Decision Trees for Analyzing Influences on the Accuracy of Indoor Localization Systems

Publikationstyp
Conference Paper
Publikationsdatum
2022-09
Sprache
English
Author
Schyga, Jakob 
Plambeck, Swantje orcid-logo
Hinckeldeyn, Johannes orcid-logo
Fey, Görschwin orcid-logo
Kreutzfeldt, Jochen orcid-logo
Institut
Technische Logistik W-6 
Eingebettete Systeme E-13 
TORE-URI
http://hdl.handle.net/11420/14192
Citation
12th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2022)
Contribution to Conference
12th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2022 
Publisher DOI
10.1109/IPIN54987.2022.9917515
Scopus ID
2-s2.0-85141630787
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.
Schlagworte
decision trees
indoor localization
influences
test and evaluation
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