One of the key feature characterizing the new-generation of radio telescopes is the large number of their antenna elements. Built in 2010, the Low-Frequency Array (LOFAR) is currently the largest radio telescope in operation with 100 000 antenna dipoles distributed across several European countries [1]. Furthermore, the upcoming Square-Kilometer Array (SKA) will be made up of more than 130 000 antennas [2]. Such a large number of antennas will make it possible to acquire increasingly accurate and detailed images of the celestial vault. Such images will form the basis for promising developments in astrophysics and cosmology in the coming years.

However, as in any other “remote sensing system”, the signal collected by a radio telescope is affected by different sources of disturbance that will degrade the quality of the collected image.

Consequently, to take full advantage of the potential of the new radio telescopes, one must first take the disturbance into account. In general, this disturbance is characterized as a zeromean Gaussian random process with possibly unknown correlation structure. Unfortunately, the Gaussian assumption is often violated by some impulsive noise sources whose presence may produce a breakdown of the performance of Gaussian-based procedures. In order to take into consideration the impulsive behavior of the disturbance, different classes of heavy-tailed distributions (including the Gaussian one as a special case) has been proposed in array processing literature [3]. However, any choice of a specific heavy-tailed distribution is arbitrary to some extent, since none of them, taken individually, can fully describe the physical behavior of the disturbance.

Then, the crucial question is: would it be possible to derive robust imaging algorithms able to guarantee a predetermined level of performance without assuming any specific non-Gaussian noise model? If yes, which is the price to pay? In the context of array processing, a positive answer has been recently provided in [4]. By letting the number of antenna elements grow unboundedly, a robust test for the detection of sources of interest in heavy-tailed noise with unspecified distribution has been proposed. Roughly speaking, the findings in [4] tell us that if the number of antenna elements is sufficiently large, it is possible to derive source detection/estimation/imaging algorithms able to achieve the desired performance regardless the (generally unknown) disturbance model.

While in a classical array processing framework, having a massive amount of antenna sensors may be unrealistic due to physical constraints, the modern radio telescopes naturally fit this requirement. Consequently, this SIDEREAL project has the following two main aims: