(An) adaptive user tracking algorithm using irregular data frames for passive fingerprint positioning

Abstract

WiFi fingerprinting is the most popular indoor positioning method today by representing received signal strength (RSS) values as a vector-type fingerprint. Unlike the active fingerprinting method, passive fingerprinting has the advantage of being able to track location without user participation by utilizing the signal that are naturally emitted from the user's smartphone. However, since signals are generated depending on the user's network usage pattern, there is a problem in that data is irregularly collected according to the pattern. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performance for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, It is expected that the stability of indoor positioning and the quality of location-based services will be improved.