基于最优样本集在线模糊最小二乘支持向量机的飞行冲突网络态势预测

Situation prediction of flight conflict network based on online fuzzy least squares support vector machine with optimal training set

针对空中交通系统运行周期性和时变性的特点,结合复杂网络理论和模糊最小二乘支持向量机(LSSVM),提出一种基于最优样本集在线模糊最小二乘支持向量机(OTSOF-LSSVM)的飞行冲突网络态势预测方法。首先,基于三维的速度障碍法构建飞行冲突网络模型,并根据航空器的位置、航向和速度判断冲突;其次,分析飞行冲突网络拓扑指标的演化时间序列,得到与预测时刻在时间和距离上相关的样本组成最优样本集;最后,采用在线模糊LSSVM训练得到预测模型,并在模型更新过程中通过分块矩阵思想简化更新过程,提高算法效率。实验结果表明,所提方法能够快速、准确地预测空中态势,为管制员掌握空中交通的发展情况提供参考,并辅助进行冲突的预先调配。

Concerning the periodicity and time-varying characteristics of air traffic system operation,a flight conflict network situation prediction method based on Optimal Training Set Online Fuzzy-Least Squares Support Vector Machine(OTSOF-LSSVM)was proposed by combining complex network theory and fuzzy Least Squares Support Vector Machine(LSSVM).Firstly,a flight conflict network model was constructed based on the three-dimensional velocity obstacle method,and conflicts were judged according to the positions,headings and velocities of the aircrafts.Then,the evolution time series of topology indicators of flight conflict network were analyzed to obtain the optimal training set which consisted of samples related to the predicted moment in time and distance.Finally,a prediction model was obtained by online fuzzy LSSVM training,and the idea of block matrix was used to simplify the updating process and improve the efficiency of the algorithm.Experimental results show that the proposed method can quickly and accurately predict the air situation,provide reference for controllers to master the development of air traffic,and assist the pre-deployment of conflicts.