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Mathematical Problems in Engineering
Volume 2014, Article ID 850926, 8 pages
Research Article

Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System

1Communication Research Center, Harbin Institute of Technology, Harbin 150080, China
2Key Engineering Research Center for Dedicated Communication Systems, Ministry of Education, Harbin 150080, China
3Open System Laboratory, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA

Received 5 July 2014; Accepted 9 October 2014; Published 22 October 2014

Academic Editor: Wanquan Liu

Copyright © 2014 Yun Mo et al. 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.


With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods.