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Indoor localization improvement via adaptive RSS fingerprinting database

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3 Author(s)
Chavalit Koweerawong ; School of Information, Computer, and Communication Technology (ICT) Sirindhorn International Institute of Technology, Thammasat University Pathumthani, Thailand ; Komwut Wipusitwarakun ; Kamol Kaemarungsi

In location fingerprinting based indoor positioning system, received signal strength (RSS) indications from a set of Wi-Fi access points are used as a unique fingerprint to identify a specific location. However these RSS fingerprints may become outdated when there are unanticipated environmental changes. Re-measuring RSS fingerprints for all locations to maintain an up-to-date RSS database incurs high operational cost, which is impractical in dynamically changed environment. In this paper, we propose a method to estimate the RSS fingerprint of a specific location from a set of neighboring re-measured RSS fingerprints, called “feedbacks”. The proposed method searches for new feedbacks and some necessary old RSS fingerprints in the cut-off area and then applies plane-interpolation to calculate the new RSS fingerprint for a specific location. Based on simulation results, about 5% of re-measured RSS feedbacks are required to satisfy 80% of positioning correctness in the simulated 30×30 m2 area.

Published in:

The International Conference on Information Networking 2013 (ICOIN)

Date of Conference:

28-30 Jan. 2013