噪声干扰环境下抑制EMD模态混叠方法

经验模态分解(EMD)作为时频分析的经典算法，已经得到广泛的应用。然而，其分解质量容易受到噪声等干扰的影响，产生模态混叠问题。本文针对经验模态分解中因噪声存在的模态混叠问题，提出一种自适应的预处理方法。首先对输入信号进行B样条最小二乘拟合，消除了噪声的影响后，再进行EMD分解。为提高算法的自适应性，提出了一种基于极值点出现时刻的节点选取方法。对线性信号与非线性信号的仿真实验表明该方法有较高的分解精度；与聚合经验模态分解方法(EEMD)的分析对比结果表明该方法能很好地抑制噪声引起的模态混叠。 

Resolving the mode-mixing problem of EMD in the presence of noise

Empirical mode decomposition has become an established tool for time-frequency analysis and has been widely used. However, a major problem is that its performance of EMD may be affected by intermittence or noise, known as the mode-mixing problem. In order to overcome the mode-mixing problem in the empirical mode decomposition (EMD) algorithm, an adaptive pre-processing technique is proposed. In this work, B-spline least squares approximation is first studied and employed before the use of EMD to eliminate the noise which may result in mode mixing. After that, a knot placement iteration algorithm using the extrema time location is put forward to enhance the adaptive property of the proposed method. Simulations of linear and non-linear signals show that it is capable of significantly reducing mode-mixing problem caused by noise. Comparisons between the proposed method and EEMD method are carried out, indicating that the proposed method is superior to existing methods in accuracy.