Semi-Passive IRS Enabled Sensing with Group Movable Sensors

TL;DR

This paper explores optimal placement of movable sensors in a semi-passive IRS-enabled NLoS sensing system to minimize estimation error, demonstrating improved performance over fixed sensors through theoretical and numerical analysis.

Contribution

It introduces a closed-form solution for optimal sensor positioning in a semi-passive IRS sensing system, enhancing DoA estimation accuracy.

Findings

Optimal sensor positions reduce CRB significantly.

Movable sensors outperform fixed-position sensors.

Theoretical analysis confirms the CRB relationship with system parameters.

Abstract

The performance of the sensing system is limited by the signal attenuation and the number of receiving components. In this letter, we investigate the sensor position selection in a semi-passive intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) sensing system. The IRS consists of passive elements and active sensors, where the sensors can receive and process the echo signal for direction-of-arrival (DoA) estimation. Motivated by the movable antenna array and fluid antenna system, we consider the case where the sensors are integrated into a group for movement and derive the corresponding Cramer-Rao bound (CRB). Then, the optimal solution for the positions of the movable sensors (MSs) to the CRB minimization problem is derived in closed form. Moreover, we characterize the relationship between the CRB and system parameters. Theoretical analysis and numerical results are provided to demonstrate the superiority of the proposed MS scheme over the fixed-position (FP) scheme.