Reconstruction of Sparse Array Pattern from a Limited Number of Samples with a Compressive Sensing Inspired Approach

In this paper, we discuss an approach for the reconstruction of the far-field pattern of unknown sparse sources. Starting from the measurement of a small number of near-field samples, we employ a compressive-sensing inspired elaboration that performs a smooth-weighted constrained minimization. This technique allows better performance with respect to similar concurrent techniques that employ the same computational effort for elaboration.