{"title":"基于统计特征的激光雷达风切变检测","authors":"J. Zhang, P. Chan, M. Ng","doi":"10.1155/2022/3039797","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LiDAR-Based Windshear Detection via Statistical Features\",\"authors\":\"J. Zhang, P. Chan, M. Ng\",\"doi\":\"10.1155/2022/3039797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":7353,\"journal\":{\"name\":\"Advances in Meteorology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2022-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Meteorology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/3039797\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Meteorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1155/2022/3039797","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
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 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.
期刊介绍:
Advances in Meteorology is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of meteorology and climatology. Topics covered include, but are not limited to, forecasting techniques and applications, meteorological modeling, data analysis, atmospheric chemistry and physics, climate change, satellite meteorology, marine meteorology, and forest meteorology.