Reconfigurable Intelligent Surface Aided Off-Grid DOA Estimation Method Using Iterative Angle Offset Correction

Direction-of-arrival (DOA) estimation performance degrades when the line-of-sight (LoS) link is not available. The reconfigurable intelligent surface (RIS) can establish a virtual LoS link between the base station and the targets. Existing RIS-based DOA estimation algorithms suffer from the offgrid problem, that shows up when the true DOAs are not located exactly on the discretized sampling grid points. In order to overcome the drawback, we propose an effective offgrid DOA estimation method using RIS in this work. The proposed algorithm first estimates the coarse DOAs by combining the atomic-norm based method with the Hankel-based MUSIC method. Subsequently, the closed-form estimator of the angle offset are derived by solving an optimization problem. Finally, the DOA estimates of multiple sources are corrected iteratively based on the angle offset estimator. Simulation results demonstrate the effectiveness and efficiency of the proposed method for off-grid DOA estimation using RIS.