局部均值分解与经验模式分解的对比研究

Comparison between the methods of local mean decomposition and empirical mode decomposition

介绍了一种新的非平稳信号分析方法——局部均值分解(Localmean decomposition,简称LMD)。LMD方法可以自适应地将任何一个复杂信号分解为若干个具有一定物理意义的PF(Product function)分量之和,其中每个PF分量为一个包络信号和一个纯调频信号的乘积,从而获得原始信号完整的时频分布。本文首先介绍了LMD方法,然后将LMD方法对仿真信号进行了分析,取得了满意的效果,最后将其和经验模式分解EMD(Empiricalmode decomposition)方法进行了对比,结果表明在端点效应、迭代次数等方面LMD方法要优于EMD方法。

A new nonstationary signal analysis method,namely,the local mean decomposition(LMD) was introduced.By using LMD,any complicated signal can be decomposed into a set of product functions whose instantaneous frequencies own certain physical sense and each of which is a product of an envelope signal and a purely frequency modulated signal,thereby the complete time-frequency distribution of the original signal can be obtained.In the paper,the simulated signal was analyzed by LMD and satisfied results have been obtained.The LMD method was then compared with EMD method and the results indicate that LMD is superior to EMD in some respects,such as in boundary effect and iterative times.