Beam Calibration for RIS-Aided Positioning: Modeling and Performance Analysis

High-accuracy positioning plays an essential role in various applications such as autonomous driving, augmented reality. Antenna arrays and reconfigurable intelligent surface (RIS) are incorporated into positioning systems to achieve high angular resolution, assisting in the localization process. However, array and RIS beam patterns in practice often deviate from the idealized models used for algorithm design, leading to significant degradation in positioning accuracy. This mismatch highlights the need for beam calibration to bridge the gap between theoretical models and real-world hardware behavior. In this paper, we present three beam models considering non-idealities such as mutual coupling, non-ideal codebook, and measurement uncertainties. Based on the models, we then develop calibration algorithms to estimate the model parameters that can be used for future positioning tasks. This work evaluates the effectiveness of the beam models and the calibration algorithms using both theoretical bounds and measured beam patterns of an RIS prototype. The simulation results show that the model incorporating combined impacts can accurately reconstruct measured beam patterns and reduce the positioning error to sub-meter level. This highlights the necessity of beam calibration to achieve high-accuracy positioning.