An aircraft has an obvious state division in a complete flight mission, the data of the trajectory points also have specific characteristics in each flight state, so the trajectory data with the similar characteristics can improve the performance of neural networks. Therefore, a combined online-learning model with K-means clustering and gated recurrent unit (GRU) neural networks for trajectory prediction is proposed in this paper. In the new model, the K-means clustering algorithm is used to adaptively cluster the trajectory points of the aircraft, and the trajectory points with higher similarity are grouped into a same cluster. Then, the online-learning prediction model based on a GRU neural networks is used to learn from the trajectory points of each cluster separately. Finally, the performance superiority of the new model proposed in this paper is tested and verified with the fused flight data of secondary radar and automatic dependent surveillance-broadcast (ADS-B).

飞机在一次完整的飞行任务中具有明显的状态划分，在每个飞行状态下轨迹点的数据也具有特定的特性，因此具有类似特性的轨迹数据可以提高神经网络的性能。因此，本文提出了一种结合K均值聚类和门控递归单元（GRU）神经网络的在线学习模型，用于轨迹预测。在新模型中，采用K-均值聚类算法对飞机的轨迹点进行自适应聚类，将相似度较高的轨迹点归为同一类。然后，使用基于GRU神经网络的在线学习预测模型分别从每个集群的轨迹点进行学习。最后，