Learning Generative Models for Climbing Aircraft from Radar Data
Nick Pepper Marc Thomas
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 66.3% 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.