  Reconfigurable intelligent surface (RIS) has attracted enormous interest for
its potential advantages in assisting both wireless communication and
environmental sensing. In this paper, we study a challenging multiuser tracking
problem in the multiple-input multiple-output (MIMO) orthogonal frequency
division multiplexing (OFDM) system aided by multiple RISs. In particular, we
assume that a multi-antenna base station (BS) receives the OFDM symbols from
single-antenna users reflected by multiple RISs and tracks the positions of
these users. Considering the users' mobility and the blockage of light-of-sight
(LoS) paths, we establish a probability transition model to characterize the
tracking process, where the geometric constraints between channel parameters
and multiuser positions are utilized. We further develop an online message
passing algorithm, termed the Bayesian multiuser tracking (BMT) algorithm, to
estimate the multiuser positions, the angles-of-arrivals (AoAs) at multiple
RISs, and the time delay and the blockage of the LoS path. The Bayesian Cramer
Rao bound (BCRB) is derived as the fundamental performance limit of the
considered tracking problem. Based on the BCRB, we optimize the passive
beamforming (PBF) of the multiple RISs to improve the tracking performance.
Simulation results show that the proposed PBF design significantly outperforms
the counterpart schemes, and our BMT algorithm can achieve up to
centimeter-level tracking accuracy.
