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Xiaojian Zhang

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Proceedings Article | 28 November 2023 Paper

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

Proceedings Volume 12772, 1277205 (2023) https://doi.org/10.1117/12.2687008

KEYWORDS: Denoising, LIDAR, Signal to noise ratio, Modal decomposition, Signal processing, Mathematical optimization

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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.

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