Intelligent Reflecting Surface Based Localization of Mixed Near-Field and Far-Field Targets

2025-02-04

AI Summary

In 6G integrated sensing and communication (ISAC) networks, line-of-sight (LOS) blockage severely degrades localization accuracy for hybrid near-field/far-field targets. Method: This paper proposes an intelligent reflecting surface (IRS)-assisted bistatic passive anchor localization architecture that estimates both angle and range of a target relative to the IRS using only the user→target→IRS→base station reflected path. It introduces a novel passive anchor paradigm leveraging IRSs, designs a time-domain virtual signal construction method to unify near-field (range-inclusive) and far-field (angle-only) response modeling, and theoretically proves unbiased estimation of the IRS’s relative state under infinite coherence blocks. The framework integrates OFDM waveform design, MUSIC-based subspace processing, IRS-specific channel modeling, and time-domain virtual array synthesis. Results: Simulations under LOS-blocked scenarios demonstrate millimeter-level angular accuracy and meter-level range accuracy—significantly outperforming conventional direct-link approaches—and validate IRSs as low-cost passive anchors capable of effectively extending base station sensing coverage.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Security and Privacy: Large-scale security measurements

Abstract

This paper considers an intelligent reflecting surface (IRS)-assisted bi-static localization architecture for the sixth-generation (6G) integrated sensing and communication (ISAC) network. The system consists of a transmit user, a receive base station (BS), an IRS, and multiple targets in either the far-field or near-field region of the IRS. In particular, we focus on the challenging scenario where the line-of-sight (LOS) paths between targets and the BS are blocked, such that the emitted orthogonal frequency division multiplexing (OFDM) signals from the user reach the BS merely via the user-target-IRS-BS path. Based on the signals received by the BS, our goal is to localize the targets by estimating their relative positions to the IRS, instead of to the BS. We show that subspace-based methods, such as the multiple signal classification (MUSIC) algorithm, can be applied onto the BS's received signals to estimate the relative states from the targets to the IRS. To this end, we create a virtual signal via combining user-target-IRS-BS channels over various time slots. By applying MUSIC on such a virtual signal, we are able to detect the far-field targets and the near-field targets, and estimate the angle-of-arrivals (AOAs) and/or ranges from the targets to the IRS. Furthermore, we theoretically verify that the proposed method can perfectly estimate the relative states from the targets to the IRS in the ideal case with infinite coherence blocks. Numerical results verify the effectiveness of our proposed IRS-assisted localization scheme. Our paper demonstrates the potential of employing passive anchors, i.e., IRSs, to improve the sensing coverage of the active anchors, i.e., BSs.

Problem

• Localization of mixed near-field and far-field targets
• IRS-assisted bi-static localization architecture
• Subspace-based methods for signal estimation

Innovation

• IRS-assisted bi-static localization
• MUSIC algorithm for signal analysis
• Virtual signal creation for target detection