A tunnel Gaussian process model for learning interpretable flight’s landing parameters
| Autor: | Zhi Jun Lim, Sameer Alam, Sim Kuan Goh, Narendra Pratap Singh |
|---|---|
| Přispěvatelé: | School of Mechanical and Aerospace Engineering, Air Traffic Management Research Institute |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Gaussian Mixture Models
Computer science Applied Mathematics Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering] Air traffic management Aerospace Engineering Mixture model symbols.namesake Flight dynamics Space and Planetary Science Control and Systems Engineering Control theory Hull Flight Landing Parameters Learning symbols Aircrew Missed approach point Electrical and Electronic Engineering Aeronautical engineering::Aviation [Engineering] Gaussian process Instrument landing system |
| Popis: | 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 three complex trajectory datasets and compared TGP against existing methods on trajectory learning. Empirically, TGP demonstrates superior modeling performance. When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters. These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers’ displays during the approach and landing procedures, enabling necessary corrective actions. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and the Civil Aviation Authority of Singapore. |
| Databáze: | OpenAIRE |
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