Near-Field RSS-Based Localization Algorithms Using Reconfigurable Intelligent Surface

Abstract

Near-field (NF) localization is a key research topic in various applications, including autonomous vehicles, real-time tracking, target monitoring, etc. However, the traditional NF localization methods mostly rely on the line-of-sight (LOS) condition, and the performance may suffer degradation when there are lacking LOS paths. In this paper, we introduce a promising technology named reconfigurable intelligent surface (RIS) to resolve the problem and investigate the NF RSS-based localization algorithms. To be specific, we apply RIS to construct virtual line-of-sight (VLOS) paths between the anchor node (AN) and the unknown node (UN) for the sake of addressing the LOS absence problem, and propose the RIS phase adjustment schemes by maximizing the received signal strength (RSS) of the UN. On this basis, we derive the relationship between the azimuth and the phase parameters, and the accurate estimation of the UN’s position is derived via weighted least square (WLS) and alternate iteration methods. Next, we further resolve the coexisting problem in terms of LOS and VLOS paths by adjusting the reflection factors. Lastly, we put forward a method for discriminating whether the UN lies in the far-field (FF) or NF of the RIS subparts to minimize the localization error. Several simulations demonstrate the effectiveness of our proposed algorithms.