Atomic Norm Minimization-based DoA Estimation for IRS-assisted Sensing Systems

TL;DR

This paper introduces an atomic norm minimization approach for multi-target DoA estimation in IRS-assisted sensing systems, leveraging information from both IRS reflecting and sensing elements to improve accuracy and resolution.

Contribution

It fully exploits DoA information from IRS REs and SEs using ANM, and derives the Cramér-Rao bound for enhanced understanding of estimation limits.

Findings

ANM-based method outperforms baseline algorithms in accuracy

Derived CRB shows inverse relation to system parameters

Numerical results confirm high resolution capability

Abstract

Intelligent reflecting surface (IRS) is expected to play a pivotal role in future wireless sensing networks owing to its potential for high-resolution and high-accuracy sensing. In this work, we investigate a multi-target direction-of-arrival (DoA) estimation problem in a semi-passive IRS-assisted sensing system, where IRS reflecting elements (REs) reflect signals from the base station to targets, and IRS sensing elements (SEs) estimate DoA based on echo signals reflected by the targets. {First of all, instead of solely relying on IRS SEs for DoA estimation as done in the existing literature, this work fully exploits the DoA information embedded in both IRS REs and SEs matrices via the atomic norm minimization (ANM) scheme. Subsequently, the Cram\'er-Rao bound for DoA estimation is derived, revealing an inverse proportionality to $MN^3+NM^3$ under the case of identity covariance matrix of the IRS measurement matrix and a single target, where $M$ and $N$ are the number of IRS SEs and REs, respectively. Finally, extensive numerical results substantiate the superior accuracy and resolution performance of the proposed ANM-based DoA estimation method over representative baselines.