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Sparse Recovery Assisted Doa Estimation Utilizing Sparse Bayesian Learning

This paper proposes a novel approach to sparse recovery assisted direction-of-arrival (SR-DOA) estimation. By exploiting the sparsity inherent in the spatial spectrum, the DOA estimation is formulated as a sparse nonnegative least squares problem. Meanwhile, in order to enhance the estimation accuracy, the devised method is able to suppress the additive Gaussian noise but at the expense of a few degrees-of- freedom, and mitigate the sampling errors by exploiting its asymptotic distribution. Subsequently, the sparse Bayesian learning with nonnegative Laplace prior is utilized to yield the DOA estimation. The performances of the proposed SR-DOA estimator along with other two existing approaches are investigated and compared. Numerical results show that the proposed SR-DOA algorithm is superior to the state-of-the-art methods in terms of the estimation accuracy.