Adaptive Antenna Diagnosis Based on Clustering Block Sparse Bayesian Learning

Massive multiple-input multiple-output systems in military or harsh environments are vulnerable to blockage or damage, changing the geometry of the array and distort the far-field radiation pattern of the array. Therefore, the blocked array elements must be diagnosed in time under a low signal-to-noise ratio. We propose an adaptive iterative diagnostic algorithm based on clustering block sparse Bayesian learning (CBSBL) to address the coupling problem of the direction of arrival (DOA) and antenna blockage estimations. We construct sparse signals by approximating the difference between the radiation pattern of the fault-free reference antenna and the antenna under test with blockage (or damage). Based on the sparse characteristics of the blocked antennas and the correlation characteristics of adjacent coefficients, the structural clustering method is used to deal with the sparse coefficients. The estimation accuracy is improved by encouraging the dependence between the adjacent coefficients by accurately controlling the neighboring hyperparameters. The proposed algorithm provides satisfactory results under the premise of unknown DOA.