Bayesian User Tracking for Reconfigurable Intelligent Surface Aided mmWave MIMO System

In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided mmWave multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate the user position and the angles-of-arrivals (AoAs) at the user in an online fashion. Simulation results show that our BULT algorithm performs close to the Bayesian Cramer Rao bound (BCRB), and outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.