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<p>This study investigates passive multi-target indoor localization in reconfigurable intelligent surface (RIS)-assisted wireless local area network (WLAN) systems. Unlike conventional methods that rely on the receiver’s spatial resolution, we propose a novel formulation that directly exploits the spatial resolution of the RIS for improved localization accuracy. To tackle the challenge of high-dimensional joint parameter estimation, we develop a low-complexity off-grid sparse representation model by applying geometric approximations and reformulating the problem into a coarse, non-uniform framework. This transformation enables efficient and accurate joint localization by fully leveraging signal sparsity. Moreover, we design a variational Bayesian algorithm for joint sparse signal recovery, which simultaneously estimates all target positions and incorporates a grid refinement strategy to enhance accuracy. Numerical results demonstrate the effectiveness and robustness of the proposed approach.</p>

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Passive Multi-Target Localization in RIS-Assisted WLAN Systems

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Si Chen,
  • Zongtao Li,
  • Guoqiang Hu,
  • Xiang Wang,
  • Liyu Liu,
  • Ruimei Wu

摘要

This study investigates passive multi-target indoor localization in reconfigurable intelligent surface (RIS)-assisted wireless local area network (WLAN) systems. Unlike conventional methods that rely on the receiver’s spatial resolution, we propose a novel formulation that directly exploits the spatial resolution of the RIS for improved localization accuracy. To tackle the challenge of high-dimensional joint parameter estimation, we develop a low-complexity off-grid sparse representation model by applying geometric approximations and reformulating the problem into a coarse, non-uniform framework. This transformation enables efficient and accurate joint localization by fully leveraging signal sparsity. Moreover, we design a variational Bayesian algorithm for joint sparse signal recovery, which simultaneously estimates all target positions and incorporates a grid refinement strategy to enhance accuracy. Numerical results demonstrate the effectiveness and robustness of the proposed approach.