A novel Rayleigh lidar signal denoising algorithm for far-field noise suppression and high-accuracy retrieval

Tong Wu; Degang Xu; Kai Zhong; Xianzhong Zhang; Xinqi Li; Xiaojian Zhang; Jianquan Yao

doi:10.1117/12.2687008

28 November 2023 A novel Rayleigh lidar signal denoising algorithm for far-field noise suppression and high-accuracy retrieval

Tong Wu, Degang Xu, Kai Zhong, Xianzhong Zhang, Xinqi Li, Xiaojian Zhang, Jianquan Yao

Author Affiliations +

Proceedings Volume 12772, Real-time Photonic Measurements, Data Management, and Processing VII; 1277205 (2023) https://doi.org/10.1117/12.2687008
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Abstract

A novel signal denoising framework (EEMD-VMD-IMWOA) for Rayleigh lidar is proposed to better suppress noise in an atmospheric lidar echo signal and improve retrieval accuracy. The ensemble empirical mode decomposition (EEMD) is used to retain the intrinsic mode functions (IMFs) of signal as the low-frequency effective component. Based on the denoising ability of variational mode decomposition (VMD) under high noise signal, the IMFs with noise is further denoised by VMD to obtain high-frequency effective component, wherein the improved whale optimization algorithm (IMWOA) is used to get the optimal decomposition layer K and the quadratic penalty α of VMD. Then, the low-frequency and high-frequency effective components are reconstructed to gain denoised signal. The simulation results show that the denoising effect of EEMD-VMD-IMWOA is superior to Wavelet threshold, EEMD and VMD, especially the far-field noise interference can be suppressed. Under the condition that the temperature retrieval error is less than ± 10 K, when the integration time is only 600s, the effective retrieval altitude can reach 59.6km, which is 17.3% higher than that without denoising. Finally, the retrieval accuracy of the measured lidar signal is significantly improved by EEMD-VMD-IMWOA.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tong Wu, Degang Xu, Kai Zhong, Xianzhong Zhang, Xinqi Li, Xiaojian Zhang, and Jianquan Yao "A novel Rayleigh lidar signal denoising algorithm for far-field noise suppression and high-accuracy retrieval", Proc. SPIE 12772, Real-time Photonic Measurements, Data Management, and Processing VII, 1277205 (28 November 2023); https://doi.org/10.1117/12.2687008

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KEYWORDS

Denoising

LIDAR

Signal to noise ratio

Modal decomposition

Mathematical optimization

Signal processing

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