Trajectory prediction (TP) is crucial for ensuring safety and efficiency in modern air traffic management systems. It is, for example, a core component of conflict detection and resolution tools, arrival sequencing algorithms, capacity planning, as well as several future concepts. However, TP accuracy within operational systems is hampered by a range of epistemic uncertainties such as the mass and performance settings of aircraft and the effect of meteorological conditions on aircraft performance. It can also require considerable computational resources. This paper proposes a method for adaptive TP that has two components: first, a fast surrogate TP model based on linear state space models (LSSM)s with an execution time that was 6.7 times lower on average than an implementation of the Base of Aircraft Data (BADA) in Python. It is demonstrated that such models can effectively emulate the BADA aircraft performance model, which is based on the numerical solution of a partial differential equation (PDE), and that the LSSMs can be fitted to trajectories in a dataset of historic flight data. Secondly, the paper proposes an algorithm to assimilate radar observations using particle filtering to adaptively refine TP accuracy. Comparison with baselines using BADA and Kalman filtering demonstrate that the proposed framework improves system identification and state estimation for both climb and descent phases, with 46.3% and 64.7% better estimates for time to top of climb and bottom of descent compared to the best performing benchmark model. In particular, the particle filtering approach provides the flexibility to capture non-linear performance effects including the CAS-Mach transition.

翻译：轨迹预测对于保障现代空中交通管理系统的安全与效率至关重要。例如，它是冲突检测与解脱工具、进场排序算法、容量规划以及若干未来概念的核心组成部分。然而，运行系统中的轨迹预测精度受到一系列认知不确定性的制约，例如飞机的质量与性能设置，以及气象条件对飞机性能的影响。此外，它也可能需要大量的计算资源。本文提出了一种自适应轨迹预测方法，该方法包含两个组成部分：首先，提出了一种基于线性状态空间模型的快速代理轨迹预测模型，其平均执行时间比Python实现的飞机性能数据库模型降低了6.7倍。研究表明，此类模型能够有效模拟基于偏微分方程数值解的飞机性能数据库模型，并且线性状态空间模型能够拟合历史飞行数据集中记录的轨迹。其次，本文提出了一种利用粒子滤波同化雷达观测数据的算法，以自适应地提升轨迹预测精度。与使用飞机性能数据库模型和卡尔曼滤波的基线方法对比表明，所提出的框架在爬升和下降阶段均改善了系统辨识与状态估计性能，其中到达爬升顶点时间和下降底部时间的估计精度相比性能最佳的基准模型分别提升了46.3%和64.7%。特别地，粒子滤波方法提供了捕捉非线性性能效应（包括校准空速-马赫数转换）的灵活性。