Underdetermined wideband DOA estimation for off‐grid targets: a computationally efficient sparse Bayesian learning approach
Ying Jiang, Ming‐Hao He, Wei‐Jian Liu, Jun Han, Ming‐Yue Feng · IET Radar Sonar & Navigation · 2020
Underdetermined wideband direction of arrival (DOA) estimation based on the sparse array is studied here and a novel algorithm is developed to improve the estimation performance of off‐grid targets in the framework of sparse Bayesian learning. First, the narrowband off‐grid model is extended to a wideband case and the sparse Bayesian model containing off‐grid biases is deduced. Then, a sequential solution is proposed to obtain the estimation, where the fast sparse Bayesian learning strategy is employed to improve the computational efficiency. The estimation accuracy is improved significantly through off‐grid compensation and the computational complexity is reduced remarkably. Simulation results verify the effectiveness of the proposed method.