Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace

2026-01-06

AIAA SCITECH 2026 Forum

AI Summary

This study addresses the challenge of aircraft trajectory prediction, which is subject to epistemic uncertainties arising from aircraft performance, mass, and meteorological conditions, while conventional models often entail high computational costs that compromise the balance between accuracy and efficiency. To overcome this, the authors propose a lightweight linear state-space surrogate model to replace the computationally intensive BADA model, integrated within an adaptive trajectory prediction framework that assimilates radar observations via a particle filter algorithm. The approach effectively captures nonlinear dynamics such as the CAS–Mach transition. Experimental results demonstrate significant improvements in prediction accuracy, with 46.3% and 64.7% gains over the best baseline in estimating the time of top-of-climb and bottom-of-descent, respectively, while achieving a 6.7× speedup in inference time compared to the Python implementation of BADA.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• trajectory prediction
• epistemic uncertainties
• aircraft performance
• computational efficiency
• air traffic management

Innovation

• surrogate model
• linear state space model
• particle filtering
• adaptive trajectory prediction
• BADA emulation