Fingerprinting Database Development Methods for Reconfigurable Intelligent Surface Assisted Indoor Positioning System

The positioning accuracy of a reconfigurable intelligent surface (RIS) assisted indoor positioning system (IPS) can be improved by developing a fingerprinting database for different RIS configurations. Every RIS configuration generates a different radio map of the indoor environment such that the variations in the received power are used to localize unknown receivers. However, creating a diverse fingerprinting database is challenging and time consuming. To this end, we compare three end-to-end (E2E) propagation modeling techniques that include a full-wave electromagnetic (EM) simulator-based model (FWS-E2E), and two hybrid models called HYB1-E2E and HYB2-E2E. The FWS-E2E technique models the RIS and the entire indoor environment in a full-wave EM simulator. On the other hand, the hybrid techniques mostly rely on analytical equations while using some important data from the EM simulator. In this paper, we also discuss methods and algorithms to identify useful RIS configurations having the potential to generate diverse radio maps for increasing the positioning accuracy of the IPS. Both hybrid methods significantly reduce the complexity of generating the radio map. Experimental results are also provided to compare the performance of the E2E models.