High-resolution direction-of-arrival estimation in beamspace

A novel method for direction-of-arrival (DOA) estimation in beamspace is proposed to solve the challenging problem of computational complexity reduction under the sparsity of solution. The essence is to map the data from element-space to beamspace by a 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 signal locations. Numerical results show that, besides efficient computational complexity reduction, the proposed method also significantly improves the DOA estimation performance.