KNN/ANN Hybrid Location Determination Algorithm for Indoor Location Base Service

실내 위치기반서비스를 위한 KNN/ANN Hybrid 측위 결정 알고리즘

  • Lee, Jang-Jae (Dept. of Computer Science and Statistics, Chosun University) ;
  • Jung, Min-A (Dept. of Computer Engineering, Mokpo National University) ;
  • Lee, Seong-Ro (Dept. of Information and Electronics Engineering, Mokpo National University) ;
  • Song, Iick-Ho (Dept. of Electrical Engineering, KAIST)
  • 이장재 (조선대학교 컴퓨터통계학과) ;
  • 정민아 (목포대학교 컴퓨터공학과) ;
  • 이성로 (목포대학교 정보전자공학과) ;
  • 송익호 (한국과학기술원 전기및전자공학과)
  • Received : 2010.10.11
  • Accepted : 2010.11.23
  • Published : 2011.03.25


As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. 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. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.


Supported by : 한국연구재단


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