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

TL;DR

This paper introduces a robust Bayesian calibration method for radio interferometers that accounts for outliers by using compound-Gaussian noise models and an iterative MAP approach, improving estimation accuracy in noisy conditions.

Contribution

It proposes a novel iterative MAP calibration algorithm based on compound-Gaussian noise models, enhancing robustness against outliers in radio interferometer calibration.

Findings

The proposed method outperforms traditional Gaussian-based calibration in noisy environments.

Numerical simulations demonstrate improved accuracy across various noise models.

The approach effectively mitigates the impact of outliers in calibration processes.

Abstract

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