改进的激光雷达回波信号去噪方法  

Improved de-noising method of lidar echo signal

作　　者：朱玲嬿 常建华[1,2] 李红旭[1] 徐帆 刘秉刚 

机构地区：[1]南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京210044 [2]南京信息工程大学江苏省气象探测与信息处理重点实验室,南京210044

出　　处：《电子测量与仪器学报》2017年第10期1608-1613,共6页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(11374161);江苏省重点研发计划(BE2016756);江苏高校优势学科Ⅱ期建设工程;江苏省高校品牌专业建设工程资助项目

摘　　要：激光雷达回波信号的强度随距离的平方衰减,当探测距离较大时,信号将淹没在较强的噪声之中。因此,如何有效地从强背景噪声中提取出有用信号至关重要。利用经验模态分解将激光雷达回波信号进行分解,根据本征模态函数与回波信号之间的相关性,结合软阈值与粗糙惩罚技术,有效地提高了激光雷达回波信号去噪的效果。实验结果表明,当加入5 dB高斯白噪声时,该方法的输出信噪比为16.67 dB,均方根误差为1.49×10^(-11)。相比于其他去噪方法,该方法具有较高的信噪比及较低的均方根误差,从而证明了此方法的有效性。The intensity of the lidar echo signal decays with the square of the distance. When the detection distance is large, the signal will be submerged in the strong noise. Therefore, it is crucial to extract valid signal efficiently from strong background noise. In this paper, the lidar echo signal is decomposed through the empirical mode decomposition. According to the correlation between the intrinsic mode function and the echo signal, the effect of lidar echo signal de-noising is improved effectively by combining with soft threshold and roughness penalty techniques. The experimental results of this method show that the output signal-to-noise ratio is 16.67 dB and the root mean square error is 1.49 ~ 10-I＇ when 5 dB Gaussian white noise is added. Compared with other de-noising techniques, this method achieves the higher signal-to-noise ratio and the lower root mean square error, thereby the effectiveness of this method is proved.

关 键 词：激光雷达回波信号 经验模态分解 软阈值 粗糙惩罚 

分 类 号：TN958.98[电子电信—信号与信息处理]