Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum
Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum
复制标题
基于希尔伯特边际谱中连续经验模态分解分量分离的趋势提取
DOI：
10.1016/j.measurement.2013.04.071
复制
发表时间：
2013-10-01
期刊：
影响因子：
5.6
通讯作者：
Bingham, Chris
中科院分区：
文献类型：
--
作者：
Yang, Zhijing;Ling, Bingo Wing-Kuen;Bingham, Chris
提取潜在趋势是分析信号的重要工具。提出了一种基于希尔伯特边际谱中连续经验模式分解(EMD)分量分离的信号潜在趋势提取方法。信号最初被表示为通过EMD获得的本征模式函数(IMF)的和。然后计算了每个IMF的希尔伯特边际谱。根据两个连续的IMF在Hilbert边际谱上的相关系数来估计它们的分离。最后几个IMF在希尔伯特边际谱中彼此接近的组将用于表示信号的潜在趋势。文中给出了大量的实验结果，验证了该方法的合理性和有效性。(C)2013爱思唯尔有限公司。保留所有权利。
Extracting the underlying trends is an important tool for the analysis of signals. This paper presents a novel methodology for extracting the underlying trends of signals based on the separations of consecutive empirical mode decomposition (EMD) components in the Hilbert marginal spectrum. A signal is initially represented as a sum of intrinsic mode functions (IMFs) obtained via the EMD. The Hilbert marginal spectrum of each IMF is then calculated. The separations of two consecutive IMFs in the Hilbert marginal spectrum are estimated based on their correlation coefficients. The group of the last several IMFs in which the IMFs are close to each other in the Hilbert marginal spectrum will be used for the representation of the underlying trend of the signal. Extensive experimental results are presented to illustrate the rationale and the effectiveness of the proposed method. (c) 2013 Elsevier Ltd. All rights reserved.