A Passive EKF-Based RIS-Aided Cellular Navigation System

This paper presents a novel reconfigurable intelligent surface (RIS)-based localization approach for mobile user equipment (UE) in a millimeter-wave uplink cellular environment. The proposed approach develops a measurement engine that employs a state-of-the-art carrier-aided code-phasebased navigation receiver and incorporates a passive correlationbased angle-locked loop (ALL) for TOA and AOA estimation. An extended Kalman filter (EKF)-based RIS-aided navigation framework is deployed, providing accurate 3D position and velocity estimates for the mobile UEs utilizing the RIS-based navigation observables, which are then leveraged to optimize the RIS phase profile to maximize the signal-to-noise ratio (SNR) for the various UEs. Finally, the paper demonstrates the accuracy of the navigation solution through extensive Monte Carlo simulations that encompass different scenarios involving pedestrians, ground vehicles, and unmanned aerial vehicles (UAVs). These simulations emphasize the utility of our proposed approach in delivering sub-meter and meter-level positioning accuracies.