Assessment of approach separation with probabilistic aircraft wake vortex recognition via deep learning

Compared to static aircraft wake separation during landing/departure, reducing separation minima related to wake turbulence without compromising safety has initially demonstrated operational benefits, while dynamic pairwise separation remains under development. This paper proposes a two-stage probabilistic deep learning framework for wake vortex recognition and duration assessment, using wake images from the Light Detection and Ranging (LiDAR) instrument at Hong Kong International Airport. The first stage consists of vortex core locating utilising the Convolutional Neural Network (CNN), and the second stage predicts vortex strength within the Region of Interest (ROI), derived from raw images based on the initial core locating results. The existence of vortices is assessed upon the reliable probabilistic estimation of vortex movement under specific wind conditions and the estimation of its endurance in the final approach path. Furthermore, the prediction uncertainty is explained from the feature analysis aspect. Computational results indicate that the proposed two-stage CNN framework excels in estimating the spatial features and strength of coupled vortices. The wake duration measurement suggests a high potential for separation minima reduction when the crosswind exceeds (2–3)m/s. This will establishe conditions for onboard real-time wake monitoring, and the development of dynamic pairwise and meteorologically-related aircraft separation systems.