Hybrid indoor tracking with Bayesian sensor fusion of RF localization and inertial navigation

The reliable tracking of humans and materials in indoor scenarios is an ongoing research issue. For example, the monitoring of humans in partially hazardous environments - like the surroundings of an underground longwall mining infrastructure - is crucial to save human lives. A centroid location estimation technique based on received signal strength (RSS) readings offers a well known and low-cost tracking solution in such a rough environment where many other systems with optical, magnetical or ultrasound sensors fail. Due to signal fading the RSS values alone can not ensure a precise tracking. The sensor fusion of the RSS-based localization with an inertial navigation system (INS) leads to a more precise tracking. The longterm stability of the RSS-based localization and the good short-term accuracy of the INS are combined using a Kalman filter. The experimental results on a motion test track show that a tracking of humans in multipath environments is possible with low infrastructural costs.