Off-Grid DOA Estimation in Mutual Coupling via Robust Sparse Bayesian Learning

摘要

The most of existing off-grid direction of arrival (DOA) estimation methods are based on the perfect array-manifold. However, in practice, it is often hard to obtain a perfect array-manifold. In this paper, to achieve the DOA estimation under mutual coupling condition with low computational complexity, we propose a robust root Sparse Bayesian Learning (SBL) method. In the proposed method, firstly, we adopt the banded complex symmetric Toeplitz structure of the mutual coupling matrix to remove the negative influence of mutual coupling on DOA estimation. Then the DOA with off-grid is estimated by formulating the root-SBL strategy. Compared with the existing SBL-based algorithms, our method can not only maintain superior DOA estimation performance under the condition of mutual coupling, especially with strong mutual coupling, but also have lower computational complexity. Simulation results demonstrate that the proposed method can still accurately estimate DOAs under strong mutual coupling conditions, while other SBL-based methods fail to work.