Compressive Sensing Pattern Matching Techniques for Synthesizing Planar Sparse Arrays

In this paper, the design of sparse planar arrays is yielded through a set of innovative and efficient pattern matching algorithms within the Bayesian Compressive Sensing (BCS) framework. Towards this end, the sparse synthesis problem is formulated in a probabilistic fashion and the single-task (ST) and the multi-task (MT) BCS solutions are derived. The results from a numerical validation concerned with different aperture size and target patterns prove that the proposed implementations enable an element saving ranging from 25% up to 87%, while achieving a reliable beam control