Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Hybrid SVM/ANN Algorithm for Efficient Indoor Positioning Determination in WLAN Environment

WLAN 환경에서 효율적인 실내측위 결정을 위한 혼합 SVM/ANN 알고리즘

  • Kwon, Yong-Man (Department of Computer Science and Statistic, Chosun University) ;
  • Lee, Jang-Jae (Department of Computer Science and Statistic, Chosun University)
  • 권용만 (조선대학교 컴퓨터 통계학과) ;
  • 이장재 (조선대학교 컴퓨터 통계학과)
  • Received : 2011.07.26
  • Accepted : 2011.08.31
  • Published : 2011.09.30
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Abstract

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

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References

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