Accurate multi-step flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the flight trajectory prediction performance of existing works. The first issue is the negative impact on prediction accuracy caused by the significant differences in data range. The second issue is that real-world flight trajectories involve underlying temporal dependencies, and existing methods fail to reveal the hidden complex temporal variations and only extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction. Specifically, FlightPatchNet first utilizes the differential coding to encode the original values of longitude and latitude into first-order differences and generates embeddings for all variables at each time step. Then, a global temporal attention is introduced to explore the dependencies between different time steps. To fully explore the diverse temporal patterns in flight trajectories, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. Finally, FlightPatchNet ensembles multiple predictors to make direct multi-step prediction. Extensive experiments on ADS-B datasets demonstrate that our model outperforms the competitive baselines. Code is available at: https://github.com/FlightTrajectoryResearch/FlightPatchNet.

准确的多步飞行轨迹预测在空中交通控制中起着重要作用，我们提出了FlightPatchNet，一个具有差分编码和多尺度补丁网络的飞行轨迹预测模型，该模型通过差分编码将经度和纬度的原始值转换成一阶差分，并引入全局时间注意力来探索不同时间步骤之间的依赖关系，通过多尺度补丁网络捕捉不同时间尺度下的内部和外部补丁依赖关系，并进一步整合不同尺度和变量之间的多尺度时间特征，最终通过集成多个预测器来进行直接的多步预测。广泛的ADS-B数据集实验表明我们的模型优于竞争基线。

FlightPatchNet：多尺度补丁网络与差分编码用于航班轨迹预测