Platform for hybrid positioning based on a sensor description language

While in the last years many different methods for indoor positioning have been presented, no single system has emerged yet that satisfies the requirements of the many different application scenarios. We propose to solve this problem with a platform for hybrid positioning which chooses the most suitable from all available sensor information for position estimation. For that task, a sensor description language is defined which allows for the description of different sensors for indoor positioning, but also offers the possibility to describe various sensor fusion algorithms or even existing positioning systems. The contribution of our work is twofold: The platform for hybrid positioning and the sensor description language. The feasibility of our approach is demonstrated by a prototypical evaluation of the sensor combination mechanism and estimation component, showing that hybrid positioning with a sensor description language is possible and offers advantages over single existing or proprietary systems by being able to cope with dynamically changing environments and varying user preferences.