光学学报, 2018, 38 (2): 0201001, 网络出版: 2018-08-30   

基于小波去噪算法的全天时大气水汽拉曼激光雷达探测与分析 下载: 1104次

Detection and Analysis of All-Day Atmospheric Water Vapor Raman Lidar Based on Wavelet Denoising Algorithm
作者单位
西安理工大学机械与精密仪器工程学院, 陕西 西安 710048
摘要
提出了一种基于小波阈值去噪算法的白天太阳背景光滤波抑制方法,实现对拉曼回波信号中真实信号与噪声的分离,并有效滤除白天背景噪声。基于西安理工大学大气水汽探测拉曼激光雷达系统的全天时实测数据,详细讨论了分解层数、小波基函数、阈值函数以及阈值选取方法等因素对白天探测回波信号去噪结果的影响,对去噪前后信号进行分析并对去噪评价函数进行对比,当利用小波基sym6、分解层数为5层,并采用改进阈值函数和改进通用阈值方法的最优条件时,可实现对白天水汽拉曼散射信号和米-瑞利散射信号较好的去噪效果。讨论了小波去噪前后大气水汽混合比反演廓线和激光雷达水汽探测信噪比(SNR)的结果,分析表明利用该去噪系统得到的白天激光雷达水汽探测SNR提高约3.4倍,水汽探测距离可从1.5~2 km提高到3 km以上。开展全天时激光雷达连续探测实验和去噪处理,获得了24 h边界层内大气水汽混合比的连续变化特性,并得到与近地面气象站数据的较一致的结果,充分验证了小波去噪算法应用于全天时大气水汽探测的可行性和有效性。
Abstract
A method based on the wavelet threshold denoising algorithm is proposed for the suppression of solar background light, so that the separation of the real signal from the noise in the Raman returned signal can be realized and the background noise in daytime can be removed. Based on all-day data measured by atmosphere water vapor Raman lidar system built in Xi’an University of Technology, influences of decomposition level, wavelet function, threshold function, and threshold selection method on the denoising results of returned signal in daytime are discussed. Signals before and after denoising are compared and denoising evaluation functions are compared. We adopt wavelet sym6, decomposition of five layers, improved threshold function, and improved threshold method to obtain the better denoising effect for water vapor Raman and Mie-Rayleigh scattering signals in daytime. Furthermore, profiles of the atmospheric water vapor mixing ratio, and the results of signal-to-noise ratio (SNR) of water vapor are discussed. Results show that SNR for lidar water vapor measurement increases by 3.4 times in the denoising process. and the water vapor detection range can be improved up to over 3 km from 1.5-2 km in daytime. Lidar continuous detection experiments and denosing process are carried out during 24 h. Variation characteristics of the atmospheric water vapor mixing ratio are obtained below boundary layer, and the results agree with data from near-surface weather stations. It is verified the feasibility and effectiveness of the wavelet denoising algorithm used in all-day atmospheric water vapor detection.

王玉峰, 曹小明, 张晶, 汤柳, 宋跃辉, 狄慧鸽, 华灯鑫. 基于小波去噪算法的全天时大气水汽拉曼激光雷达探测与分析[J]. 光学学报, 2018, 38(2): 0201001. Yufeng Wang, Xiaoming Cao, Jing Zhang, Liu Tang, Yuehui Song, Huige Di, Dengxin Hua. Detection and Analysis of All-Day Atmospheric Water Vapor Raman Lidar Based on Wavelet Denoising Algorithm[J]. Acta Optica Sinica, 2018, 38(2): 0201001.

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