Toward Wireless Localization Using Multiple Reconfigurable Intelligent Surfaces

TL;DR

This paper explores the use of multiple reconfigurable intelligent surfaces (RISs) for wireless localization, demonstrating theoretical models, performance analysis, and a practical prototype for direction of arrival estimation and source localization.

Contribution

It introduces a RIS-based backward sensing framework with models, analysis, and a prototype, advancing wireless localization capabilities using multiple RISs.

Findings

RIS can estimate DoA with a single surface using configurational diversity.

Multiple RISs enable accurate localization of multiple sources.

Numerical experiments validate the theoretical models and prototype performance.

Abstract

This paper investigates the capabilities and effectiveness of backward sensing centered on reconfigurable intelligent surfaces (RISs). We demonstrate that the direction of arrival (DoA) estimation of incident waves in the far-field regime can be accomplished using a single RIS by leveraging configurational diversity. Furthermore, we identify that the spatial diversity achieved through deploying multiple RISs enables accurate localization of multiple power sources. Physically accurate and mathematically concise models are introduced to characterize forward signal aggregations via RISs. By employing linearized approximations inherent in the far-field region, the measurement process for various configurations can be expressed as a system of linear equations. The mathematical essence of backward sensing lies in solving this system. A theoretical framework for determining key performance indicators is established through condition number analysis of the sensing operators. In the context of localization using multiple RISs, we examine relationships among the rank of sensing operators, the size of the region of interest (RoI), and the number of elements and measurements. For DoA estimations, we provide an upper bound for the relative error of the least squares reconstruction algorithm. These quantitative analyses offer essential insights for system design and optimization. Numerical experiments validate our findings. To demonstrate the practicality of our proposed RIS-centric sensing approach, we develop a proof-of-concept prototype using universal software radio peripherals (USRP) and employ a magnitude-only reconstruction algorithm tailored for this system. To our knowledge, this represents the first trial of its kind.