Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

TL;DR

A novel Kalman Filter-based uncertainty propagation method that models AAM Flight Management System (FMS) architectures through sigmoid-blended measurement noise covariance that continuously adapts the filter's measurement trust based on progress toward waypoints, enabling FMS correction behavior to emerge naturally.

Abstract

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.

Figures (4)

• Figure 1: Kalman filter uncertainty propagation with traditional uLPA approach showing unrealistic sharp drop in uncertainty when the two-thirds RTA threshold activates the measurement update step, demonstrating the need for a more gradual transition method.
• Figure 2: Improved Kalman filter uncertainty propagation using sigmoid-blended measurement noise covariance with LPA threshold set to zero, showing smooth and continuous uncertainty evolution that more realistically models FMS correction behavior throughout the flight segment.
• Figure 3: Required time of arrival (RTA) predictions showing nominal trajectory with upper and lower temporal variance bounds derived from velocity covariance propagation, demonstrating the method's ability to quantify arrival time uncertainty for strategic flight plan validation.
• Figure 4: Simulation results showcasing the accuracy versus the computational cost of various methods.