Fine tuning consensus optimization for distributed radio interferometric calibration

We recently proposed the use of consensus optimization as a viable and effective way to improve the quality of calibration of radio interferometric data. We showed that it is possible to obtain far more accurate calibration solutions and also to distribute the compute load across a network of computers by using this technique. A crucial aspect in any consensus optimization problem is the selection of the penalty parameter used in the alternating direction method of multipliers (ADMM) iterations. This affects the convergence speed as well as the accuracy. In this paper, we use the Hessian of the cost function used in calibration to appropriately select this penalty. We extend our results to a multi-directional calibration setting, where we propose to use a penalty scaled by the squared intensity of each direction.Comment: Draft, to be published in the Proceedings of the 24th European Signal Processing Conference (EUSIPCO-2016) in 2016, published by EURASI

Adaptive ADMM in Distributed Radio Interferometric Calibration

Distributed radio interferometric calibration based on consensus optimization has been shown to improve the estimation of systematic errors in radio astronomical observations. The intrinsic continuity of systematic errors across frequency is used by a consensus polynomial to penalize traditional calibration. Consensus is achieved via the use of alternating direction method of multipliers (ADMM) algorithm. In this paper, we extend the existing distributed calibration algorithms to use ADMM with an adaptive penalty parameter update. Compared to a fixed penalty, its adaptive update has been shown to perform better in diverse applications of ADMM. In this paper, we compare two such popular penalty parameter update schemes: residual balance penalty update and spectral penalty update (Barzilai-Borwein). We apply both schemes to distributed radio interferometric calibration and compare their performance against ADMM with a fixed penalty parameter. Simulations show that both methods of adaptive penalty update improve the convergence of ADMM but the spectral penalty parameter update shows more stability.Comment: Draft, to be published in the Proceedings of the 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (IEEE CAMSAP 2017), published by IEE

Multi-frequency calibration for DOA estimation with distributed sensors

In this work, we investigate direction finding in the presence of sensor gain uncertainties and directional perturbations for sensor array processing in a multi-frequency scenario. Specifically, we adopt a distributed optimization scheme in which coherence models are incorporated and local agents exchange information only between connected nodes in the network, i.e., without a fusion center. Numerical simulations highlight the advantages of the proposed parallel iterative technique in terms of statistical and computational efficiency