Adaptive Sensor Fusion Framework for Personalized Indoor Navigation

Abstract

Many methods using various sensors of the latest smartphones are being studied for accurate indoor navigation. In particular, sensor fusion frameworks integrate all information collectible from smartphones, such as Wi-Fi signals, and measurements obtained from gyroscopes, accelerometers, or magnetometers. However, sensor measurements contain unpredictable real-time errors made in dynamic indoor environments. In this paper, we propose a new sensor fusion framework that attains high positioning accuracy by learning errors. The proposed system discriminates errors in the sensor measurements and accumulates the errors to adjust the measurement values based on the accumulated error distributions. High positioning accuracy was achieved in experiments conducted in two typical environments, a corridor-type space, and an open space.