  Indoor positioning systems (IPSs) can make an important contribution to the
analysis and optimization of internal transport processes. The overall aim of
this work is to examine how position data can be used to analyze the operation
of industrial trucks in warehouses. This is achieved by developing a concept
for the analysis of industrial truck operations based merely on position data.
The concept consists of a signal processing scheme to derive kinematic data and
three analysis methods - Monitoring, Area analysis, and Motion analysis.
Schemes for the signal processing and detection of motion events were developed
and implemented as part of the TrOpLocerApp (Truck Operation Localization
Analyzer-Application) for recording, displaying, and processing position data,
according to the proposed system concept. The TrOpLocer-App source code is
published on GitLab. Different filter algorithms were examined, as part of the
signal processing scheme, from which the low pass Butterworth filter has shown
the best results in static experiments. Validation of the motion detection
scheme shows good detection quality for distinct events in a realistic movement
experiment.
