Source Localization with Intelligent Surfaces

Ziyi Wang, Zhenyu Liu, Yuan Shen, Andrea Conti, Moe Z. Win

Abstract: Source localization is essential for a wide range of applications. The efficiency of source localization in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). In this paper, we investigate the information inequality of RIS-aided localization systems. We propose a general signal model for RIS-aided localization valid for both near-field and far-field scenarios. Based on the proposed model, we perform Fisher information analyses of localization performance in networks with RISs. Numerical results show that optimal-configured RISs can improve the localization accuracy significantly.