LiDAR-Based Windshear Detection via Statistical Features
Windshear is a kind of microscale meteorological phenomenon which can cause danger to the landing and takeoff of aircrafts. Accurate windshear detection plays a crucial role in aviation safety. With the development of machine learning, several learning-based methods are proposed for windshear detect...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/3039797 |
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| _version_ | 1832552683731419136 |
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| author | Jie Zhang Pak Wai Chan Michael K. Ng |
| author_facet | Jie Zhang Pak Wai Chan Michael K. Ng |
| author_sort | Jie Zhang |
| collection | DOAJ |
| description | Windshear is a kind of microscale meteorological phenomenon which can cause danger to the landing and takeoff of aircrafts. Accurate windshear detection plays a crucial role in aviation safety. With the development of machine learning, several learning-based methods are proposed for windshear detection, i.e., windshear and non-windshear classification. To obtain accurate detection results, it is significant to extract features that can distinguish windshear and non-windshear properly from the obtained wind velocity data. In this paper, we mainly introduce two statistical indicators derived from the Doppler Light Detection and Ranging (LiDAR) observational wind velocity data by plan position illustrate (PPI) scans for windshear features construction. Besides the indicators directly derived from the wind velocity data, we also study the visual information from the corresponding conical images of wind velocity. Based on the proposed indicators, we construct three feature vectors for windshear and non-windshear classification. Inspired by the idea of multiple instance learning, the wind velocity data collected in the 4 minutes within the reported time spot are considered in the procedure of feature vector construction, which can reduce the possibility of windshear features missing. Both statistical methods and clustering methods are applied to evaluate the effectiveness of the proposed feature vectors. Numerical results show that the proposed feature vectors have good effect on windshear and non-windshear classification and can be used to provide more accurate windshear alerting to pilots in practice. |
| format | Article |
| id | doaj-art-0a12d98d80cb45ff878a23c5654d29cd |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-0a12d98d80cb45ff878a23c5654d29cd2025-02-03T05:57:59ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/3039797LiDAR-Based Windshear Detection via Statistical FeaturesJie Zhang0Pak Wai Chan1Michael K. Ng2Department of MathematicsAviation Weather ServicesDepartment of MathematicsWindshear is a kind of microscale meteorological phenomenon which can cause danger to the landing and takeoff of aircrafts. Accurate windshear detection plays a crucial role in aviation safety. With the development of machine learning, several learning-based methods are proposed for windshear detection, i.e., windshear and non-windshear classification. To obtain accurate detection results, it is significant to extract features that can distinguish windshear and non-windshear properly from the obtained wind velocity data. In this paper, we mainly introduce two statistical indicators derived from the Doppler Light Detection and Ranging (LiDAR) observational wind velocity data by plan position illustrate (PPI) scans for windshear features construction. Besides the indicators directly derived from the wind velocity data, we also study the visual information from the corresponding conical images of wind velocity. Based on the proposed indicators, we construct three feature vectors for windshear and non-windshear classification. Inspired by the idea of multiple instance learning, the wind velocity data collected in the 4 minutes within the reported time spot are considered in the procedure of feature vector construction, which can reduce the possibility of windshear features missing. Both statistical methods and clustering methods are applied to evaluate the effectiveness of the proposed feature vectors. Numerical results show that the proposed feature vectors have good effect on windshear and non-windshear classification and can be used to provide more accurate windshear alerting to pilots in practice.http://dx.doi.org/10.1155/2022/3039797 |
| spellingShingle | Jie Zhang Pak Wai Chan Michael K. Ng LiDAR-Based Windshear Detection via Statistical Features Advances in Meteorology |
| title | LiDAR-Based Windshear Detection via Statistical Features |
| title_full | LiDAR-Based Windshear Detection via Statistical Features |
| title_fullStr | LiDAR-Based Windshear Detection via Statistical Features |
| title_full_unstemmed | LiDAR-Based Windshear Detection via Statistical Features |
| title_short | LiDAR-Based Windshear Detection via Statistical Features |
| title_sort | lidar based windshear detection via statistical features |
| url | http://dx.doi.org/10.1155/2022/3039797 |
| work_keys_str_mv | AT jiezhang lidarbasedwindsheardetectionviastatisticalfeatures AT pakwaichan lidarbasedwindsheardetectionviastatisticalfeatures AT michaelkng lidarbasedwindsheardetectionviastatisticalfeatures |