Context-adaptive algorithms to improve indoor positioning with inertial sensors

Body-mounted inertial systems for indoor positioning and pedestrian guidance have some major advantages against other technologies. They do not require any preinstalled facilities, i.e. they can run completely autonomous and all the necessary components are standard equipment of modern smart phones. The quick availability and autonomy is a special advantage in fields of application such as first responders and military, while the integration of all system components in an everyday life gadget makes it especially attractive for the consumer market. But in both cases there is a severe problem to solve: The positioning accuracy is very weak if only a simple double integration of the accelerations is performed. In the following two context-adaptive algorithms to improve indoor positioning with inertial sensors are presented and the achieved results are discussed. The first algorithm enhances the detection of zero-velocity updates (ZUPT); the other method improves positioning by map matching.