Grid Adaptive Sparse Bayesian Learning for 2D-DOA Estimation with L-shape Array

Sparsity based methods have gained its popularity in two-dimensional (2D) direction-of-arrival (DOA) estimation in recent years. However, these methods suffer from the off-grid problem, and also need additional angle pairing process, which results in degraded performance when applied in practice. In this paper, to address these problems, a novel and effective method named grid adaptive sparse Bayesian learning (GASBL) is proposed for 2D-DOA estimation with L-shape array from the perspective of sparse Bayesian learning. Specifically, an off-grid DOA model is proposed to enable grid adaptive refinement, and the auxiliary compound electric angle (CEA) is introduced to achieve automatic angle pairing of the elevation angles and azimuth angles. Then, a hierarchical probability framework with Laplacian prior is imposed. Finally, the 2D-DOA estimation problem is solved by Bayesian inferences. Compared with the state-of-the-art approaches, numerical results highlight the proposed method with more superior performance in terms of high angle resolution and robustness against the noise.