Multi-User Localization and Tracking with Spatiotemporal Correlation in Multi-RIS-Assisted Systems

TL;DR

This paper presents a novel multi-user localization and tracking method in multi-RIS-assisted systems that leverages spatiotemporal correlations and Bayesian inference for improved accuracy and performance.

Contribution

It introduces a general spatiotemporal Markov random field model and a Bayesian-based multi-user tracking algorithm, along with a predictive RIS beamforming optimization scheme.

Findings

Significant performance improvements over benchmark schemes.

Effective utilization of multi-user correlation for tracking accuracy.

Enhanced RIS configuration for better localization performance.

Abstract

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.