Comparison of De-Noising Methods of LiDAR Signal

被引：0

作者：

Ding H. ^{[1
,2
]}

Wang Z. ^{[1
,3
]}

Liu D. ^{[1
,3
]}

机构：

[1] Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei

[2] University of Science and Technology of China, Hefei

[3] Advanced Laser Technology Laboratory of Anhui Province, Hefei

来源：

Wang, Zhenzhu (zzwang@aiofm.ac.cn); Wang, Zhenzhu (zzwang@aiofm.ac.cn) | 2021年 / Chinese Optical Society卷 / 41期

关键词：

De-noising; Empirical mode decomposition; LiDAR; Remote sensing; Variational mode decomposition; Wavelet transform;

D O I：

10.3788/AOS202141.2401001

中图分类号：

学科分类号：

摘要：

The echo signal of light detection and ranging (LiDAR) is nonlinear and non-stationary and is easily disturbed by various noises. In order to filter out noises and extract effective signal information, it is necessary to select appropriate methods for noise reduction processing. In this study, Poisson noise was added to the simulated LiDAR echo signal, and then de-noising experiments were carried out by wavelet transform (WT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and their improved and combined algorithms. Afterward, we selected the optimal de-noising method for LiDAR echo signal through comparative analysis. The experimental results showed that the WT-VMD joint algorithm has the maximum output signal-to-noise ratio (SNR) and the minimum root-mean-square error (RMSE) under different original SNRs, with a small smoothness of the de-noised curve, and therefore it can restore the original LiDAR echo signal well and improve the accuracy of subsequent signal inversion. © 2021, Chinese Lasers Press. All right reserved.

引用

共 22 条

[1]

Mao J D., Noise reduction for lidar returns using local threshold wavelet analysis, Optical and Quantum Electronics, 43, pp. 59-68, (2012)

[2]

Wang Z Z, Li J, Zhong Z Q, Et al., LiDAR exploration of atmospheric boundary layer over downtown of Beijing in summer, Journal of Applied Optics, 29, 1, pp. 96-100, (2008)

[3]

Zhou J, Yue G M, Qi F D, Et al., Optical properties of aerosol derived from lidar measurements, Chinese Journal of Quantum Electronics, 15, 2, pp. 140-148, (1998)

[4]

Zhou Z R, Hua D X, Yang R, Et al., De-noising method for Mie scattering lidar echo signal based on wavelet theory, Acta Photonica Sinica, 45, 7, (2016)

[5]

Fang H T, Huang D S., Noise reduction in lidar signal based on discrete wavelet transform, Optics Communications, 233, pp. 67-76, (2004)

[6]

Huang N E, Shen Z, Long S R, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 454, 1971, pp. 903-995, (1998)

[7]

Zheng F T, Hua D X, Zhou A W., Empirical mode decomposition algorithm research & application of Mie lidar atmospheric backscattering signal, Chinese Journal of Lasers, 36, 5, pp. 1068-1074, (2009)

[8]

Xu F, Chang J H, Liu B G, Et al., De-noising method research for lidar echo signal based on variational mode decomposition, Laser & Infrared, 48, 11, pp. 1443-1448, (2018)

[9]

Liu Y Q, Deng H W, Wu L B, Et al., Study on signal denoising of microseismic monitoring based on combined variational mode decomposition and wavelet threshold method, Mining Research and Development, 40, 2, pp. 98-103, (2020)

[10]

Zhang Y D, Hou M, Liu Z L, Et al., Empirical mode decomposition with wavelet de-noising, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 183-186, (2010)

← 1 2 3 →