On the Far-Field Sampling Strategies for Reliable Bayesian Compressive Diagnosis of Planar Arrays

Dealing 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.