Learning Generative Models for Climbing Aircraft from Radar Data
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866911101726228480 |
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| author | Pepper, Nick Thomas, Marc |
| author_facet | Pepper, Nick Thomas, Marc |
| contents | Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 26.7% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_14941 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Generative Models for Climbing Aircraft from Radar Data Pepper, Nick Thomas, Marc Systems and Control Machine Learning Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 26.7% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost. |
| title | Learning Generative Models for Climbing Aircraft from Radar Data |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2309.14941 |