EMD-Based signal filtering
EMD-Based signal filtering
复制标题
DOI：
10.1109/tim.2007.907967
复制
发表时间：
2007-12-01
影响因子：
5.6
通讯作者：
Cexus, Jean-Christophe
中科院分区：
文献类型：
--
作者：
Boudraa, Abdel-Ouahab;Cexus, Jean-Christophe
本文提出了一种基于经验模态分解的信号滤波方法。过滤方法是一种完全数据驱动的方法。噪声信号自适应分解成固有的振荡成分称为固有模式函数（IMF）的一种算法称为筛选过程的装置。该方法的基本原理是利用信号的部分重构，其中相关IMF对应于信号的最重要结构（低频分量）。提出了一种确定IMF的准则，使信号重要结构的能量分布克服了噪声和信号高频成分的能量分布。模拟和真实的数据的方法进行说明，并将结果与众所周知的过滤方法。该研究仅限于被加性白色高斯噪声破坏的信号，并进行扩展的数值实验的基础上。
In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.