An Improved Direction-of-Arrival Estimation Method Using Atomic Norm Minimization Algorithm Based on Support Set of Sparse Vectors
Qinlong Li, Zheng Wan, Kaizhi Huang, Liang Jin, Ming Yi · 2023
The compressed sensing-based direction of arrival (DOA) estimation method with high precision and fewer snapshots has garnered attention. However, existing studies decrease in accuracy significantly as the number of sources increases. To address this issue, this paper proposes an improved DOA estimation method based on a support set of the sparse vector with an Atomic Norm Minimization (ANM) algorithm. The method constructs a support set of the sparse vector to enhance the sparse support degree. Then, the ANM algorithm is performed on the support set of the sparse vector to obtain DOA estimation results. Simulation results show that the proposed method improves the performance of DOA estimation compared to the classic ANM algorithm, particularly when the sparsity approaches or exceeds the number of array elements.