DFM 2013 - Special Session on Data Fusion Methods for Indoor Localization of People and Objects

Indoor positioning has gained great importance since technology allows for affordable realtime sensing and processing systems. Also the pervasiveness of WSNs (e.g., in the form of WLAN) and mobile sensors (such as smartphones) has inspired researchers and developers to exploit the existing infrastructure. Applications include pedestrian navigation in public buildings and shops, location based services, safety for the elderly and impaired, museum guides, surveillance tasks, but also tracking products in manufacturing, warehousing etc., the list goes on. Unlike outdoor environments, which are covered by GNSS to a satisfiable extent, indoor navigation faces additional challenges, depending on the underlying measurement system, such as occlusions, reflections and attenuation. There is a great variety of sensors and measuring principles, however, in practice every single measuring technique suffers from deficits. While RF and (ultra-)sound are subject to multipath propagation, optical systems are intolerant to NLOS conditions. Some systems require setting up beacons, while others are self-calibrating and easy-to-install. Obviously, data fusion can overcome these limitations by combining complementary, and redundant sensing techniques, and using elaborate algorithmic methods, such as stochastic filtering. This special session addresses fundamental techniques, recent developments and future research directions to help clear the way toward robust, accurate, indoor localization.