Planar array diagnosis through compressive sensing: A preliminary assessment

An approach based on the Bayesian Compressive Sensing (BCS) is presented in this work for the diagnosis of failed elements in planar arrays. After formulating the relation between the measured “difference” far-field pattern samples and the failed elements as a linear relationship with sparse unknowns, the BCS paradigm is adopted in order to get an estimate of the location and to retrieve the “entity” of the failures while enforcing the sparsity of the solution. The approach is numerically validated through preliminary examples aimed t showing the effectiveness and reliability of the proposed methodology also in the presence of noisy data.