Multifrequency array calibration in presence of radio frequency interferences

• Published in: Signal processing 2022-10, Vol.199, p.108613, Article 108613
• Main Authors: Mhiri, Yassine, El Korso, Mohammed Nabil, Breloy, Arnaud, Larzabal, Pascal
• Format: Article
• Language: English
• Subjects: Array signal processing; Astrophysics; Calibration; Computer Science; Instrumentation and Methods for Astrophysic; Radio astronomy; RFI mitigation; SAGE; Sciences of the Universe; Signal and Image Processing
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2022.108613

•Calibration algorithm of a radio telescope in the presence of man-made radio waves.•Array signal processing model that considers the presence of interferences.•Maximum likelihood estimation of the model parameters.•Development of a Space Alternating Generalized Expectation-Maximization algorithm. 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 they 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 closed-form expressions for the updates. Numerical simulations show a significant gain in performance for RFI corrupted data in comparison with some more classical methods.