经验模态分解(Empirical Mode Decomposition,简称EMD)法是美籍华人N. E. Huang等人于1998年提出的，适合于分析非线性、非平稳信号序列，具有很高的信噪比。该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数(Intrinsic Mode Function，简称IMF)，所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。-Empirical Mode Decomposition method (Empirical Mode Decomposition, the EMD for short) is a chinese-american n. e. Huang et al., in 1998, is suitable for analyzing nonlinear and non-stationary signal sequence, has the very high signal-to-noise ratio.Empirical Mode decomposition is a key to this method, it can make the complex signal is decomposed into a finite number of Intrinsic Mode Function (the Intrinsic Mode Function, the IMF), the decomposition of each IMF component contains the original signals of the local characteristics of different time scales.