An Off-Grid Block-Sparse Bayesian Method for Direction of Arrival and Polarization Estimation

Abstract

The problem of DOA and polarization parameter estimation is considered in this paper from a perspective of sparse reconstruction. We present a novel off-grid hierarchical block-sparse Bayesian method for DOA and polarization parameter estimation to improve the estimation accuracy. Firstly, an off-grid model is formulated via the first-order Taylor expansion of the source steering vector. Then, a block-sparse vector is constructed based on sparse Bayesian inference, on which a two-layer hierarchical prior is imposed to promote block sparsity and internal sparsity simultaneously. Finally, the variables and model parameters are updated alternately by adopting the variational Bayesian approximation. In addition, the Cramer–Rao bound for DOA and polarization estimation, the convergence property and the computational complexity analysis of the proposed method are derived. Compared with the existing sparse reconstruction methods and the traditional subspace-based methods, the proposed method can achieve higher estimation accuracy. Simulation results demonstrate the effectiveness and notable performance of the proposed method.