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

TL;DR

This work addresses locating multiple targets in an IRS-assisted bi-static 6G ISAC setting where LOS between targets and the BS is blocked. It proposes a three-phase approach that first estimates channel impulses, then uses a MUSIC-based framework on a virtual, time-augmented signal to recover AOAs and (for near-field) distances to the IRS, and finally localizes targets from their AOAs and ranges. Theoretical results establish perfect relative-state recovery in the ideal infinite-coherence limit, and numerical results corroborate the method’s effectiveness, highlighting the IRS’s potential as a passive anchor to extend BS sensing. The proposed scheme enables meter-level localization and demonstrates significant reductions in search complexity when prior information is exploited, underscoring the practical impact for 6G ISAC deployments.

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.

Figures (10)

• Figure 1: System model for IRS-assisted bi-static localization in a 6G ISAC network, where LOS paths between the targets and the BS are blocked, and the BS's received signals are merely over the user-target-IRS-BS path. Therein, targets are in both of the near-field and far-field regions of the IRS. We utilize the IRS as a passive anchor for localization.
• Figure 2: A toy example for coexistence of near-field and far-field targets, where target $1$ and target $2$ coexist within the range cluster $l$. They are in the different regions for the IRS. Specifically, target $1$ is in the near-field region of the IRS, while target $2$ is in the far-field region of the IRS.
• Figure 3: Illustration of original signal model and newly created signal model. Specifically, we combine all the estimated channels of the first $Q_0$ OFDM symbols within one coherence block together to create a new virtual high-dimension signal, which contains information in both spatial (BS antennas) and temporal (OFDM symbols) domain.
• Figure 4: Comparison of the average number of search grids for the near-field and far-field targets between the proposed method and the conventional method.
• Figure 5: The MUSIC spectrums in Phase II for far-field and near-field targets with $M_{\rm B} = 4$ and $Q_0=4$.

Theorems & Definitions (3)

• Theorem 1
• Corollary 1
• Remark 1