基于EEMD和模糊阈值的去噪方法

为了提高EEMD分解中噪声主导模态的去噪效果,利用模糊隶属度的优势,提出了一种EEMD和模糊阈值相结合的去噪方法。首先用二范数计算各个本征模态函数(IMF)与观测信号的概率密度函数(PDF)之间的相似度,得到噪声主导的IMF;然后对噪声主导的IMF进行模糊阈值处理,以去除IMF中的噪声;最后将所有的IMF重构得到消噪信号。分别采用仿真信号和ECG信号进行去噪实验,结果均表明,所提方法的去噪效果整体上优于小波半软阈值方法和基于EMD的间隔阈值(EMD-IT)方法。

EEMD and fuzzy threshold based noise suppression

In order to improve the denoising effect of the noise dominant mode in EEMD decomposition, we propose a novel denoising method combining the EEMD and fuzzy threshold by using fuzzy membership degree. Firstly, the similarity between the intrinsic density function (IMF) and the probability density function (PDF) of the observed signals is calculated using the two norms, and the noise-dominated IMF is obtained. Then, the noise-dominated IMF is subjected to fuzzy threshold processing and hence the noise is removed from the IMF. Finally, all of the remained IMFs are reconstructed to get noise suppression signals. Simulation experiments are conducted by using both suppositional and ECG signals. The results show that the denoising effect of the proposed method is better than that of the wavelet half-soft threshold method and the EMD-based interval threshold (EMD-IT) method.