基于ADS-B与Mode-SEHS联合观测的民航空域风场重建方法

Wind field reconstruction method in civil aviation airspace based on joint observations of ADS-B and Mode-S EHS

准确实时的风场数据对保障民航飞行安全有着重要作用,针对风场的精确重构问题,提出了一种基于飞行器监测数据的风场重建方法。旨在利用广播式自动相关监视和S模式增强型监视联合观测数据计算空域内的风观测值,并结合机器学习中的高斯过程回归模型,利用时间和空间上离散的风观测值进行模型训练,完整重建目标空域风场。实验结果表明,重建的风场风速的平均绝对误差为2.72m/s,相对误差为8.21%,风向的平均绝对误差为3.66°,证明了方法能够快速地完成准确实时的风场重建。

Accurate and real-time wind field data play a crucial role in ensuring the safety of civil aviation flights.In addressing the precise reconstruction of wind fields,this paper proposes a method based on aircraft monitoring data.The approach aims to utilize joint observations of automatic dependent surveillance-broadcast(ADS-B)and Mode-S enhanced surveillance(Mode-S EHS)to calculate wind observation values in the airspace.By integrating a Gaussian Process Regression model from machine learning and utilizing temporally and spatially discrete wind observation values for model training,the method achieves a complete reconstruction of the target airspace wind field.Experimental results demonstrate that the average absolute error of wind speed reconstructed is 2.72 m/s,with a relative error of 8.21%,and the average absolute error of wind direction is 3.66 degrees.This validates the method's capability to rapidly and accurately reconstruct wind fields in real-time.