Direction of Arrival Estimation Using Sparse Signal Recovery Techniques

Direction-of-arrival (DOA) estimation finds numerous applications in various areas such as acoustics, radar and wireless communications. Recently, the research on DOA estimation has been advanced thanks to the rapid development of compressive sensing (CS) techniques. By exploiting the sparsity of signal sources in the spatial domain, CS-based DOA estimation has emerged as a promising approach especially in the case of a limited number of snapshots. However, due to the use of a large overcomplete dictionary obtained from a predefined grid, CS-based DOA estimation methods normally su er from high computational complexity due to involved matrix inversion and the grid mismatch problem. To address the grid mismatch problem, some methods, in particular sparse Bayesian learning (SBL) based ones, have been developed in the literature, which however result in higher complexity, hindering their applications. The objective of this thesis is to develop more e cient DOA methods using sparse signal recovery techniques.