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Title: Real-time unstable approach detection using sparse variational Gaussian process
Authors: Singh, Narendra Pratap
Goh, Sim Kuan
Alam, Sameer
Keywords: Engineering::Aeronautical engineering::Aviation
Issue Date: 2020
Source: Singh, Narendra P., Goh, S. K., & Alam, S. (2020). Real-time unstable approach detection using sparse variational Gaussian process. Proceedings of the 2020 International Conference on Artificial Intelligence and Data Analytics in Air Transportation (AIDA-AT), 1-10. doi:10.1109/AIDA-AT48540.2020.9049174
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.
ISBN: 978-1-7281-5380-3
DOI: 10.1109/AIDA-AT48540.2020.9049174
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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MAE Conference Papers

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