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

Yun Mo, Zhongzhao Zhang, Yang Lu, Weixiao Meng and Gul Agha

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

Abstract:

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.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/850926.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/850926.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:850926

DOI: 10.1155/2014/850926

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().