Low complexity DOA estimation using AMP with unitary transformation and iterative refinement
© 2020 Elsevier Inc. This work deals with the problem of fast direction-of-arrival (DOA) estimation. A low complexity iterative off-grid method is proposed, which employs the approximate message passing with unitary transformation based sparse Bayesian learning (SBL) to obtain initial estimates of the signals and their corresponding DOAs, and then refines the estimates iteratively using the Jacobi or Gauss-Seidel iteration with low complexity. Both general array and uniform linear array (ULA) are considered. Simulation results demonstrate that, with much lower complexity, the proposed method outperforms state-of-the-art methods, and its performance can approach the Cramer-Rao bound closely.