Passive Fingerprinting Reinforced by Active Radiomap for WLAN Indoor Positioning System

Abstract

Passive fingerprinting collects the received signal strength (RSS) of wireless local area network (WLAN) signals emitted from a smartphone to create fingerprints and then to construct a radiomap for an indoor positioning system (IPS). However, human-impassable paths and irregular signal collection periods result in missing values in fingerprints. Moreover, missing fingerprints in shaded areas result in the degradation of the positioning accuracy. In this paper, we propose a novel passive fingerprinting method, introducing the concept of a WiFi monitor adjacency matrix and a method of transforming legacy active fingerprints to passive fingerprints by a linear regression model. Our system estimates walking paths with RSS data, removing outliers of human-impassable paths. Moreover, we can construct a high-performance radiomap by supplementing the missing signals as well as missing fingerprints in shaded areas. In an experiment conducted in a complex building environment, the proposed method achieved a positioning accuracy of 2.18 m, which is 32% better than that of a state-of-the-art system. The radiomap construction time was also greatly reduced.