Fingerprinting-based Dynamic RSS Adjustment for Time-variant Indoor Positioning Systems

Abstract

Fingerprinting is one of the most reliable technique for indoor positioning. The concept of the fingerprinting is to compare the offline received signal strength (RSS) database and online RSS measurement to find the most similar reference point to represent the user position. However, fingerprinting is sensitive to the environmental changes, where the offline database is required to be updated. In this paper, we discuss the offline and online measurement mismatch when the transmission power is altered by the Wi-Fi access point. We propose dynamic adjustment (DA) algorithm to reduce the influence caused by the power variation. Experimental results validate that the proposed DA algorithm maintains the positioning accuracy under the power adjustment.