Summary of Learning Antenna Pointing Correction in Operations: Efficient Calibration Of a Black Box, by Leif Bergerhoff
Learning Antenna Pointing Correction in Operations: Efficient Calibration of a Black Box
by Leif Bergerhoff
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed offline pointing calibration method for operational antenna systems minimizes downtime and utilizes technical signal information typically used for monitoring and control purposes. The approach generates training data using an operational satellite contact and standard antenna interface, then learns parameters through linear regression. This efficient method is demonstrated in a real-world setup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to calibrate satellite antennas without stopping them. It uses the signals already being sent to the ground station to figure out how to adjust the antenna’s position. The approach generates its own training data by analyzing the signals from one satellite contact, and then uses simple math to determine the best coordinates for the antenna. This method is tested in a real-world setting and shows promising results. |
Keywords
» Artificial intelligence » Linear regression
