Real-time Unstable Approach Detection Using Sparse Variational Gaussian Process

Abstract

Worldwide, Air Navigation Service Providers (ANSP) are striving to exceed the desired safety levels. The Terminal Manoeuvre Area (TMA) is one of the most safety-critical areas in ATM as it encompasses the most critical phase of flight, i.e., departure and landing. An aircraft, during the final approach phase, is required to remain in a stable configuration and prevent any undesired state such as an unstable approach, which may subsequently lead to incidents/accidents such as Go-Around, Runway Excursions, etc. In this paper, we propose a data-driven framework to model the aircraft 4D trajectories in the final approach phase by adopting sparse variational Gaussian process (SVGP) model. The model is trained to learn the aircraft landing dynamics from Advanced Surface Movement Guidance and Control System (A-SMGCS) data, during the final approach phase. We experimentally demonstrate that SVGP provides an interpretable probabilistic bound of aircraft parameters that can quantify deviation and perform real-time anomaly detection. The findings of this work can increase situational awareness of the air traffic controller and has implications for the design of a new approach procedure in complex runway configurations such as parallel approach.