WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Experiments on Local Positioning with Bluetooth
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
A novel backtracking particle filter for pattern matching indoor localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
RF-Based Initialisation for Inertial Pedestrian Tracking
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
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As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors. The results with different combinations of the available sensor information are compared.