Off-Grid DOA Estimation for Block Orthogonal Matching Pursuit
Hao Wang, Jianfeng Li, Ming‐Yi You, Xiaofei Zhang, Qihui Wu · 2024
In the direction-of-arrival (DOA) estimation problem with multiple incident signals and multiple snapshots, an off-grid Block Orthogonal Matching Pursuit (OG-BOMP) algorithm is proposed to address the performance instability of the Orthogonal Matching Pursuit (OMP) algorithm. Under multiple snapshots conditions, the vectors of signal sources to be estimated from different snapshots exhibit joint sparsity. By converting the multiple measurement vectors (MMV) model into a block single measurement vector (SMV) model, this joint sparsity can be fully exploited. After further considering off-grid errors, more accurate off-grid parameters are obtained, leading to more precise estimation results. Simulation results demonstrate that the proposed method significantly improves estimation accuracy compared to traditional OMP and BOMP algorithms under multiple snapshots and exhibits strong multi-target resolution capability.