Developing a novel real-time indoor positioning system based on BLE beacons and smartphone sensors

In this work, we study the problem of fusing one Pedestrian-Dead-Reckoning-based (PDR-based) position measurement and one instant Received-Signal-Strength-based (RSS-based) position measurement. This situation can arise in a smartphone-based indoor positioning system when we want to locate a moving user in real-time with sustainable accuracy, but the RSS sampling ability of smartphones is limited; for example, one RSS sample per second. Firstly, by investigating RSS’s heterogeneity, we offer a solution for RSS-based continuous positioning problems under a low RSS sampling rate that satisfies real-time requirements. Secondly, we propose a method to improve accuracy for the RSS-based position estimation method, i.e., multilateration using Least Square Estimation. We consider PDR-based and improved RSS-based positions both have Gaussian uncertainty due to initial position plus drifting and RSS-to-distance conversion, respectively. Then, the Kalman filter will fuse two kinds of Gaussian distribution to produce more precise positions. The method is intended to design a real-time system for locating a moving target. Experiments are conducted in real indoor space with a commodity device. Its results show that our proposed solution is highly accurate and feasible in actual deployment.