Planar Array Diagnosis Based on Bayesian Learning With a Bernoulli–Gaussian Prior Model

A new approach is proposed for the planar array failure diagnosis from a set of far-field measurements. Under the framework of Bayesian learning, a Bernoulli–Gaussian (BG) model is constructed to exploit the binary prior information of the unknown failure elements. Subsequently, the variational expectation maximization (EM) strategy is used to perform the Bayesian inference. Moreover, the damped generalized approximate message passing (GAMP) is further adopted to circumvent the matrix inversion in the variational EM procedure, thus making the overall process computationally very efficient. A set of representative numerical experiments are provided to validate the accuracy, efficiency, and flexibility of the proposed method.