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

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 th...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (2014), p.1-8
Main Authors: Meng, Weixiao, Lu, Yang, Zhang, Zhongzhao, Mo, Yun, Agha, Gul
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Clustering
Coarsening
Component reliability
Construction
Control systems
Euclidean space
Feature extraction
Fingerprinting
Indoor
Indoor environments
Machine learning
Manifolds (mathematics)
Mathematical analysis
Mathematical models
Methods
Principal components analysis
Wireless networks
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/850926