Dynamic route prediction with the magnetic field strength for indoor positioning

WiFi fingerprinting has been a popular approach for indoor positioning in the past decade. However, most existing fingerprint-based systems were designed as an on-demand service to guide the user to his wanted destination. This paper introduces a novel feature that allows the positioning system to predict in advance which walking route the user may use, and the potential destination. To achieve this goal, a new so-called routine database will be used to maintain the magnetic field strength in the form of the training sequences to represent the walking trajectories. The benefit of the system is that it does not adhere to a certain predicted trajectory. Instead, the system dynamically adjusts the prediction as more data are exposed throughout the user's journey. The proposed system was tested in a real indoor environment to demonstrate that the system not only successfully estimated the route and the destination, but also improved the single positioning prediction.