Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer
Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive si...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3812 |
| _version_ | 1797586082797191168 |
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| author | Shijie Li Tong Wu Kai Zhong Xianzhong Zhang Yue Sun Yijian Zhang Yu Wang Xinqi Li Degang Xu Jianquan Yao |
| author_facet | Shijie Li Tong Wu Kai Zhong Xianzhong Zhang Yue Sun Yijian Zhang Yu Wang Xinqi Li Degang Xu Jianquan Yao |
| author_sort | Shijie Li |
| collection | DOAJ |
| description | Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function <i>F</i> is formed by three objective functions: correlation coefficient <i>R</i>, regression stability coefficient <i>S</i> and mean fit deviation <i>D</i>. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions <i>R</i>, <i>S</i> and <i>D</i> are pre-trained by NRS, and IGWO is used to optimize the fitness function <i>F</i>. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II. |
| first_indexed | 2024-03-11T00:17:17Z |
| format | Article |
| id | doaj.art-3ba13563a5c0405e92a27013ae03aa8a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:17Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3ba13563a5c0405e92a27013ae03aa8a2023-11-18T23:31:09ZengMDPI AGRemote Sensing2072-42922023-07-011515381210.3390/rs15153812Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf OptimizerShijie Li0Tong Wu1Kai Zhong2Xianzhong Zhang3Yue Sun4Yijian Zhang5Yu Wang6Xinqi Li7Degang Xu8Jianquan Yao9Key Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Marine Science and Technology, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Optoelectronic Information Science and Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, ChinaLidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function <i>F</i> is formed by three objective functions: correlation coefficient <i>R</i>, regression stability coefficient <i>S</i> and mean fit deviation <i>D</i>. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions <i>R</i>, <i>S</i> and <i>D</i> are pre-trained by NRS, and IGWO is used to optimize the fitness function <i>F</i>. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II.https://www.mdpi.com/2072-4292/15/15/3812atmospheric lidarsignal gluingmulti-channel signalimproved gray wolf optimizer (IGWO)neighborhood rough set (NRS) |
| spellingShingle | Shijie Li Tong Wu Kai Zhong Xianzhong Zhang Yue Sun Yijian Zhang Yu Wang Xinqi Li Degang Xu Jianquan Yao Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer Remote Sensing atmospheric lidar signal gluing multi-channel signal improved gray wolf optimizer (IGWO) neighborhood rough set (NRS) |
| title | Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer |
| title_full | Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer |
| title_fullStr | Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer |
| title_full_unstemmed | Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer |
| title_short | Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer |
| title_sort | gluing atmospheric lidar signals based on an improved gray wolf optimizer |
| topic | atmospheric lidar signal gluing multi-channel signal improved gray wolf optimizer (IGWO) neighborhood rough set (NRS) |
| url | https://www.mdpi.com/2072-4292/15/15/3812 |
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