Root Sparse Bayesian Learning for Off-Grid DOA Estimation

Abstract

The performance of the existing sparse Bayesian learning (SBL) methods for off-gird DOA estimation is dependent on the trade off between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, where a coarse refinable grid, whose sampled locations are viewed as the adjustable parameters, is adopted. We utilize an expectation-maximization (EM) algorithm to iteratively refine this coarse grid, and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced and the modeling error can be almost eliminated.

4 pages, 4 figures

References in corpus (2)

• Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
• Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling

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