A Bayesian Compressive Sensing-Based Planar Array Diagnosis Approach From Near-Field Measurements
Zhenwei Lin, Yaowu Chen, Xuesong Liu, Rongxin Jiang, Binjian Shen
Abstract
Array diagnosis is an important tool for detecting and correcting array antenna failures. In this letter, a high-precision planar array diagnosis method based on the Bayesian compressive sensing (BCS) theory is proposed. The model of a near-field signal with a spherical wavefront is used to acquire the measured data. Then, the difference between the beam pattern of the reference array and the array under test is obtained. The array diagnosis problem involves finding the difference between the weights of the reference and of the array under test with known differences between patterns. This problem is reformulated in a Bayesian compressive sensing framework and can be efficiently solved using a fast relevance vector machine. Numerical results confirm the superiority of the proposed method in terms of diagnostic accuracy and computational efficiency than those in previous studies.