摘要
差分光柱像运动激光雷达的信号噪声直接影响大气湍流强度廓线的反演精度,采用一种有效的去噪方法能够提升雷达探测性能。为了减弱小波-经验模态分解(EMD)联合降噪方法对小波的依赖性,提出先利用信号的趋势项对小波降噪后的信号进行自适应调制,再利用调制后的信号进行EMD去噪,即小波-趋势项-EMD方法,趋势项的提取仍采用EMD方法。为了保证调制的有效性,提出适用于大气相干长度(r0)廓线的调制判定准则,并采用去趋势波动分析方法自适应识别EMD降噪的阈值。为了论证所提方法的有效性,将该方法与小波、EMD、集合经验模态分解(EEMD)、小波-EMD 4种去噪方法进行对比。数值仿真和实验结果表明,在不同噪声强度下,5种方法均可提高r0廓线的信噪比和反演精度。两种联合方法优于单独方法,小波法优于EMD和EEMD方法,小波-趋势项-EMD方法进一步提高了小波-EMD方法的去噪能力,为小波-EMD联合去噪方法的改进提供了新思路。
The retrieval precision of atmospheric turbulence intensity profile is affected directly by the signal noise from differential light column image motion lidar. A valid denoising method can improve the detection performance of lidar. To reduce the dependence of the combined denoising method with wavelet-empirical mode decomposition （EMD） on wavelet, an adaptive modulation strategy with the signal trend term is proposed for wavelet denoising signal, and then the modulated signal is denoised by EMD, which is called wavelet-trend-EMD method. The trend term is still extracted by EMD. To ensure the validity of modulation, a decision criteria of modulation suitable for coherent length （r0） profile is presented, and the detrended fluctuation analysis is carried out to identify the EMD denoising threshold adaptively. To illustrate the validity of the proposed method, four other methods of wavelet, EMD, ensemble empirical mode decomposition （EEMD） and wavelet-EMD are used for comparison. The numerical simulation and experimental results indicate that all the five methods can improve the signal-to-noise ratio of r0 profile and the retrieval precision. The two joint methods are better than the single method and the wavelet method is superior to EMD and EEMD methods. More importantly, wavelet-trend-EMD further improves the denoising ability of wavelet-EMD, which provides a new improvement consideration for the joint method of Wavelet-EMD.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2017年第12期7-18,共12页
Acta Optica Sinica
基金
国家自然科学基金(41405014)
关键词
大气光学
小波变换
小波-经验模态分解
去趋势波动分析
激光雷达
去噪
atmospheric optics
wavelet transform
wavelet and empirical mode decomposition
delrended fluctuation analysis
lidar denoising
