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Approach and landing accidents have resulted in a significant number of hulllosses worldwide. Technologies (e.g., instrument landing system) and procedures(e.g., stabilized approach criteria) have been developed to reduce the risks.In this paper, we propose a data-driven method to learn and interpret flight'sapproach and landing parameters to facilitate comprehensible and actionableinsights into flight dynamics. Specifically, we develop two variants of tunnelGaussian process (TGP) models to elucidate aircraft's approach and landingdynamics using advanced surface movement guidance and control system (A-SMGCS)data, which then indicates the stability of flight. TGP hybridizes thestrengths of sparse variational Gaussian process and polar Gaussian process tolearn from a large amount of data in cylindrical coordinates. We examine TGPqualitatively and quantitatively by synthesizing three complex trajectorydatasets and compared TGP against existing methods on trajectory learning.Empirically, TGP demonstrates superior modeling performance. When applied tooperational A-SMGCS data, TGP provides the generative probabilistic descriptionof landing dynamics and interpretable tunnel views of approach and landingparameters. These probabilistic tunnel models can facilitate the analysis ofprocedure adherence and augment existing aircrew and air traffic controllers'displays during the approach and landing procedures, enabling necessarycorrective actions.

A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters