Distributed image reconstruction for very large arrays in radio astronomy

Abstract

Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox. Their large number of receptors (up to millions) allow theoretically unprecedented high imaging resolution. In the same time, the ultra massive amounts of samples makes the data transfer and computational loads (correlation and calibration) order of magnitudes too high to allow any currently existing image reconstruction algorithm to achieve, or even approach, the theoretical resolution. We investigate here decentralized and distributed image reconstruction strategies which select, transfer and process only a fraction of the total data. The loss in MSE incurred by the proposed approach is evaluated theoretically and numerically on simple test cases.

arXiv commentSensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th, Jun 2014, Coruna, Spain. 2014

References in corpus (4)

• Compressed sensing imaging techniques for radio interferometry
• Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging
• Advances in Calibration and Imaging Techniques in Radio Interferometry
• Positive deconvolution for superimposed extended source and point sources

Cited by in corpus (5)

• Scalable splitting algorithms for big-data interferometric imaging in the SKA era
• Parallel Calibration for Sensor Array Radio Interferometers
• Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
• Distributed Deblurring of Large Images of Wide Field-Of-View
• Robust Calibration of Radio Interferometers in Multi-Frequency Scenario