论述了EMD分解的基本原理,研究了利用EMD分解进行信号去噪的方法。EMD把信号按照不同的特征尺度分解为不同频带的IMF分量,将含有噪声的高频IMF分量剔除,选择低频或者指定频带的IMF进行信号重构,即可达到去噪的目的。仿真信号与实测数据的处理结果都表明,该方法不但有效地去除信号中的确定性噪声和随机噪声,而且尽可能地保持了有效信号,减少了信号损失,提高了数据处理的准确性。
The method dealt with in this paper is very suitable for non-stationary nonlinear multi-component signal analysis. This paper discusses the basic principle of EMD, and studies a signal denoising method based on EMD. According to the different feature scales of EMD, the signal is decomposed into different frequency IMF components. Through deleting the high frequency IMF components full of noise and choosing the low frequency or the specified frequency IMF components, the signal can be reconstructed, thus achieving de- noising. The results of simulation signal and measured data both show that the method can not only effectively remove the deterministic or random noise but also preserve the effective signal as much as possible and reduce the signal loss, thus improving the accuracy of da- ta processing.