  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.
