Off-Grid Error Calibration for Single Snapshot DOA Estimation Based on Sparse Bayesian Learning

Abstract

In this work, the grid mismatch problem for direction of arrival (DOA) estimation of array is studied. To solve the grid mismatch impact on the DOA estimation accuracy, a novel off-grid model for DOA estimation using sinc interpolation is proposed in this paper. Specifically, the off-grid error is represented as parameter to be estimated in the observation model. Under the framework of sparse Bayesian learning (SBL), the off-grid error is estimated via the variational Bayesian expectation maximization (VBEM) and eliminated by updating the grids with sinc interpolation. The simulation results demonstrate superior performance of the proposed method over state-of-the-art methods are reported, as well.