Statistical Evaluation and Synthetic Generation of Ultra-Wideband Distance Measurements for Indoor Positioning Systems

Ultra-Wide Band (UWB) radios provide high-precision distance measurements, however are still prone to ranging errors due to the environment. For localization system design and testing, this leads to cost-intense field trials in which localization methods are validated. As an alternative, publicly available UWB datasets exist, which however are also limited to only certain environmental conditions. In order to provide a more general evaluation basis, we present and discuss a procedure which allows the synthetic generation of UWB distance measurements with respect to theoretical error sources and parameterization based on a conducted measurement campaign. This contribution builds an understanding of commonly present errors for wireless distance measurements and their effects on ranging residual distributions in the distance domain. In addition, we discuss the influences of non-line-of-sight and multipath conditions. Based on this work, distance measurements with respect to the multipath richness of the propagation environment can be generated, helping to flexibly evaluate positioning methods for various emerging applications for telematics systems in the consumer, industrial, or transportation sector. These application fields are ranging from unmanned aerial vehicles, in-house parking systems, and intelligent freight wagons to connected aircraft cabins.