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Sensors 2016, 16(12), 2055; doi:10.3390/s16122055

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

School of Electronic Engineering, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
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Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 27 September 2016 / Revised: 17 November 2016 / Accepted: 29 November 2016 / Published: 2 December 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Abstract

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means. View Full-Text
Keywords: indoor positioning; fingerprinting; access point deployment; clustering algorithm; K-means; coordinate based clustering; smallest enclosing circle indoor positioning; fingerprinting; access point deployment; clustering algorithm; K-means; coordinate based clustering; smallest enclosing circle
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, W.; Fu, X.; Deng, Z. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments. Sensors 2016, 16, 2055.

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