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A flight profile clustering method combining twed with K-means algorithm for 4D trajectory prediction

机译：三种轨迹预测与K平均算法相结合的飞行型材聚类方法

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摘要

4D trajectory prediction is the core technology of modern ATC automatic system. Mining nominal flight profile containing intention of controllers is the key issue in 4D trajectory prediction. In this paper, a clustering method combining time warp edit distance (TWED) with K-means algorithm is proposed to improve the accuracy of nominal flight profile. Firstly, a series of historical trajectory data with same origin and destination are pre-processed to eliminate the effect of outlier point. Secondly, a novel adaptive clustering algorithm is proposed in which the distance between different trajectories is calculated by TWED algorithm rather than the conventional elastic similarity measure. In proposed clustering algorithm, a non-fuzzy clustering model assessment index integrating the separation degree of intra-cluster and condensation degree of inter-clusters is provided to determine K clustering centers adaptively under a density threshold. Then K nominal flight profiles in each cluster are fitted based on matching rules of TWED. Finally, the predicted trajectory containing various control intentions is used to forecast aircraft trajectory in advance to improve efficiency of airspace. The experimental results show that the accuracy and stability of the proposed adaptive clustering algorithm are significantly higher than the state-of-the-art clustering algorithm with dynamic time warping (DTW).
机译：4D轨迹预测是现代ATC自动系统的核心技术。挖掘控制器意图的挖掘名义飞行型材是4D轨迹预测中的关键问题。在本文中，提出了一种组合时间WARP编辑距离（TWED）与K-MEAT算法的聚类方法，以提高标称飞行型材的准确性。首先，预先处理具有相同原点和目的地的历史轨迹数据，以消除异常值点的效果。其次，提出了一种新颖的自适应聚类算法，其中通过TWED算法而不是传统的弹性相似度测量来计算不同轨迹之间的距离。在提出的聚类算法中，提供了积分集群内簇间分离程度的非模糊聚类模型评估指数，以在密度阈值下自适应地确定K聚类中心。然后，每个集群中的k标称飞行配置文件基于TWED的匹配规则拟合。最后，含有各种控制意图的预测轨迹用于预先预测航空器轨迹以提高空域的效率。实验结果表明，所提出的自适应聚类算法的准确性和稳定性显着高于动态时间翘曲（DTW）的最新聚类算法。

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