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A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System

Communication Research Center, School of Electronics Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Communication Department, Shenyang Artillery Academy, Shenyang 110867, China
Author to whom correspondence should be addressed.
Sensors 2014, 14(1), 1850-1876;
Received: 26 December 2013 / Revised: 14 January 2014 / Accepted: 20 January 2014 / Published: 22 January 2014
(This article belongs to the Section Physical Sensors)
Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect. View Full-Text
Keywords: clustering; outliers; GA-SVM; kernel PCA; asymmetric matching clustering; outliers; GA-SVM; kernel PCA; asymmetric matching
MDPI and ACS Style

Mo, Y.; Zhang, Z.; Meng, W.; Ma, L.; Wang, Y. A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System. Sensors 2014, 14, 1850-1876.

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