基于EMD和SST算法的闪电电场信号去噪研究

为进一步减少噪声对闪电电场信号的干扰,提出了一种经验模态分解(EMD)和同步压缩小波变换(SST)相结合的组合去噪方法。利用EMD算法能够自适应分解信号和SST算法可将噪声压缩为点状噪声或颗粒状噪声并集中分布的特点,从而选用中值滤波达到对噪声的抑制。利用该方法对标准闪电波和自然闪电波信号分别进行去噪处理,并运用信噪比、相关系数和均方误差对去噪效果进行了定量分析。实验结果表明,所提去噪方法相比于传统小波阈值去噪法、单独用EMD算法和单独用SST算法均取得了较好的去噪效果。

Denoising of Lightning Electric Field Signals Based on EMD and SST Algorithms

In order to reduce the interference of noise on lightning electric field signals,a denoising method combining empirical mode decomposition (EMD) and synchrosqueezing wavelet transform (SST) is proposed.The EMD algorithm can decompose the signals adaptively and the SST algorithm can compress the noise into concentrated point noise or granular noise,so the median filter is selected to suppress the noise.Using this method,the standard lightning wave and the natural lightning wave are denoised separately,and the denoising effect is analyzed quantitatively by using the signal-to-noise ratio,correlation coefficient and mean square error.The experimental results show that the proposed denoising method achieves better denoising results than the traditional wavelet threshold denoising,EMD algorithm alone,or SST algorithm alone.