Learning Generative Models for Climbing Aircraft from Radar Data
This work addresses trajectory prediction for climbing aircraft, which is incremental as it builds upon the existing BADA model with data-driven corrections.
The paper tackles the problem of inaccurate trajectory prediction for climbing aircraft due to epistemic uncertainties by proposing a generative model that enriches the BADA model with a data-learned functional correction to thrust, resulting in a 26.7% reduction in arrival time error compared to BADA.
Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 26.7% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost.