  As a promising technique, reconfigurable intelligent surfaces (RISs) exhibit
its tremendous potential for high accuracy positioning. In this paper, we
investigates multi-user localization and tracking problem in
multi-RISs-assisted system. In particular, we incorporate statistical
spatiotemporal correlation of multi-user locations and develop a general
spatiotemporal Markov random field model (ST-+MRF) to capture multi-user
dynamic motion states. To achieve superior performance, a novel multi-user
tracking algorithm is proposed based on Bayesian inference to effectively
utilize the correlation among users. Besides that, considering the necessity of
RISs configuration for tracking performance, we further propose a predictive
RISs beamforming optimization scheme via semidefinite relaxation (SDR).
Compared to other pioneering work, finally, we confirm that the proposed
strategy by alternating tracking algorithm and RISs optimization, can achieve
significant performance gains over benchmark schemes.
