On the far‐field sampling strategies for reliable Bayesian compressive diagnosis of planar arrays

Abstract

AbstractDealing with the detection of the faulty elements of planar antenna arrays with a probabilistic Bayesian compressive sensing (BCS) approach, a key asset for the reliable prediction of the actual status of the antenna under test is the sampling strategy to remotely collect the far‐field (FF) data. The aim of this letter is to provide insights into the effectiveness and the reliability of different FF sampling strategies to collect the input data for a state‐of‐the‐art array diagnosis method based on a multitask BCS technique. Representative results are shown to verify the impact of each sampling strategy on the achievable reconstructions.