Power Pattern Matching Through the Hybrid Bayesian Compressive Sensing

Abstract

In this work, the synthesis problem of sparse linear arrays complying with user defined power masks is addressed by means of a Compressive Sensing $(CS)$-based methodology. A novel hybrid Bayesian $CS$ (BCS) approach integrating a mask-constrained synthesis within a $BCS$ solver is proposed, enabling an effective and efficient tool for the design of sparse arrays, starting from arbitrary user-defined requirements. A simple example validating the proposed approach is reported and discussed.