Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty
Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty
复制标题
使用基于相关的 EMD 结合软阈值和粗糙度惩罚来降低激光雷达信号的噪声
DOI：
10.1016/j.optcom.2017.09.063
复制
发表时间：
2018-01-15
影响因子：
2.4
通讯作者：
Yang, Zhenbo
中科院分区：
文献类型：
--
作者：
Chang, Jianhua;Zhu, Lingyan;Yang, Zhenbo
经验模式分解(EMD)被广泛应用于非线性、非平稳信号的降噪处理。提出了一种基于经验模式分解的激光雷达信号去噪新方法--软阈值与粗糙度惩罚经验模式分解法(EMD-STRP)。该方法首先通过相关系数来区分相关和不相关的固有模式函数。然后，将软阈值技术应用于无关模式，并将粗糙度惩罚技术应用于相关模式，以提取尽可能多的信息。用三个典型的被高斯白噪声污染的信号对该方法的有效性进行了评估。然后将去噪性能与基于相关性的EMD部分重构、基于相关性的EMD硬阈值和小波变换等其他方法的去噪性能进行了比较。将EMD-STRP应用于激光雷达测量信号，有效地抑制了噪声，信噪比提高了22.25分贝，探测距离延长了11公里。(C)2017爱思唯尔B.V.保留所有权利。
Empirical mode decomposition (EMD) is widely used to analyze the non-linear and non-stationary signals for noise reduction. In this study, a novel EMD-based denoising method, referred to as EMD with soft thresholding and roughness penalty (EMD-STRP), is proposed for the Lidar signal denoising. With the proposed method, the relevant and irrelevant intrinsic mode functions are first distinguished via a correlation coefficient. Then, the soft thresholding technique is applied to the irrelevant modes, and the roughness penalty technique is applied to the relevant modes to extract as much information as possible. The effectiveness of the proposed method was evaluated using three typical signals contaminated by white Gaussian noise. The denoising performance was then compared to the denoising capabilities of other techniques, such as correlation-based EMD partial reconstruction, correlation-based EMD hard thresholding, and wavelet transform. The use of EMD-STRP on the measured Lidar signal resulted in the noise being efficiently suppressed, with an improved signal to noise ratio of 22.25 dB and an extended detection range of 11 km. (C) 2017 Elsevier B.V. All rights reserved.