Advanced Aerial Mobility (AAM) operations require strategic flight planning services that predict both spatial and temporal uncertainties to safely validate flight plans against hazards such as weather cells, restricted airspaces, and CNS disruption areas. Current uncertainty estimation methods for AAM vehicles rely on conservative linear models due to limited real-world performance data. This paper presents a novel Kalman Filter-based uncertainty propagation method that models AAM Flight Management System (FMS) architectures through sigmoid-blended measurement noise covariance. Unlike existing approaches with fixed uncertainty thresholds, our method continuously adapts the filter's measurement trust based on progress toward waypoints, enabling FMS correction behavior to emerge naturally. The approach scales proportionally with control inputs and is tunable to match specific aircraft characteristics or route conditions. We validate the method using real ADS-B data from general aviation aircraft divided into training and verification sets. Uncertainty propagation parameters were tuned on the training set, achieving 76% accuracy in predicting arrival times when compared against the verification dataset, demonstrating the method's effectiveness for strategic flight plan validation in AAM operations.

翻译：先进空中交通（AAM）运行需要战略飞行规划服务，该服务需预测空间和时间不确定性，以安全地验证飞行计划，应对诸如天气单元、限制空域以及通信、导航、监视（CNS）中断区域等风险。由于缺乏真实的性能数据，目前针对AAM飞行器的不确定性估计方法依赖于保守的线性模型。本文提出了一种新颖的基于卡尔曼滤波的不确定性传播方法，该方法通过S型混合测量噪声协方差对AAM飞行管理系统（FMS）架构进行建模。与现有采用固定不确定性阈值的方法不同，我们的方法根据飞行器向航路点的接近程度，持续调整滤波器对测量值的信任度，从而使FMS的修正行为自然涌现。该方法与控制输入成比例扩展，并可调整以适应特定航空器特性或航线条件。我们使用来自通用航空飞行器的真实ADS-B数据（分为训练集和验证集）对该方法进行了验证。不确定性传播参数在训练集上进行调优，与验证数据集相比，在预测到达时间方面达到了76%的准确率，证明了该方法在AAM运行中进行战略飞行计划验证的有效性。