  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.
