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In this paper, we aim to design robust estimation techniques based on thecompound-Gaussian (CG) process and adapted for calibration of radiointerferometers. The motivation beyond this is due to the presence of outliersleading to an unrealistic traditional Gaussian noise assumption. Consequently,to achieve robustness, we adopt a maximum a posteriori (MAP) approach whichexploits Bayesian statistics and follows a sequential updating procedure here.The proposed algorithm is applied in a multi-frequency scenario in order toenhance the estimation and correction of perturbation effects. Numericalsimulations assess the performance of the proposed algorithm for differentnoise models, Student's t, K, Laplace, Cauchy and inverse-Gaussiancompound-Gaussian distributions w.r.t. the classical non-robust Gaussian noiseassumption.

Bayesian Calibration using Different Prior Distributions: an Iterative Maximum A Posteriori Approach for Radio Interferometers