An EEMD-SVD-LWT algorithm for denoising a lidar signal. (15th January 2021)

• Record Type: Journal Article
• Authors: Cheng, Xiao; Mao, Jiandong; Li, Juan; Zhao, Hu; Zhou, Chunyan; Gong, Xin; Rao, Zhimin
• Is Part Of: Measurement. Volume 168(2021)
• Publication Date: 2021-01-15
• Subjects: Lidar -- Denoising algorithm -- Ensemble empirical mode decomposition -- Singular value decomposition -- Lifting wavelet transform; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108405
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An EEMD-SVD-LWT denoising algorithm is proposed. The denoising algorithm is designed for denoising the lidar signal. The denoising algorithm has important significance for processing the lidar signal. Abstract: A segmentation singular value decomposition (SVD)-lifting wavelet transform (LWT) denoising algorithm based on ensemble empirical mode decomposition (EEMD) was proposed to better suppress noise in an atmospheric lidar return signal. The EEMD method is used to distinguish inherent modal functions (IMFs) of the noise and signal, and remove the IMF with noise as its main component. Moreover, the SVD-LWT method is adopted to remove the noise in the IMF component containing the signal and thus finely extract the signal. The simulated Bumps signal with different sequences of Gaussian white noise was denoised, and the denoising effect of the EEMD-SVD-LWT algorithm was compared with the effects of the wavelet soft threshold, EEMD (correlation coefficient), and EEMD (difference value) methods. Simulation shows that the denoising effect of the EEMD-SVD-LWT algorithm was best. The EEMD-SVD-LWT algorithm was also used to denoise practical lidar signals and was better than that achieved with the other methods.