Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

Li, Hongxu; Chang, Jianhua; Xu, Fan; Liu, Zhenxing; Yang, Zhenbo; Zhang, Luyao; Zhang, Shuyi; Mao, Renxiang; Dou, Xiaolei; Liu, Binggang

doi:10.3390/rs11020126

Cited by 56 publications

(34 citation statements)

References 27 publications

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“…Naturally, Lidar data are highly vulnerable and easily affected by noise [26]. Different scholars have been studying the factors which affect the Lidar data regarding structural health monitoring [13,27].…”

Section: Introductionmentioning

confidence: 99%

Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data

Maru

Lee

Cha

et al. 2020

Sensors

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The Light Detection And Ranging (LiDAR) system has become a prominent tool in structural health monitoring. Among such systems, Terrestrial Laser Scanning (TLS) is a potential technology for the acquisition of three-dimensional (3D) information to assess structural health conditions. This paper enhances the application of TLS to damage detection and shape change analysis for structural element specimens. Specifically, estimating the deflection of a structural element with the aid of a Lidar system is introduced in this study. The proposed approach was validated by an indoor experiment by inducing artificial deflection on a simply supported beam. A robust genetic algorithm method is utilized to enhance the accuracy level of measuring deflection using lidar data. The proposed research primarily covers robust optimization of a genetic algorithm control parameter using the Taguchi experiment design. Once the acquired data is defined in terms of plane, which has minimum error, using a genetic algorithm and the deflection of the specimen can be extracted from the shape change analysis.

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Section: Introductionmentioning

confidence: 99%

Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data

Maru

Lee

Cha

et al. 2020

Sensors

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“…VMD is a very simple and fast method for signal adaptive decomposition that can decompose a real value signal into an ensemble of modes with a central frequency, and it has optimality in dealing with noise because of its close relations to the Wiener filter [17]. However, penalty parameter α and mode number K in VMD need to be predefined, and these two values have an important impact on VMD [18]. Although the parameters can be determined by the trial-and-error method or through experience, these options are time-consuming and inefficient because of the wide range of choices.…”

Section: Introductionmentioning

confidence: 99%

Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

Liu

Chang

et al. 2020

IEEE Access

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“…The variational mode decomposition (VMD) technique can achieve a nice smoothing effect using its Wiener filtering characteristics. The research results show that the VMD combined with whale optimization algorithm (VMD-WOA) can also achieve better results in LiDAR signal denoising [12]. The common point of the above methods is that the LiDAR signal is decomposed into a series of components and removed the high-frequency components containing noise to realize the denoising.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined With Improved Thresholding Method

Zhang

Chang

et al. 2020

IEEE Access

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The echo signal of light detection and ranging (LiDAR) system is easily disturbed by various noises under the influence of intense background light. The signal to noise ratio (SNR) decreases with increasing distance, which seriously affects the retrieval accuracy of the LiDAR system. This paper combines local mean decomposition and an improved thresholding method (LMD-ITM) to process the Lidar signal with a lot of noise, so as to avoid the loss of useful information. In this research, the correlation coefficient and energy entropy are used to distinguish between the relevant and irrelevant components decomposed by the local mean decomposition. Subsequently, the improved thresholding method is applied to process the irrelevant components. It can extract the effective signals which are difficult to be separated from the high-frequency signals. Finally, all components are reconstructed to get the denoising signal. The experimental results demonstrate that this method can effectively avoid the short-range migration, suppress the long-range noise, and adapt to different weather conditions. Compared with the previous denoising method, the proposed method increases the SNR by about 15%. INDEX TERMS LiDAR signal denoising, local mean decomposition, correlation coefficient, thresholding method.

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Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

Cited by 56 publications

References 27 publications

Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data

Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data

Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined With Improved Thresholding Method

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