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The main objective of this work is to accelerate the Maximum-Likelihood (ML)estimation procedure in radio interferometric calibration. We introduce theOS-LS and the OS-SAGE radio interferometric calibration methods, as acombination of the Ordered-Subsets (OS) method with the Least-Squares (LS) andSpace Alternating Generalized Expectation maximization (SAGE) calibrationtechniques, respectively. The OS algorithm speeds up the ML estimation andachieves nearly the same level of accuracy of solutions as the one obtained bythe non-OS methods. We apply the OS-LS and OS-SAGE calibration methods tosimulated observations and show that these methods have a much higherconvergence rate relative to the conventional LS and SAGE techniques. Moreover,the obtained results show that the OS-SAGE calibration technique has a superiorperformance compared to the OS-LS calibration method in the sense of achievingmore accurate results while having significantly less computational cost.

Radio Interferometric Calibration via Ordered-Subsets Algorithms: OS-LS and OS-SAGE calibrations