Semi-Passive IRS-Aided DOA Estimation: Weighted ESPRIT Approach

Intelligent reflecting surface (IRS) has emerged as a promising technology for high-accuracy direction-of-arrival (DOA) estimation. Nevertheless, existing subspace-based DOA estimation methods, such as multiple signal classification (MUSIC), fail to utilize the additional spatial information embedded in the passive reflecting elements, limiting their estimation performance. To this end, we devise a DOA estimator based on weighted estimation of signal parameters via rotational invariance techniques (WESPRIT), which fully exploits the spatial information embedded in both active and passive sensing elements. Unlike MUSIC and atomic norm minimization, which require grid search or convex optimization, our approach provides a closed-form solution, significantly increasing computational efficiency. Simulation results demonstrate that the proposed approach achieves a good compromise between accuracy and computational complexity.