2-D off-grid DOA estimation using sparse Bayesian learning with L-shape array

To further improve the performance of the two-dimensional (2-D) direction-of-arrival (DOA) estimation, we propose a novel method employing the sparse Bayesian learning (SBL). In this new method the 2-D DOA estimation is divided into two independent 1-D DOA estimations which can be resolved by the SBL respectively. To avoid the negative effects on the accuracy of DOA estimation and signal reconstruction caused by grid mismatch in sparse reconstruction, the off-grid model is adopted. By exploiting the self-coherent property of signals, pair matching of elevations and azimuths is accomplished by pair matching of the two sets of reconstructed signals. Numerical simulation results demonstrate the superior performance of the proposed method.