一种改进型经验模态分解及其在信号消噪中的应用

针对非线性非平稳信号的消噪问题, 基于经验模态分解提出一种模态单元滤波新方法. 该方法将经验模态分解的内模函数中两个相邻过零点之间的信号定义为模态单元, 并以之作为基本分析对象, 通过对模态单元振幅的阈值处理来判断模态单元的类型, 进而建立模态单元滤波模型. 分析了经验模态分解法在分解不同Hurst指数分形高斯噪声时模态振幅的演化规律, 并建立了一种用于高斯消噪的阈值选取规则. 为验证本文方法的有效性, 进行了数字仿真与实例应用实验. 仿真和实验结果均表明, 所提方法的消噪效果整体上优于最优小波阈值消噪方法, 同时模态单元滤波消噪算法具有自适应性, 是一种有效的信号消噪新方法.

A Modified Empirical Mode Decomposition Method with Applications to Signal De-noising

In order to solve the problem of nonlinear and nonstationary signal de-noising, a novel mode cell filtering (MCF) method was proposed based on empirical mode decomposition (EMD). The method defined the signal between the two adjacent zero-crossings within intrinsic mode function (IMF) of EMD as a mode cell, and treated the mode cell as the basic analyzable object. The mode cells were sorted by judging the amplitudes of the mode cells, and then the mode cell's filtering model was established. Evolutional rules of the amplitude of the mode cell were analyzed when the noisy signal corrupted by fractional Gaussian noise with different Hurst exponent were decomposed by EMD method, and threshold choosing rules used in Gaussian de-noising were also established. Numerical simulation and real data test were carried out to evaluate the performance of the method. Simulation and test results showed that the proposed method outperformed the optimal wavelet threshold de-noising algorithm in whole, so it is a novel effective signal de-noising method with virtue of self-adaption.