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

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.

References in corpus (6)

• Reconfigurable Intelligent Surfaces: A Signal Processing Perspective With Wireless Applications
• Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces
• STARS Enabled Integrated Sensing and Communications
• Reconfigurable Intelligent Surfaces for N-LOS Radar Surveillance
• The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms
• One-Bit Phase Retrieval: More Samples Means Less Complexity?

Cited by in corpus (3)

• STARS Enabled Integrated Sensing and Communications
• Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information
• Quantized Phase-Shift Design of Active IRS for Integrated Sensing and Communications