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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.15247 |
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| _version_ | 1866917705192308736 |
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| author | Bergerhoff, Leif |
| author_facet | Bergerhoff, Leif |
| contents | We propose an efficient offline pointing calibration method for operational antenna systems which does not require any downtime. Our approach minimizes the calibration effort and exploits technical signal information which is typically used for monitoring and control purposes in ground station operations. Using a standard antenna interface and data from an operational satellite contact, we come up with a robust strategy for training data set generation. On top of this, we learn the parameters of a suitable coordinate transform by means of linear regression. In our experiments, we show the usefulness of the method in a real-world setup. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Antenna Pointing Correction in Operations: Efficient Calibration of a Black Box Bergerhoff, Leif Machine Learning We propose an efficient offline pointing calibration method for operational antenna systems which does not require any downtime. Our approach minimizes the calibration effort and exploits technical signal information which is typically used for monitoring and control purposes in ground station operations. Using a standard antenna interface and data from an operational satellite contact, we come up with a robust strategy for training data set generation. On top of this, we learn the parameters of a suitable coordinate transform by means of linear regression. In our experiments, we show the usefulness of the method in a real-world setup. |
| title | Learning Antenna Pointing Correction in Operations: Efficient Calibration of a Black Box |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.15247 |