Evaluation of Smartphone-based Indoor Positioning Using Different Bayes Filters

Within the research project LOBSTER, a system for analyzing the behavior of escaping groups of people in crisis situations within public buildings to support first responders is developed. The smartphone-based indoor localization of the escaping persons is performed by using positioning techniques like WLAN fingerprinting and dead reckoning realized with MEMS-IMU. Hereby, WLAN fingerprinting is analyzed especially in areas of few access points and the IMU-based dead reckoning is accomplished using step detection and heading estimation. The data of all sensors are fused in combination with building layouts using different Bayes filters. The behavior of the Bayes filters is investigated especially within indoor environments. The restrictions of the Kalman filter are shown as well as the advantages of a Particle filter using building plans.