Sparse continuous-field and super-resolution method for direction-of-arrival estimation

Abstract

In the application of direction-of-arrival (DOA) estimation based on sensor array, bearing-discretization has been a major technique for the sparsity-based methods in the past decade. However, discretization leads to the modeling error of sparse representation, which usually deteriorates the accuracy of DOA estimation. In this paper, a sparse continuous-field and super-resolution method (SCSM) is proposed to overcome the discretization issue. SCSM estimates DOA in the continuous bearing-space by solving a minimization problem, which combines the measurement covariance fitting criterion with the source-sparsity constraint characterized by minimizing an atomic norm. This minimization problem has a unique solution. After obtained the solution uniquely, Prony's method is utilized to estimate the DOA of sources. Therefore, SCSM has the capability to acquire a sparse and accurate estimation of DOA. In addition, it has some other advantages, including excellent super-resolution capability, no requirement of source number, applicability to arbitrary number of snapshots. Numerical results demonstrate the superior performance of the presented method.