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

Ronghua Peng, Peng Gao, Jing You, Lixiang Lian

TL;DR

This work tackles direct multi-user localization and tracking in multi-RIS assisted mmWave systems by introducing a spatiotemporal Markov random field (ST-MRF) to capture inter-user and temporal correlations. A Bayesian multi-user direct localization and tracking (MUDLT) algorithm with a three-module message-passing framework is proposed, integrating AoA estimation, temporal propagation, and spatial bidirectional updates. To enhance tracking and future positioning, a predictive RIS beamforming design based on the Bayesian Cramer-Rao bound (BCRB) is formulated and solved via semidefinite relaxation (SDR). Simulation results demonstrate that alternating MUDLT and SDR-based RIS optimization yields significant performance gains over benchmark schemes, enabling more accurate localization and efficient RIS configuration in dynamic multi-user scenarios.

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.

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

TL;DR

This work tackles direct multi-user localization and tracking in multi-RIS assisted mmWave systems by introducing a spatiotemporal Markov random field (ST-MRF) to capture inter-user and temporal correlations. A Bayesian multi-user direct localization and tracking (MUDLT) algorithm with a three-module message-passing framework is proposed, integrating AoA estimation, temporal propagation, and spatial bidirectional updates. To enhance tracking and future positioning, a predictive RIS beamforming design based on the Bayesian Cramer-Rao bound (BCRB) is formulated and solved via semidefinite relaxation (SDR). Simulation results demonstrate that alternating MUDLT and SDR-based RIS optimization yields significant performance gains over benchmark schemes, enabling more accurate localization and efficient RIS configuration in dynamic multi-user scenarios.

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.
Paper Structure (15 sections, 32 equations, 5 figures, 1 algorithm)

This paper contains 15 sections, 32 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal Model
  4. Statistical Spatiotemporal Correlation Model
  5. Pedestrian Surveillance
  6. Communities with Few Distant Users
  7. Bayesian Multi-user Direct Localization and Tracking
  8. MPDL Algorithm Framework
  9. Message passing in spatial domain for Module 1 and 3
  10. Message passing in temporal domain Module 2
  11. BCRB Optimization for Predictive PBF Design
  12. NUMERICAL EXPERIMENTS
  13. Tracking Performance for Different Parameters
  14. Tracking Performance for Practical Trajectory
  15. CONCLUSION

Figures (5)

  • Figure 1: System model of multi-RIS-assisted multi-user localization and tracking.
  • Figure 2: The message passing model of MRF
  • Figure 3: Averaged RMSE versus SNR for different schemes.
  • Figure 4: RMSE versus time slots for different schemes.
  • Figure 5: The practical trajectories and tracking performance of three users