Improving Indoor Positioning Accuracy Using RIS-based RSS Optimization

In the Received Signal Strength (RSS) based Indoor Positioning Systems (IPS), the position of a receiver is estimated by comparing its RSS values with a fingerprint. The fingerprint is a dataset including the measured RSS values at pre-planned Reference Points (RP) for a set of reference transmitters. For a given RPs' spatial distribution, the RSS values are however affected by the intrinsic temporal and spatial uncertainties in the indoor wireless channel, hence constraining the positioning accuracy. To address this issue, we propose an algorithm to predesign the RSS values at each RP using Reconfigurable Intelligent Surface (RIS) technology. In the proposed method, the RIS reflection coefficients are obtained to maximize the difference between the RSS values between the RPs. The simulation results confirm that even with a relatively small number of RIS elements, the proposed method significantly improves the IPS efficiency.