Multifrequency Array Calibration in Presence of Radio Frequency Interferences

TL;DR

This paper introduces a novel calibration method for radio interferometers that effectively mitigates the bias caused by Radio Frequency Interferences by modeling RFI as low-rank structured noise across multiple frequencies.

Contribution

It proposes a new RFI-aware calibration model and a maximum likelihood estimation approach using a SAGE algorithm with closed-form updates, improving calibration accuracy under RFI.

Findings

Significant performance gains over classical methods in RFI-affected data

Effective modeling of RFI as low-rank structured noise across frequencies

Enhanced calibration accuracy in high-sensitivity radio interferometers

Abstract

Radio interferometers are phased arrays producing high-resolution images from the covariance matrix of measurements. Calibration of such instruments is necessary and is a critical task. This is how the estimation of instrumental errors is usually done thanks to the knowledge of referenced celestial sources. However, the use of high sensitive antennas in modern radio interferometers (LOFAR, SKA) brings a new challenge in radio astronomy because there are more sensitive to Radio Frequency Interferences (RFI). The presence of RFI during the calibration process generally induces biases in state-of-the-art solutions. The purpose of this paper is to propose an alternative to alleviate the effects of RFI. For that, we first propose a model to take into account the presence of RFI in the data across multiple frequency channels thanks to a low-rank structured noise. We then achieve maximum likelihood estimation of the calibration parameters with a Space Alternating Generalized Expectation-Maximization (SAGE) algorithm for which we derive originally two sets of complete data allowing close form expressions for the updates. Numerical simulations show a significant gain in performance for RFI corrupted data in comparison with some more classical methods.