用卡尔曼滤波动态预测飞行不确定性，提升空中交通管理安全性。
Kalman Filtering Based Flight Management System Modeling for AAM Aircraft
- 基于逻辑斯蒂函数调节测量噪声，随航点进度自适应信任数据。
- 在真实飞行动态数据上验证，到达时间预测准确率达76%。
- 适合需要精准路径规划的先进空中交通系统开发者。
先进空中交通（AAM）运营需战略飞行规划服务，以预测时空不确定性，从而安全评估飞行计划是否避开天气区、禁飞区及通信导航监视（CNS）中断区域。当前AAM车辆的不确定性估计多依赖保守的线性模型，因真实性能数据有限。本文提出一种基于卡尔曼滤波的新型不确定性传播方法，通过逻辑斯蒂混合测量噪声协方差建模AAM飞行管理系统（FMS）架构。与固定阈值方法不同，该方法随航点进展动态调整滤波器对测量的信任度，使FMS修正行为自然涌现。该方法与控制输入成比例扩展，且可调参适配特定机型或航线条件。使用通用航空飞机的真实ADS-B数据进行验证，数据分为训练集与验证集。在训练集上校准不确定性传播参数后，在验证集上预测到达时间的准确率达76%，证明了该方法在AAM战略飞行计划验证中的有效性。
原文摘要 · Abstract (English)
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.
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