Learning Antenna Pointing Correction in Operations: Efficient Calibration of a Black Box

TL;DR

This paper introduces an efficient offline calibration method for antenna pointing correction that leverages existing operational data and linear regression, eliminating the need for downtime and improving ground station accuracy.

Contribution

It presents a novel calibration approach that uses standard antenna data and linear regression to correct antenna pointing without operational downtime.

Findings

Effective calibration achieved in real-world setup

No downtime required for calibration process

Utilizes existing signal monitoring data

Abstract

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.