Improved RSSI-based euclidean distance positioning algorithm for large and dynamic WLAN environments

Abstract

This paper presents an algorithm for RSSI fingerprint positioning based on Euclidean distance for the use in a priori existing larger and dynamically changing WLAN infrastructure environments. Symptomatical for such environments are changing sets of base stations for different calibration points and for calibration phase and positioning phase. The presented algorithm has an accuracy of 2.06m median location estimation error. The algorithm uses four threshold parameters to adapt the calculation to the specific measuring environment. Furthermore the reduction of calibration effort is investigated. It is shown that an enlargement of the calibration grid size from 1m to 6m increases the median location estimation error from 2.06m to 3.5m. Regular calibration measurements include measurements in four rectangular bearings. Reducing the number of calibration bearings results in less calibration effort, but worsens estimation quality.