Cramer-Rao Bounds for Target Parameter Estimation in a Bi-Static IRS-Assisted Radar Configuration

Abstract: Non-Line-of-Sight (NLoS) sensing and detection of low-observable (stealth) targets are challenging for conventional radar due to blockage and severe propagation loss. Intelligent Reflective Surface (IRS)-assisted radar can extend the field-of-view (FOV), but common architectures rely on the four-hop radar--IRS--target--IRS--radar link, whose attenuation limits estimation performance. This paper proposes an alternative architecture, that exploits the target-scattered component received at a spatially separated IRS and redirected back to a mono-static radar receiver. The geometry provides bi-static/multi-static-like diversity using a passive panel, while retaining a mono-static front-end and avoiding inter-node time synchronization concerns. We develop a signal model for the proposed configuration and recast it into a compact, parameterized form that is suitable for angle estimation. Using this reformulation, we derive the Fisher Information Matrix and the associated Cramér--Rao Lower Bounds (CRLB) for target azimuth and elevation angles with respect to the IRS. Numerical evaluations quantify the impact of various signal-model parameters on the achievable bounds. These results provide insights on the parameter-estimation limits within the FOV against SNR, snapshots and IRS elements.