Calibration of Radio Interferometers Using a Sparse DoA Estimation Framework

TL;DR

This paper introduces a new iterative calibration algorithm for radio interferometers that uses sparse representation to accurately estimate source directions, gains, and noise powers even under challenging conditions.

Contribution

A novel sparse DoA-based calibration framework that improves accuracy and efficiency in low-frequency radio interferometry calibration tasks.

Findings

Effective at low SNR conditions

Handles non-calibration sources at unknown directions

Statistically efficient in simulations

Abstract

The calibration of modern radio interferometers is a significant challenge, specifically at low frequencies. In this perspective, we propose a novel iterative calibration algorithm, which employs the popular sparse representation framework, in the regime where the propagation conditions shift dissimilarly the directions of the sources. More precisely, our algorithm is designed to estimate the apparent directions of the calibration sources, their powers, the directional and undirectional complex gains of the array elements and their noise powers, with a reasonable computational complexity. Numerical simulations reveal that the proposed scheme is statistically efficient at low SNR and even with additional non-calibration sources at unknown directions.