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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Bibliographic Details
Published in:Advances in meteorology 2022-12, Vol.2022, p.1-14
Main Authors: Zhang, Jie, Chan, Pak Wai, Ng, Michael K.
Format: Article
Language:English
Subjects:
Air safety
Aircraft
Airports
Algorithms
Aviation
Classification
Clustering
Construction
Detection
Doppler sonar
Feature extraction
Flight simulation
Indicators
Lidar
Machine learning
Methods
Optical radar
Pilots
Position indicators
Remote sensing
Statistical methods
Vectors
Velocity
Wind speed
Wind velocities
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Summary: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.
ISSN:1687-9309
1687-9317
DOI:10.1155/2022/3039797