Off-Grid Underdetermined DOA Estimation of Quasi-stationary Signals via Sparse Bayesian Learning

In the context of sparse reconstruction framework, direction of arrival (DOA) estimation of quasi-stationary signals (QSS) usually has difficulties in practical situations where true DOAs are not on the discretized sampling grid. In order to solve such an off-grid DOA estimation problem, this paper proposes novel DOA estimation strategy based on off-grid sparse Bayesian learning method. By using the KhatriRao transform, the virtual array aperture of uniform circular array is extended, thus the proposed method have the ability to achieve underdetermined DOA estimation. Then, an expectation-maximization iteration method is developed to estimate DOAs of QSS based on the off-grid model from a Bayesian perspective. Compared with state-of-the-art techniques, the proposed method do not need estimate parameters in performing the algorithms and has better estimation precision. Numerical simulations demonstrate the validity of the proposed method.