Array covariance matrix‐based sparse Bayesian learning for off‐grid direction‐of‐arrival estimation

Abstract

An off‐grid direction‐of‐arrival (DOA) estimation method using sparse Bayesian learning (SBL) based on an array covariance matrix is presented. By the vectorisation operator, a new off‐grid model that has a wider aperture and can therefore detect an increased number of sources is built. A scheme of SBL is utilised to estimate off‐grid DOAs without needing to know the number of sources. Owing to the extended aperture and off‐grid model, the proposed method cannot only improve the angle resolution but also enhance the accuracy of DOA estimation. Simulation results demonstrate the efficiency of the proposed method and illustrate the performance improvement over the existing methods.