IRS-Aided Secure Sensing for Surveillance Area Coverage: Framework and Algorithm Design

This paper proposes a novel IRS-aided framework for secure sensing, which aims to minimize the worst-case Cram´er-Rao Bound (WC-CRB) within an entire surveillance area by optimizing the IRS reflecting beamforming, enabling reliable and secure localization of arbitrary and unknown targets. Specifically, we first establish a general IRS-aided localization coverage model and derive the closed-form expression for the CRB of an arbitrary point, which reveals the relationship between the localization