Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm
Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/2/126 |
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| author | Hongxu Li Jianhua Chang Fan Xu Zhenxing Liu Zhenbo Yang Luyao Zhang Shuyi Zhang Renxiang Mao Xiaolei Dou Binggang Liu |
| author_facet | Hongxu Li Jianhua Chang Fan Xu Zhenxing Liu Zhenbo Yang Luyao Zhang Shuyi Zhang Renxiang Mao Xiaolei Dou Binggang Liu |
| author_sort | Hongxu Li |
| collection | DOAJ |
| description | Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km. |
| first_indexed | 2024-12-20T11:18:16Z |
| format | Article |
| id | doaj.art-6105dcc1d4284e03bc86e782968b6d34 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:18:16Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6105dcc1d4284e03bc86e782968b6d342022-12-21T19:42:34ZengMDPI AGRemote Sensing2072-42922019-01-0111212610.3390/rs11020126rs11020126Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization AlgorithmHongxu Li0Jianhua Chang1Fan Xu2Zhenxing Liu3Zhenbo Yang4Luyao Zhang5Shuyi Zhang6Renxiang Mao7Xiaolei Dou8Binggang Liu9Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaKey Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, ChinaAlthough lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km.http://www.mdpi.com/2072-4292/11/2/126lidar signalvariational mode decompositionwhale optimization algorithmBhattacharyya distance |
| spellingShingle | Hongxu Li Jianhua Chang Fan Xu Zhenxing Liu Zhenbo Yang Luyao Zhang Shuyi Zhang Renxiang Mao Xiaolei Dou Binggang Liu Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm Remote Sensing lidar signal variational mode decomposition whale optimization algorithm Bhattacharyya distance |
| title | Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm |
| title_full | Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm |
| title_fullStr | Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm |
| title_full_unstemmed | Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm |
| title_short | Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm |
| title_sort | efficient lidar signal denoising algorithm using variational mode decomposition combined with a whale optimization algorithm |
| topic | lidar signal variational mode decomposition whale optimization algorithm Bhattacharyya distance |
| url | http://www.mdpi.com/2072-4292/11/2/126 |
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