Distributed Reconfigurable Intelligent Surfaces–Enabled Indoor Localization Based on Wireless Fingerprinting

Wireless fingerprint-based localization methods are widely applied in indoor scenarios due to their low complexity. However, traditional localization methods rely on signals from multiple access points (APs), and their performance degrade significantly when signals from distant APs are weak. In this letter, we propose using distributed reconfigurable intelligent surfaces (RISs) to enhance indoor localization with only one single-antenna AP, achieved by adjusting the phase-shift matrices of multiple RISs to generate distinguishable wireless fingerprints for each sub-region within the localization area. We optimize the phase-shift matrices of multiple RISs by minimizing the weighting localization error probability. The formulated minimization problem is solved through an alternating optimization (AO) algorithm and a Riemannian steepest descent (RSD) algorithm. Extensive simulations validate the effectiveness of the proposed approach.