基于OLS-RBF神经网络的进场飞行时间预测

摘要

Estimated Time of Arrival ( ETA ) plays a great role in arrival sequencing and scheduling , therefore it is particularly important to predict the arrival flight time quickly and accurately .Based on the analysis of historical radar track ,with the help of RBF ( Radial Basic Function ) Neural Network ,the map-ping relationship is constructed between the arrival aircraft′s altitude/speed at the metering point ,flight distances and flight time .And then,the orthogonal least squares ( OLS) algorithm is adopted to design the RBF-NN based arrival flight time prediction model .Taking the arrival aircrafts via VMB to Shanghai Pud-ong Airport as examples ,the RMSE between estimated and actual time of arrival is controlled within 50 s with consideration of the same aircraft type .The simulation results indicated that the proposed approach is able to predict arrival flight time quickly and accurately .%航空器预计到达时刻（ ETA）是航空器进场排序与调度的基础，因此进场航空器飞行时间的快速与准确预测显得尤为重要。基于历史雷达轨迹分析，通过RBF（ Radial Basic Function ）神经网络构建进场航空器进港时的高度、速度、进场飞行距离与进场飞行时间的映射关系，利用正交最小二乘算法设计基于RBF神经网络的进场飞行时间预测模型。以上海浦东机场VMB进港点进场航班为例进行仿真验证，在考虑航空器机型的情况下，可将航空器飞行时间预测的均方根误差控制在50 s以内。仿真结果表明，提出的方法能够有效地实现进场飞行时间的快速与准确预测。