EEMD在激光测云仪后向散射信号处理中的应用

激光测云仪后向散射信号是典型的非线性、非稳态信号,容易受噪声污染。针对该问题采用集成经验模态分解(EEMD)去噪算法进行处理,首先对含噪信号进行经验模态分解(EMD),将分解后的IMF分量进行自相关性分析,找出含噪占有量较大的IMF分量,对其进行SG(savitzky-golay)滤波,最后将滤波后的IMF分量和剩余分量进行信号的重构。经仿真实验结果表明,与传统的EMD方法相比,EEMD方法处理含噪信号后的输出信噪比提高了1.695 dB,均方误差平均降低了30%以上,说明该方法可以适用于非线性、非稳态的后向散射回波信号去噪处理,能为激光测云仪下一级的云底高度反演提供高信噪比的初始数据。

Application of EEMD in laser ceilometer backscattering signal processing

The backscattering signal of laser ceilometer as a typical nonlinear and non-stationary signal is susceptible to be polluted by noise. Aiming at this problem, the ensemble empirical mode decomposition (EEMD) denoising method is applied. Firstly, we use EEMD to decompose the noise signal and analyze the decomposition of the IMF component,then find out the larger component of IMF. Finally,we reconstruct the IMF component and the rest of the components signal after using Savitzky-Golay(SG) filter. The simulation and experiment results show that compared with the traditional empirical mode decomposition(EMD) method, the signal-to-noise ratio based on the EEMD method after processing increases 1.695 dB,the mean square error decreases by an average of more than 30%. It is shown that the method is suitable for nonlinear and non-stationary characteristics for the scattering echo signal processing,and able to provide the high signal-to-noise ratio of the initial data by laser ceilometer for the next level cloud base height inversion.