Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar

TL;DR

This paper develops a multi-IRS radar system to improve the estimation of hidden moving targets, deriving bounds and optimizing phase shifts to enhance accuracy in NLoS scenarios.

Contribution

It introduces a multi-IRS framework with Doppler-aware phase shift optimization, extending beyond single-IRS approaches for improved target estimation.

Findings

Multiple IRS platforms improve estimation accuracy.

Optimized phase shifts outperform non-IRS and single-IRS methods.

Numerical results confirm the effectiveness of the proposed approach.

Abstract

Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on IRS-aided radar estimation performance of a moving hidden or NLoS target. Unlike prior works that employ a single IRS, we investigate this problem using multiple IRS platforms and assess the estimation performance by deriving the associated Cramer-Rao lower bound (CRLB). We then design Doppler-aware IRS phase shifts by minimizing the scalar A-optimality measure of the joint parameter CRLB matrix. The resulting optimization problem is non-convex, and is thus tackled via an alternating optimization framework. Numerical results demonstrate that the deployment of multiple IRS platforms with our proposed optimized phase shifts leads to a higher estimation accuracy compared to non-IRS and single-IRS alternatives.