Intelligent Reflecting Surface-Assisted NLOS Sensing With OFDM Signals

TL;DR

This paper introduces a novel IRS-assisted NLOS sensing method using OFDM signals, employing a two-phase scheme and tensor decomposition to accurately estimate target parameters despite limited resources.

Contribution

It proposes a two-phase sensing scheme with tensor-based parameter estimation to resolve scaling ambiguity in IRS-assisted NLOS sensing scenarios.

Findings

Achieves reliable NLOS sensing with modest pulse/subcarrier resources.

Effectively estimates DOA, Doppler shift, and time delay using tensor decomposition.

Demonstrates robustness even when AP-IRS channel degrees-of-freedom are limited.

Abstract

This work addresses the problem of intelligent reflecting surface (IRS) assisted target sensing in a non-line-of-sight (NLOS) scenario, where an IRS is employed to facilitate the radar/access point (AP) to sense the targets when the line-of-sight (LOS) path between the AP and the target is blocked by obstacles. To sense the targets, the AP transmits a train of uniformly-spaced orthogonal frequency division multiplexing (OFDM) pulses, and then perceives the targets based on the echoes from the AP-IRS-targets-IRS-AP channel. To resolve an inherent scaling ambiguity associated with IRS-assisted NLOS sensing, we propose a two-phase sensing scheme by exploiting the diversity in the illumination pattern of the IRS across two different phases. Specifically, the received echo signals from the two phases are formulated as third-order tensors. Then a canonical polyadic (CP) decomposition-based method is developed to estimate each target's parameters including the direction of arrival (DOA), Doppler shift and time delay. Our analysis reveals that the proposed method achieves reliable NLOS sensing using a modest quantity of pulse/subcarrier resources. Simulation results are provided to show the effectiveness of the proposed method under the challenging scenario where the degrees-of-freedom provided by the AP-IRS channel are not enough for resolving the scaling ambiguity.