LARZABAL , Pascal

The Higher-Order Singular Value Decomposition (HOSVD) is a possible generalization of the Singular Value Decomposition (SVD) to tensors, which have been successfully applied in various domains. Unfortunately, this decomposition is computationally demanding. Indeed, the HOSVD of a Nth- order tensor involves the computation of the SVD of N...

This paper investigates calibration of sensor arrays in the radio astronomy context. Current and future radio telescopes require com-putationally efficient algorithms to overcome the new technical challenges as large collecting area, wide field of view and huge data volume. Specifically, we study the calibration of radio interferometry stations...

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...

In radio astronomy, accurate calibration is of crucial importance for the new generation of radio inter-ferometers. More specifically, because of the potential presence of outliers which affect the measured data, robustness needs to be ensured. On the other hand, calibration is improved by taking advantage of these new instruments and...

On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the l20 pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the l20 term with a group minimax concave penalty with suitable parameters. This...

Robust calibration of next-generation radio-interferometers, as the square kilometer array (SKA) for instance, is a crucial prepro-cessing step for sky imaging. Recently, several robust calibration es-timators based on the use of well known strong sources in the field of view (FOV) have been proposed in the literature. For that, usually a...

This paper is devoted to two classical sparse problems in array processing: Channel estimation and DOA estimation. It is shown after some background and some recent results in l0 optimization how this latter can be used, at the same computational cost, in order to obtain improvement in comparison with l1 optimization for sparse estimation.