Efficient Direction-of-arrival Estimation via Sparse Bayesian Learning

Sparsity-based Direction-Of-Arrival (DOA) estimation via l1-norm optimization requires fine tuning of the regularization parameter and large computational times. To alleviate these problems, this paper presents an efficient approach based on Sparse Bayesian Learning (SBL). The presented approach constructs and solves the jointly sparse DOA estimation model in real domain by making good use of the special geometry of the uniform linear array. Furthermore, the basis pruning mechanism of sparse Bayesian learning is modified to speed up the convergence rate. Simulation results demonstrate that the presented approach provides higher spatial resolution and accuracy with lower computational complexity in comparison with those l1-norm-based estimators.