An efficient sparsity-inducing method for high-resolution direction-of-arrival estimation

An efficient sparsity-inducing method for direction-of-arrival (DOA) estimation is proposed to solve the challenging problem of computation cost and resolution. Element-space is firstly mapped to beamspace by using the beamforming matrix, and then the array covariance matrix is used for sparse representation. In doing so, the sparse Bayesian learning (SBL) technique is applied to enforce sparsity at the true source locations and the coarse sources locations are obtained. Finally, the refined method is used to get the high-resolution DOA estimation based on the coarse estimation. The proposed method not only reduces the computation load, but also improve the precision of DOA estimation. Numerical simulation results validate the effectiveness of the proposed method.