Adaptive Path Loss Model for BLE Indoor Positioning System

Abstract

Indoor positioning systems (IPSs) allow the location and tracking of mobile devices in indoor environments where the Global Positioning System (GPS) does not provide satisfactory results. In model-based IPSs, it is common to use signal propagation models to estimate distances between anchor nodes and mobile devices using the Received Signal Strength Indicator (RSSI). However, using fixed parameters in the path loss model to characterize the signal in large-scale scenarios results in the degradation of the positioning accuracy. In this paper, we propose the ADAM (Adaptive Model) Positioning System, a model-based IPS that chooses the best anchor nodes to benefit the positioning computation and uses different parameters for the log-distance model to represent the signal in different regions and conditions of the scenario. Then, we estimate a single, more precise position using a data fusion technique. Our proposal does not require training nor prior knowledge of the best parameters for each region. We evaluated the performance of our proposed system in a real-world, large-scale environment using Bluetooth-based mobile devices. Our results clearly show that ADAM can locate mobile devices with an average error of 2.93 m in relation to the real position, which is 23% better than literature-based models using fixed parameters for the entire environment.