An Efficient Sparse Representation Algorithm for Direction-of-Arrival Estimation

This paper presents an efficient sparse representation approach to direction-of-arrival (DOA) estimation using uniform linear arrays. The proposed approach constructs the jointly sparse model in real domain by exploiting the properties of centro-Hermitian matrices. Subsequently, DOA estimation is realized via the sparse Bayesian learning (SBL) algorithm. Further, the pruning threshold of SBL is adaptively selected to speed up the basis pruning rate. Simulation results demonstrate that the proposed approach achieves an improved performance and enjoys computational efficiency as compared to the state-of-the-art l(1)-norm-based DOA estimators especially in scenarios with small sample size and low signal-to-noise ratio.