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

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.

Discovering Optimal Robust Minimum Redundancy Arrays (RMRAs) through Exhaustive Search and Algebraic Formulation of a New Sub-Optimal RMRA

Modern sparse arrays are maximally economic in that they retain just as many sensors required to provide a specific aperture while maintaining a hole-free difference coarray. As a result, these are susceptible to the failure of even a single sensor. Contrarily, two-fold redundant sparse arrays (TFRSAs) and robust minimum redundancy arrays (RMRAs) ensure robustness against single-sensor failures due to their inherent redundancy in their coarrays. At present, optimal RMRA configurations are known only for arrays with sensor counts N=6 to N=10. To this end, this paper proposes two objectives: (i) developing a systematic algorithm to discover optimal RMRAs for N>10, and (ii) obtaining a new family of near-/sub-optimal RMRA that can be completely specified using closed-form expressions (CFEs). We solve the combinatorial optimization problem of finding RMRAs using an exhaustive search technique implemented in MATLAB. Optimal RMRAs for N = 11 to 14 were successfully found and near/sub-optimal arrays for N = 15 to 20 were determined using the proposed technique. As a byproduct of the exhaustive search, a large catalogue of valid near- and sub-optimal RMRAs was also obtained. In the second stage, CFEs for a new TFRSA were obtained by applying pattern mining and algebraic generalizations to the arrays obtained through exhaustive search. The proposed family enjoys CFEs for sensor positions, available aperture, and achievable degrees of freedom (DOFs). The CFEs have been thoroughly validated using MATLAB and are found to be valid for $N\geq8$. Hence, it can be concluded that the novelty of this work is two-fold: extending the catalogue of known optimal RMRAs and formulating a sub-optimal RMRA that abides by CFEs.