  Absolute position accuracy is the key performance criterion of an Indoor
Localization System (ILS). Since ILS are heterogeneous and complex
cyber-physical systems, the localization accuracy depends on various influences
from the environment, system configuration, and the application processes. To
determine the position accuracy of a system in a reproducible, comparable, and
realistic manner, these factors must be taken into account. We propose a
strategy for analyzing the influences on the position accuracy of ILS using
decision trees in combination with application-related or technology-related
categorization. The proposed strategy is validated using empirical data from
120 experiments. The accuracy of an Ultra-Wideband and a LiDAR-based ILS was
determined under different application-driven influencing factors, considering
the application of autonomous mobile robots in warehouses. Finally, the
opportunities and limitations of analyzing decision trees to compare system
performance, find a suitable system, optimize the environment or system
configuration, and understand the relevance of different influencing factors
are presented.
