Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

TL;DR

This paper introduces a Kalman Filter-based method for modeling uncertainty in AAM flight management systems, improving prediction accuracy of arrival times and enhancing safety in strategic flight planning.

Contribution

It presents a novel adaptive Kalman Filter approach with sigmoid-blended noise covariance for better uncertainty modeling in AAM flight planning.

Findings

Achieved 76% accuracy in predicting arrival times.

Validated method using real ADS-B data.

Demonstrated effective uncertainty propagation for AAM operations.

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.