RSSI-based Euclidean Distance algorithm for indoor positioning adapted for the use in dynamically changing WLAN environments and multi-level buildings

This paper presents positioning algorithms for WLAN environments based on RSSI-fingerprinting and Euclidean Distance (EDA). The adapted algorithms can be used in large and dynamically changing environments and multi-level buildings. Evaluation results show a median location estimation error (MLEE) of 2.12m. The discrete third spatial direction (floor level) was correctly estimated in 99% of cases. The calibration effort may be decreased by a factor of 4, if an increase of MLEE error from 2.12m to 2.69m is tolerated.