A Novel RSS model and Power-bias Mitigation Algorithm in fingerprinting-based Indoor Localization in Wireless Local Area Networks

With the proliferation of location based services (LBS), various indoor localization systems have been proposed based on received signal strength (RSS). An acceptable localization performance is achieved by using fingerprinting-based methods. However, its performance is restricted by RSS deviation. This deviation is not well modelled and studied in past. With this motivation, we propose a novel RSS model to define this deviation based on orthogonal frequency dimensional multiplexing (OFDM). Unlike conventional random RSS fluctuation, this deviation cannot be eliminated by merely using sample average. Hence, we also propose a power-bias mitigation algorithm to improve the performance through eliminating part of the deviation. Simulation and experimental results demonstrate good performance gains are achieved by using the power-bias mitigation algorithm under various scenarios.