基于EMD和SST算法的闪电电场信号去噪研究
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摘要
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。
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.
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.
引文
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[19]高伟,胡定玉,方宇.采用小波变换和盲源分离的电机轴承故障诊断[J].测控技术,2017,36(5):51-60.
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[2]李鹏,郑毅,张义军.闪电瞬态电场信号波形去噪方法[J].强激光与粒子束,2007,19(12):2055-2059.
[3]高太长,韩小冬,邱实,等.基于多小波变换的电场变化仪信号去噪[J].解放军理工大学学报(自然科学版),2009,7(3):302-306.
[4]刘志田,江志东,李云.基于提升小波的闪电瞬态电场信号去噪[J],气象水文海洋仪器,2013(2).
[5]火元莲,张广庶,吕世华,等.双密度双树小波变换的闪电信号去噪研究[J].红外与激光,2013,43(9):1076-1079.
[6] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceeding:Mathematical, Physical and Engineering Sciences, 1998,454(1971):903-995.
[7] Daubechies I,Lu J,Wu H T. Synchrosqueezed wavelet transforms:an empirical mode decomposition-like tool[J]. Ap-plied and Computational Harmonic Analysis,2011,30(2):243-261.
[8]黄翔.基于EMD重构地震信号的去噪方法[J].油气地球物理,2017,15(2):19-23.
[9] Huang B Q,Kunoth A. An optimization based empirical mode decomposition scheme[J]. Journal of Computational and Applied Mathematics,2013,240:174-183.
[10]李季,潘孟春,唐莺,等.基于形态滤波和HHT的地磁信号分析与预处理[J].仪器仪表学报,2012,33(10):2175-2180.
[11] Xu G L,Wang X T,Xu X G,et al. Improved EMD for the analysis of FM signals[J]. Mechanical Systems and Signal Processing. 2012,33:181-196.
[12]孙抗,刘永超.基于小波包和EMD的断路器机械故障诊断研究[J].测控技术,2017,36(2):21-27.
[13]刘晗,张建中,黄忠来.应用同步挤压小波变换去除面波[J].石油地球物理勘探,2016,51(1):71-79.
[14]张志禹,李向月,李向阳.同步挤压小波变换对随机噪声抑制的研究[J].计算机工程与应用,2018,54(5):57-60.
[15]舒畅,李辉.基于小波与GBDT的无人机传感器故障诊断[J].测控技术,2017,36(8):41-46.
[16] An J Y, Ma X M. Image denoising for furnace flame based on median filter and wavelet transform[J]. Computer Engineering&Science,2016,38(8):1702-1708.
[17] Wang P,Gao J,Wang Z. Time-frequency analysis of seismic data using synchrosqueezing transform[J]. IEEE Geoscience and Remote Sensing Letters,2014,11(12):2042-2044.
[18] Chen Y, Liu T,Chen X,et al. Time-frequency analysis of seismic data using synchrosqueezing wavelet transform[J].Journal of Seismic Exploration,2014,23(4):303-312.
[19]高伟,胡定玉,方宇.采用小波变换和盲源分离的电机轴承故障诊断[J].测控技术,2017,36(5):51-60.
[20] Wik M W. Double exponential pulse models for comparison of lightning, nuclear and electrostatic discharge spectra[C]//Proceedings of 6th International Symposium on Electromagnetic Compatibility. 1985:169.