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Reconfigurable intelligent surface (RIS)-assisted localization has attractedextensive attention as it can enable and enhance localization services inextreme scenarios. However, most existing works treat RISs as anchors withknown positions and orientations, which is not realistic in applications withmobile or uncalibrated RISs. This work considers the joint RIS calibration anduser positioning (JrCUP) problem with an active RIS. We propose a noveltwo-stage method to solve the considered JrCUP problem. The first stagecomprises a tensor-estimation of signal parameters via rotational invariancetechniques (tensorESPRIT), followed by a channel parameters refinement usingleast-squares. In the second stage, a two-dimensional search algorithm isproposed to estimate the three-dimensional user and RIS positions,one-dimensional RIS orientation, and clock bias from the estimated channelparameters. The Cramer-Rao lower bounds of the channel parameters andlocalization parameters are derived to verify the effectiveness of the proposedtensorESPRIT-based algorithms. In addition, simulation results reveal that theactive RIS can significantly improve the localization performance compared tothe passive case under the same system power supply in practical regions.Moreover, we observe the presence of blind areas with limited JrCUPlocalization performance, which can be mitigated by either leveraging moreprior information or deploying extra base stations.

JrCUP: Joint RIS Calibration and User Positioning for 6G Wireless Systems