Intelligent Reflecting Surface Assisted Localization: Performance Analysis and Algorithm Design

TL;DR

This paper introduces a semi-passive IRS system for localization that overcomes line-of-sight limitations, providing sub-meter accuracy through joint ToA/DoA estimation and theoretical CRB analysis.

Contribution

It proposes a novel semi-passive IRS architecture with active sensors for improved localization, along with theoretical CRB analysis and a simple estimator.

Findings

Achieves sub-meter localization accuracy over long distances.

Provides significant accuracy improvement over fully passive IRS.

Theoretical analysis of CRB relationships with system parameters.

Abstract

The target sensing/localization performance is fundamentally limited by the line-of-sight link and severe signal attenuation over long distances. This paper considers a challenging scenario where the direct link between the base station (BS) and the target is blocked due to the surrounding blockages and leverages the intelligent reflecting surface (IRS) with some active sensors, termed as \textit{semi-passive IRS}, for localization. To be specific, the active sensors receive echo signals reflected by the target and apply signal processing techniques to estimate the target location. We consider the joint time-of-arrival (ToA) and direction-of-arrival (DoA) estimation for localization and derive the corresponding Cram\'{e}r-Rao bound (CRB), and then a simple ToA/DoA estimator without iteration is proposed. In particular, the relationships of the CRB for ToA/DoA with the number of frames for IRS beam adjustments, number of IRS reflecting elements, and number of sensors are theoretically analyzed and demystified. Simulation results show that the proposed semi-passive IRS architecture provides sub-meter level positioning accuracy even over a long localization range from the BS to the target and also demonstrate a significant localization accuracy improvement compared to the fully passive IRS architecture.