Passive Fingerprinting Reinforced by Active Radiomap for WLAN Indoor Positioning System

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2022-03
Language
English
Article Type
Article
Citation

IEEE SENSORS JOURNAL, v.22, no.6, pp.5238 - 5247

ISSN
1530-437X
DOI
10.1109/JSEN.2021.3127135
URI
http://hdl.handle.net/10203/292809
Appears in Collection
CS-Journal Papers(저널논문)
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