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

TL;DR

This paper introduces a fast, adaptive aircraft trajectory prediction method using linear state space models and particle filtering, significantly improving accuracy and computational efficiency in air traffic management.

Contribution

It presents a novel surrogate TP model based on LSSMs that emulates complex PDE-based models and an adaptive filtering algorithm for improved accuracy using radar data.

Findings

LSSM surrogate models are 6.7 times faster than BADA in Python.

The framework improves time-to-top-of-climb estimates by 46.3%.

The approach enhances state estimation during climb and descent phases.

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.