Reconfigurable Intelligent Surface-Aided Near-Field Localization With Array Spatial Correlation

Reconfigurable intelligent surface (RIS) is a promising technology for reconfiguring the wireless environment by controlling the amplitudes and phases of its reflected signals, which provides a new solution to enhance the performance of indoor localization systems. In this work, we focus on a single-anchor localization system assisted by a large-scale RIS to serve a blocked user and propose a near-field (NF) localization method based on an approximation for the NF channel model. Specifically, we first design a hierarchical NF codebook with modified parameters and devise its associated beam training scheme to reduce the training overhead significantly. Then, we utilize the training results to estimate the covariance matrix regarding the array spatial correlation information, which is able to decouple the three-dimensional (3D) position parameters in the NF array response vector. Finally, the least squares (LS) method is used to estimate the parameters separately and determine the user’s location. Simulation results numerically demonstrate the high estimation accuracy of the proposed localization method.