基于集合经验模态分解和奇异值分解的激光雷达信号去噪

发布时间：2018-05-26 05:15

本文选题：大气湍流 + 去噪　；参考：《光子学报》2017年12期

【摘要】：为了提高差分光柱像运动激光雷达(DCIM雷达)探测信噪比,提出了一种基于集合经验模态分解(EEMD)和奇异值分解(SVD)的混合降噪法.由EEMD获得含噪信号多层模态分量,根据各模态分量之间互相关系数的差分量确定主要噪声并予以滤除,利用奇异值分解识别模态分量中的残余噪声并提取有用信号.利用混合降噪法EEMD-SVD和EEMD方法分别对模拟仿真信号和实测激光雷达信号进行降噪处理.结果表明,当模拟噪声标准差在0.05~0.2之间时,相比与未降噪直接反演的湍流廓线,EEMD-SVD方法降噪后反演的湍流廓线信噪比提高了2.718 7dB~6.921 5dB,相应的EEMD方法提高了1.446 1dB~3.366 1dB;两个不同时段DCIM雷达降噪前后反演廓线与探空廓线的对比发现,EEMD-SVD和EEMD两种方法降噪后反演廓线较之于未降噪的反演廓线,信噪比最大提高了2.526 5dB和2.155 6dB.EEMD-SVD的降噪效果优于EEMD,能够更有效地识别和滤除噪声,较大地提高了原始信号的信噪比,获得更准确的大气湍流廓线反演结果.
[Abstract]:In order to improve the signal-to-noise ratio (SNR) of differential optical column moving lidar (DCIM) detection, a hybrid noise reduction method based on set empirical mode decomposition (EEMD) and singular value decomposition (SVD) is proposed. The multilayer modal component of noisy signal is obtained by EEMD. The main noise is determined and filtered according to the difference component of the correlation number between each modal component. The residual noise in the modal component is identified by singular value decomposition and the useful signal is extracted. The EEMD-SVD and EEMD methods are used to reduce the noise of simulated and measured lidar signals, respectively. The results show that when the standard deviation of simulated noise is in the range of 0.05o 0.2, Compared with the direct inversion of turbulence profile without noise reduction, the SNR of turbulent profile inversion by EEMD-SVD method is increased by 2.718 7dB~6.921 5dB and the corresponding EEMD method by 1.446 1dB~3.366 1dB.The inversion profile and sounding profile of DCIM radar in two different periods before and after denoising are improved by 2.718 7dB~6.921 5db and 1.446 1dB~3.366 1db respectively. The comparison of the lines shows that the two methods, EEMD-SVD and EEMD, are better than the undenoised inversion profiles after de-noising. The maximum signal-to-noise ratio (SNR) of 2.526 5dB and 2.155 6dB.EEMD-SVD is better than that of EEMD, which can identify and filter the noise more effectively, improve the signal to noise ratio of the original signal, and obtain more accurate atmospheric turbulence profile inversion results.
【作者单位】：中国科学院安徽光学精密机械研究所中国科学院大气成分与光学重点实验室;中国科学技术大学;
【基金】：国家自然科学基金(No.41405014)资助~~
【分类号】：TN958.98

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