Intelligent Reflecting Surface Enabled Sensing: Cram\'er-Rao Lower Bound Optimization

TL;DR

This paper proposes a joint beamforming design for IRS-enabled NLoS sensing to minimize the Cramér-Rao lower bound on target DoA estimation error, leading to improved sensing accuracy over traditional methods.

Contribution

It derives a closed-form CRLB expression for DoA estimation and optimizes transmit and reflective beamforming to enhance sensing performance.

Findings

CRLB-based joint beamforming reduces estimation error

Proposed method outperforms SNR maximization schemes

Numerical results validate improved sensing accuracy

Abstract

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cram\'er-Rao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming.