Robust diagnosis of planar antenna arrays through a Bayesian compressive sensing approach

A novel methodology for the robust diagnosis of large planar phased arrays is presented in this work. The developed strategy exploits the inherent sparsity of failures in large arrangements thanks to a customized Bayesian Compressive Sensing (BGS)-based approach. The detection, localization and characterization of faults is accomplished by processing noisy far-field measurements of the antenna under test (AUT) and exploiting the knowledge of the pattern radiated by the gold (error-free) antenna. Some representative numerical results are shown in order to assess the effectiveness of the proposed diagnosis technique, as well as to verify its robustness to noise occurring in real measurements.