Having an accurate calibration method is crucial for any scientific research done by a radio telescope. The next generation radio telescopes such as the Square Kilometre Array (SKA) will have a large number of receivers which will produce exabytes of data per day. In this paper we propose new direction-dependent and independent calibration algorithms that, while requiring much less storage during calibration, converge very fast. The calibration problem can be formulated as a non-linear least square optimization problem. We show that combining a block-LDU decomposition with Gauss-Newton iterations produces systems of equations with convergent matrices. This allows significant reduction in complexity per iteration and very fast converging algorithms. We also discuss extensions to direction-dependent calibration. The proposed algorithms are evaluated using simulations.
|Title of host publication||2018 26th European Signal Processing Conference (EUSIPCO)|
|Number of pages||5|
|Publication status||Published - 2018|
|Event||EUSIPCO 2018: 26th European Signal Processing Conference - Rome, Italy|
Duration: 3 Sep 2018 → 7 Sep 2018
Conference number: 26
|Period||3/09/18 → 7/09/18|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Covariance Matching
- Non-Linear Optimization
- Radio Astronomy