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

HFCAS OpenIR

	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
其他题名	Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition
	程知 1; 何枫 1; 靖旭1 ; 张巳龙1 ; 侯再红1
	2017
发表期刊	光子学报
ISSN	1004-4213
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.
其他摘要	In order to enhance the Signal-to-Noise Ratio（SNR）of Differential Column Image Motion lidar（DCIM lidar） detetion,a hybid denoising method which combines Ensemble Empirical Mode Decomposition（EEMD）and singular value decomposition（SVD）is proposed.The multilayer mode components are obtained from EEMD firstly.The difference of cross-correlation coefficients among these mode components is then utilized to determine the main noises which should be removed.The residual noises contained in mode components are identified by SVD and then the useful signal is extracted.Both the EEMD-SVD and EEMD methods are used to denoise the simulation signals and measured DCIM lidar signals.When the standard deviation of simulated noises is between 0.05 and 0.2,the signal-to-noise ratio（SNR）of retrieved turbulence profile with EEMD-SVD denoising is increased by 2.718 7 dB to 6.921 5 dB and the SNR of corresponding EEMD method is increased by 0.168 4 dB to 3.555 4 dB compared with the retrieved profile without denoising.Turbulence profiles retrieved from the undenoised and denoised DCIM lidar measurements and radio-sounding balloons are also compared at two typical time periods.It is found that the maximum SNR of turbulence profiles can separately be increased by 2.526 5 dB and 2.155 6 dB for EEMD-SVD and EEMD method compared with undenoising retrieval profile.The results indicate that the noise reduction effect of EEMD-SVD is superior than EEMD,which it is able to identify and reduce the noises more effectively.The SNR of original signal is greatly improved through EEMD-SVD method,thereby the retrieved atmospheric turbulence profile is achieved more accurately.
关键词	大气湍流去噪集合经验模态分解奇异值分解激光雷达
收录类别	CSCD
语种	中文
CSCD记录号	CSCD:6116965
引用统计
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/53483
专题	中国科学院合肥物质科学研究院
作者单位	1.中国科学院安徽光学精密机械研究所 2.中国科学院安徽光学精密机械研究所 3.中国科学院安徽光学精密机械研究所 4.中国科学院安徽光学精密机械研究所 5.中国科学院安徽光学精密机械研究所
推荐引用方式 GB/T 7714	程知,何枫,靖旭,等. 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[J]. 光子学报,2017,046.
APA	程知,何枫,靖旭,张巳龙,&侯再红.(2017).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.光子学报,046.
MLA	程知,et al."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".光子学报 046(2017).