The Diagnosis of Planar Array Antenna Based on Pattern-Coupled Sparse Bayesian Learning

With the increasing requirement of array antenna performance, the failure rate of array antenna is also increasing. In this paper, a pattern-coupled hierarchical Gaussian prior model is constructed by exploiting the block-sparse structure of the array under test (AUT). The model shares hyperparameters by capturing the pattern dependencies among neighboring coefficients. Thus, under the Bayesian framework, Pattern-coupled sparse Bayesian learning (PCSBL) is proposed. Besides, generalized approximate message passing (GAMP) is used to reduce the computation time. Experimental results show that this method has high precision and low computational complexity in the array fault diagnosis.