Utilizing building layout for performance optimization of a multi-sensor fusion model in indoor navigation

Indoor navigation requires the integration of different sensors to be able to perform continuous position determination of the user. Along with Global Navigation Satellite System (GNSS), other technologies like WLAN, UWB, RFID and aiding sensors such as accelerometers, gyroscopes, magnetometers, etc. can be integrated in a multi-sensor system. The use of Kalman Filter (KF) as a sensor fusion model for multi-sensor navigation systems is nowadays a popular choice. In this paper, a KF-based fusion model is implemented for fusing the measurements obtained from WLAN, high-sensitivity GPS along with some aiding sensors like accelerometer and digital compass. The fusion filter provides improved positioning accuracy to a single-technology system and offers higher positioning availability in time. In the experiment conducted inside a typical office building, the KF-based fusion model achieves a horizontal accuracy of around 6 meters with a 1-Hz update rate. Aiding the fusion filter with the building layout information further improves the horizontal position accuracy to around 4.95 meters. The building layout is utilized in the fusion model in such a way that it restricts the heading measurements to be within some known discrete values derived from the layout.