Calibration of radio interferometers using a sparse DoA estimation framework

The calibration of modern radio interferometers is a significant challenge, specifically at low frequencies. In this perspective, we propose a novel iterative calibration algorithm, which employs the popular sparse representation framework, in the regime where the propagation conditions shift dissimilarly the directions of the sources. More precisely, our algorithm is designed to estimate the apparent directions of the calibration sources, their powers, the directional and undirectional complex gains of the array elements and their noise powers, with a reasonable computational complexity. Numerical simulations reveal that the proposed scheme is statistically efficient at low SNR and even with additional non-calibration sources at unknown directions.

[1]  S. Tol.  Bayesian estimation for ionospheric calibration in radio astronomy , 2009 .

[2]  Marius Pesavento,et al.  Direction finding and array calibration based on sparse reconstruction in partly calibrated arrays , 2014, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[3]  Mike E. Davies,et al.  Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.

[4]  Rainer Beck,et al.  Square kilometre array , 2010, Scholarpedia.

[5]  S. Markoff,et al.  LOFAR - low frequency array , 2006 .

[6]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[7]  H. Rottgering,et al.  Ionospheric calibration of low frequency radio interferometric observations using the peeling scheme I. Method description and first results , 2009, 0904.3975.

[8]  Stefan J. Wijnholds,et al.  Fast gain calibration in radio astronomy using alternating direction implicit methods: Analysis and applications , 2014, 1410.2101.

[9]  A. Boonstra,et al.  Sky Noise Limited Snapshot Imaging in the Presence of RFI with Lofar’s Initial Test Station , 2004 .

[10]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[11]  H. E. Smith,et al.  THE REVISED 3C CATALOG OF RADIO SOURCES: A REVIEW OF OPTICAL IDENTIFICATIONS AND SPECTROSCOPY. , 1976 .

[12]  Visa Koivunen,et al.  Robust iterative hard thresholding for compressed sensing , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[13]  R. Tibshirani,et al.  PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.

[14]  24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, August 29 - September 2, 2016 , 2016, European Signal Processing Conference.

[15]  Mike E. Davies,et al.  Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance , 2010, IEEE Journal of Selected Topics in Signal Processing.

[16]  Alle-Jan van der Veen,et al.  Signal Processing Tools for Radio Astronomy , 2013, Handbook of Signal Processing Systems.

[17]  P. Vandergheynst,et al.  Compressed sensing imaging techniques for radio interferometry , 2008, 0812.4933.

[18]  S. Wijnholds.  Fish-Eye Observing with Phased Array Radio Telescopes , 2010 .

[19]  Namir E. Kassim,et al.  Beyond the isoplanatic patch in the VLA Low-frequency Sky Survey , 2004, SPIE Astronomical Telescopes + Instrumentation.

[20]  Alle-Jan van der Veen,et al.  Calibration challenges for future radio telescopes , 2010, IEEE Signal Processing Magazine.