Off-grid fast relevance vector machine algorithm for direction of arrival estimation

Direction of arrival (DOA) estimation is a basic and important problem in signal processing and has been widely applied. Its research has been advanced by the recently developed methods based on Bayesian compressive sensing (BCS). Among these methods, the ones combined with an off-grid (OG) model have been proved to be more accurate than the on-grid ones. However, the conventional BCS-based methods have a disadvantage of the slow speed. In this study, a high-efficiency iterative algorithm, based on the fast relevance vector machine and the OG model, is developed. This new approach applies to both the single- and multiple-snapshot cases. Numerical simulations show that the proposed method estimates DOAs more accurately than the l1-penalisation method and computes more efficiently than the conventional BCS-based methods. Finally, comparisons with state-of-the-art methods and Cramer–Rao bound are also reported.