CEEMDAN联合小波阈值算法在水下激光雷达中抑制散射杂波的应用  

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

作　　者：樊超阳 李朝锋 杨苏辉[1,2] 刘欣宇[1,2] 廖英琦 Fan Chao-Yang;Li Chao-Feng;Yang Su-Hui;Liu Xin-Yu;Liao Ying-Qi(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Information Photonics Technology,Ministry of Industry and Information Technology,Beijing Institute of Technology,Beijing 100081,China;China Electronics Technology Group Corporation 34th Research Institute,Guilin 541000,China;China Electronics Technology Group Corporation 28th Research Institute,Nanjing 210000,China)

机构地区：[1]北京理工大学光电学院,北京100081 [2]北京理工大学,信息光子技术工业和信息化部重点实验室,北京100081 [3]中国电子科技集团公司第三十四研究所,桂林541000 [4]中国电子科技集团公司第二十八研究所,南京210000

出　　处：《物理学报》2023年第22期154-161,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:61835001)资助的课题。

摘　　要：水下激光雷达回波信号中往往含有大量散射噪声.为了能够有效抑制散射噪声,提高水下激光雷达测距精度,提出了基于自适应完备噪声经验模态分解(CEEMDAN)与小波阈值相结合的去噪新方法.首先通过相关系数法对自适应完备噪声经验模态分解得到的本征模态函数(IMF)进行筛选;然后对筛选后的本征模态函数进行小波阈值去噪,进一步去除本征模态函数中的噪声成分;最后将去噪后的本征模态函数进行信号重构得到去噪后信号.将该方法应用到不同衰减系数水体的强度调制连续光水下测距实验,使用白色聚氯乙烯(PVC)反射板为探测目标,在3.75个衰减长度时,直接采用相关极值确定延时,测距误差达到19.2 cm;应用该方法处理后,测距误差减小到6.2 cm,有效提高测距精度.The echo of underwater lidar often contains a significant quantity of scattering clutters.In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar,a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and wavelet threshold denoising is proposed.The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select intrinsic mode function(IMF)components obtained from the CEEMDAN decomposition.The IMFs,which are more closely related to the original signal,are selected.Then,the wavelet thresholding denoising algorithm is applied to each of the selected IMFs to perform additional denoising.For each IMF component,specific threshold values are calculated based on their frequency and amplitude characteristics.Subsequently,the wavelet coefficients of the IMF components are processed by using these threshold values.Finally,the denoised IMF components are combined and reconstructed to obtain the final denoised signal.Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods.By using the threshold value calculated based on the characteristics of each IMF component,the wavelet thresholding denoising process is improved in comparison with directly using a single threshold value.This approach enhances the algorithm’s adaptability and enables more effective removal of noise from the signal.We apply the proposed method to underwater ranging experiments.A 532 nm intensity-modulated continuous wave laser is used as a light source.Ranging is performed for a target in water with varying attenuation coefficients.A white polyvinyl chloride(PVC)reflector is used as a target.When the correlation extreme value is directly used to determine the delay at a distance of 3.75 attenuation length,it results in a ranging error of 19.2 cm.However,after applying the proposed method,the rangin

关 键 词：水下激光雷达 经验模态分解 小波阈值去噪 相关系数法 

分 类 号：TN958.98[电子电信—信号与信息处理]