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

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 moving hidden or NLoS target. Unlike prior works that employ single IRS, investigate problem using multiple IRS platforms and assess the by deriving associated Cramér-Rao lower bound (CRLB). We then design Doppler-aware phase shifts minimizing scalar A-optimality measure joint parameter CRLB matrix. The resulting optimization non-convex, thus tackled via an alternating framework. Numerical results demonstrate deployment with our proposed optimized leads higher accuracy compared non-IRS single-IRS alternatives.