Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface

TL;DR

This paper introduces a novel passive indoor localization method using Intelligent Reflecting Surfaces (IRS) to enhance WiFi spatial resolution, enabling sub-centimeter accuracy for multiple moving persons without infrastructure modifications.

Contribution

It proposes a new IRS-based framework for passive multi-person WiFi localization, including optimal phase control and a side-lobe cancellation algorithm for multi-person scenarios.

Findings

Achieves sub-centimeter localization accuracy in simulations.

Effectively localizes multiple moving persons passively.

Operates without changes to existing WiFi infrastructure.

Abstract

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.