BCS-based formulations for antenna arrays synthesis

A review of recently introduced Bayesian approaches for the synthesis of maximally-sparse antenna arrays is presented. More specifically, the use of numerically-efficient techniques based on the Bayesian Compressive Sampling (BCS) is introduced to solve the linear array design problem. Towards this end, a probabilistic framework is exploited to formulate the synthesis problem, and a fast relevance vector machine (RVM) is employed for the computation of the optimal excitations and geometries. An illustrative numerical validation is presented to show the features of the proposed approach.