A Comparison of Indoor Positioning Approaches with UWB, IMU, WiFi and Magnetic Fingerprinting

Accurate indoor positioning is quite hard to be achieved with a single sensor embedded in the current generation of mobile devices (e.g. smartphones), hence it is commonly accepted that the integration of information from different sensors is a sine qua non condition in order to improve the indoor positioning performance of such devices. Among the sensors typically used to such aim, it is possible to list inertial, magnetic sensors and radio transreceivers. In particular, pedestrian dead reckoning typically relies on the use of inertial sensor navigation, aided with external information, provided for example from the WiFi radio signal, to compensate the inertial navigation drift. WiFi is used for positioning either exploiting the radio signal strength (RSS) path loss, which can be used to estimate the distance of the device from access points and then trilaterate the device position, or with a fingerprinting approach. The latter has been recently used also for the magnetic field. One of the main issues related to fingerprinting is the long training phase, needed to determine a reliable WiFi/magnetic field model. In this paper, a set of Ultra Wide Band (UWB) devices has been used to obtain a reference trajectory of a moving pedestrian. In particular, first, the paper compares different approaches used to obtain an appropriate reference trajectory from the UWB measurements, and, then, it investigates the use of UWB positioning to speed up the fingerprinting training phase, showing the characteristics of the WiFi and magnetic datasets collected and processed in this way.