A tunnel Gaussian process model for learning interpretable flight’s landing parameters
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 inter...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/153283 |
| _version_ | 1826123137499004928 |
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| author | Goh, Sim Kuan Lim, Zhi Jun Alam, Sameer Singh, Narendra Pratap |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Goh, Sim Kuan Lim, Zhi Jun Alam, Sameer Singh, Narendra Pratap |
| author_sort | Goh, Sim Kuan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:59:44Z |
| format | Journal Article |
| id | ntu-10356/153283 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:59:44Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1532832021-12-11T20:10:32Z A tunnel Gaussian process model for learning interpretable flight’s landing parameters Goh, Sim Kuan Lim, Zhi Jun Alam, Sameer Singh, Narendra Pratap School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute Engineering::Aeronautical engineering::Aviation Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gaussian Mixture Models Flight Landing Parameters Learning 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. 2021-12-11T13:21:45Z 2021-12-11T13:21:45Z 2021 Journal Article Goh, S. K., Lim, Z. J., Alam, S. & Singh, N. P. (2021). A tunnel Gaussian process model for learning interpretable flight’s landing parameters. Journal of Guidance, Control, and Dynamics, 44(12), 2263-2275. https://dx.doi.org/10.2514/1.G005802 0731-5090 https://hdl.handle.net/10356/153283 10.2514/1.G005802 2011.09335v3 12 44 2263 2275 en Journal of Guidance, Control, and Dynamics © 2021 American Institute of Aeronautics and Astronautics. All rights reserved. This paper was published in Journal of Guidance, Control, and Dynamics and is made available with permission of American Institute of Aeronautics and Astronautics. application/pdf |
| spellingShingle | Engineering::Aeronautical engineering::Aviation Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gaussian Mixture Models Flight Landing Parameters Learning Goh, Sim Kuan Lim, Zhi Jun Alam, Sameer Singh, Narendra Pratap A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title | A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title_full | A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title_fullStr | A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title_full_unstemmed | A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title_short | A tunnel Gaussian process model for learning interpretable flight’s landing parameters |
| title_sort | tunnel gaussian process model for learning interpretable flight s landing parameters |
| topic | Engineering::Aeronautical engineering::Aviation Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gaussian Mixture Models Flight Landing Parameters Learning |
| url | https://hdl.handle.net/10356/153283 |
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