。考虑到RDE和EWT的优点，一方面将EWT引入到水声信号处理和故障诊断领域，提高信号分解精度;另一方面将RDE作为EWT的特征，提高信号的可分性和稳定性。最后，本文提出了一种新的基于EWT和RDE的信号特征提取技术。实验结果表明，本文提出的特征提取技术能够有效地提取实际信号的复杂度特征。此外，它还具有更高的区分能力，不同类型的信号比五个最新的特征提取技术。(c)2020中国园艺学会由Elsevier B. V.代表KeAi Communications Co. Ltd.提供的出版服务。这是CC BY-NC-ND许可证（http：//www.example.com licenses/by-nc-nd/4.0/）下的开放获取文章。creativecommons.org/
Feature 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. (c) 2020 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).