基于LightGBM算法的飞行冲突探测研究OA

Aimed at the problems that timeliness is poor in geometric flight conflict detection methods and detec-tion samples are imbalanced in machine learning-based detection methods,this paper proposes a flight conflict de-tection method of integrating spatial geometric modeling and machine learning.Firstly,in combination of synthetic multi-dimensional features such as aircraft position and velocity,the geometric method is used to determine con-flicts based on the velocity obstacle method and the three-dimensional cylindrical protection zone.In order to ad-dress the poor timeliness of geometric judgment,a machine learning method is introduced.Conflict samples being short and training samples being imbalanced,the lightweight Gradient Boosting Machine(LightGBM)algorithm with a class weight adjustment mechanism is selected.Finally,the proposed method is verified by using actual sec-ondary radar data collected in the Xi'an area.The experimental results show that the operating speed by the pro-posed method is 3.91 times faster than that by the geometric method.Compared with typical algorithms such as Random Forest(RF)and K-Nearest Neighbors(KNN),the proposed method in conflict judgment is an accuracy increase of 19％and 91％respectively.