An EEMD-SVD method based on gray wolf optimization algorithm for lidar signal noise reduction

Li, Shun; Mao, Jiandong; Li, Zhiyuan

doi:10.1080/01431161.2023.2249597

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Their experimental results showed that the denoised signal was close to the original signal. 11 A number of other algorithms were also applied to lidar signal denoising and proved to be effective. In 2018, Lu et al used the total variation penalty likelihood probability estimation algorithm to realize noise reduction inversion based on photon counting detection data.…”

Section: Introductionmentioning

confidence: 99%

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

Zhao,

Mao

2024

Journal of Applied Physics

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Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

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Section: Introductionmentioning

confidence: 99%