关键词: 通用航空, 冲突探测, 支持向量机(SVM), GA-PSO, 智能优化算法

Abstract: With the continuous increasing of flight density, the aviation safety in low altitude has caused extensive concern. Low-altitude environment is complex, and ground obstacles and weather have more significant impact on low-altitude flight than commercial aviation. Traditional traffic alert and collision avoidance system (TCAS) and other methods may not be applicable to low-altitude intensive flight environment. In view of the computational complexity and lack of applicability of traditional detection methods, a binary classification method of support vector machine (SVM) was introduced. By normalizing the trajectories of own and surrounding aircraft, optimizing the key parameters by intelligent optimization algorithm, and pre-training the classifier through simulation data, efficient conflict detection for low-altitude flight was carried out. Various sets of artificial data were utilized to verify the effectiveness of the algorithm. The results show that the missed alarm rate and false alarm rate are controlled at about 0.1% and 6% respectively, which proves that the proposed algorithm can overcome the shortcomings of traditional deterministic and probabilistic methods which are difficult to take both the efficiency and applicability into account.

Key words: general aviation, conflict detection, support vector machine(SVM), GA-PSO, intelligent optimization algorithm