A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters
Sim Kuan Goh, Narendra Pratap Singh, Zhi Jun Lim, Sameer Alam

TL;DR
This paper introduces a novel Tunnel Gaussian Process model that learns and interprets aircraft approach and landing dynamics from large datasets, improving understanding and safety during critical flight phases.
Contribution
The paper presents two variants of TGP models that combine sparse variational and polar Gaussian processes for interpretable, probabilistic modeling of flight landing parameters.
Findings
TGP outperforms existing methods in trajectory learning accuracy.
TGP provides interpretable probabilistic insights into landing dynamics.
Application to real data demonstrates enhanced analysis of approach stability.
Abstract
Approach and landing accidents have resulted in a significant number of hull losses 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's approach and landing parameters to facilitate comprehensible and actionable insights into flight dynamics. Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing…
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Taxonomy
MethodsGaussian Process
