Earticle

초록

영어

Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquitous. The user-based localization algorithm utilizes the information of the Received Signal Strength(RSS) from the surrounding access points(APs) to determine the user position. In this paper, focusing on the development of a user localization uses existing WiFi environment for its low cost and ease of deployment. We propose an indoor localization of WiFi based on support vector machines(ILW-SVM), and use the bilinear median interpolation method(BMIM) to reduce the calibration effort on creating fingerprint map while still retaining the accuracy of user localization. According to comparison of accuracy of three different kernel functions, choosing the radial basis function(RBF) as kernel function. In addition, we also propose improved ILW-SVM algorithm to solve the indoor localization that nearest neighbor points are not concentrated. At last, overall comparison of kNN, ILW-SVM and improved ILW-SVM in consideration of accuracy. Experimental results indicate that the proposed algorithm can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and stabilization.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Indoor Localization System
  3.1. Offline Training Phase
  3.2. Online Localization Phase
 4. Increasing Localization Accuracy by Using ILW-SVM
  4.1. Support Vector Classification Model
  4.2. Localization Based on Improved ILW-SVM
 5. Experiment Results and Evaluation
  5.1. Experimental Platform Building
  5.2. Reconstruction Fingerprint Map by BMIM
  5.3. Performance of ILW-SVM algorithm
  5.4. Improved ILW-SVM: Expanded Real-time Measurement of RSS
  5.5. Error Analysis of the BMIM
  5.6. Comparison of Localization Error
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• YuFeng [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]
• JiangMinghua [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]
• LiangJing [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]
• QinXiao [ Department of Computer Science and Software Engineering Auburn University, Auburn AL 36849 America ]
• HuMing [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]
• PengTao [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]
• HuXinrong [ School of Electronic and Electrical Engineering Wuhan Textile University, Wuhan 430200 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605