自由飞行下基于集成学习的概率型冲突探测算法

空中交通流量的持续增加对中、短期飞行冲突探测的精度与处理大量目标的能力的要求更高,提出基于集成学习(ensemble learning)的冲突探测算法。首先,对飞机冲突过程建模,收集飞行样本;其次,提取飞机当前位置、速度矢量、向前看时间、待转弯时刻和转角为特征量,训练基本分类器,获得元数据集;然后,以支持向量机为二级分类器,元数据集为新的特征量,训练Stacking元分类器(meta classifier),分类阶段通过Sigmoid函数概率映射法输出冲突概率;最后进行仿真分析。结果表明:基于集成学习的冲突探测算法对冲突探测具有较高的准确率,使得虚警概率大幅下降,且适用于转弯飞行。

A probabilistic conflict detection algorithm based onensemble learning in free flight

Aiming at improving the conflict detection precision in a single classifier, a probabilistic conflict detection algorithm based on ensemble learning was proposed. Firstly, aircraft conflict model was established for the purpose of selecting flight datasets. Secondly, the current positions, speed vectors, look-ahead time, estimated turning time and turning angles were extracted as characteristic quantities which were inputted to train the basic classifiers, and a meta-datasets were obtained. Afterwards, support vector machine is used as the second-level classifier, the meta-datasets was regarded as new characteristic quantities which is used to train the stacking meta classifier. Lastly, the conflict probability was solved by the Sigmoid function mapping method. Simulation results have shown that this algorithm has a high accuracy for conflict detection and is suitable for turning flight.