EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs
EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs
复制标题
DOI：
10.1109/tim.2013.2275243
复制
发表时间：
2014-01-01
影响因子：
5.6
通讯作者：
Dare-Emzivat, Delphine
中科院分区：
文献类型：
--
作者：
Komaty, Ali;Boudraa, Abdel-Ouahab;Dare-Emzivat, Delphine
本文将经验模式分解(EMD)和相似性度量相结合，提出了一种新的信号滤波方法。通过EMD将噪声信号自适应地分解成称为本征模式函数的振荡分量，然后估计每个提取模式的概率密度函数(Pdf)。本文的核心思想是利用部分重构，根据输入信号的pdf与每个模式的pdf之间惊人的相似性来选择相关模式。对不同的相似性度量进行了研究和比较。在模拟信号和真实信号上的结果表明，基于pdf的滤波策略对去除高斯白噪声和有色噪声都是有效的，并且其性能优于文献中报道的部分重建方法。
This paper introduces a new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature.