Sparse RIS-Aided DOA Estimation for NLOS Scenario: A Pseudo-Inverse Vectorization Perspective

作者：Jun Zhao, Xudong Dong, Jibing Qiu, Jie Luo, Meng Sun, Yide Wang, Xiaofei Zhang · 发表于：IEEE Transactions on Vehicular Technology · 年份：2025 · DOI：10.1109/tvt.2025.3632831 · 被引用次数：2 · 研究领域：Advanced Wireless Communication Technologies、Direction-of-Arrival Estimation Techniques、Radar Systems and Signal Processing

In this paper, a sparse reconfigurable intelligent surface (RIS)-based direction-of-arrival (DOA) estimation algorithm is proposed to address the non-line-of-sight (NLOS) links of passive sensing systems. To address the under-utilization of existing RIS elements, a novel method for the DOA estimation based on pseudo inverse vectorization (PIV) is proposed. Through establishing a mathematical characterization model of non-uniform array configuration, the spatial sampling capability of sparse RIS arrays and the efficiency of hardware resource usage are significantly improved under the premise of guaranteeing the accuracy of DOA estimation. Moreover, the Cram´ er-Rao bound (CRB) of the DOA estimation of the signal-RIS-receiver link is derived. Numerical results show that the estimation performance of the proposed algorithm significantly outperforms existing algorithms and conserves the RIS elements compared to the RIS with the uniform linear array configuration.