The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Weight of W-KNN for WiFi Fingerprint Positioning

WiFi 핑거프린트 위치추정 방식에서 W-KNN의 가중치에 관한 연구

  • Oh, Jongtaek (Dept. of Electronics Information Eng., Hansung University)
  • 오종택 (한성대학교 전자정보공학과)
  • Received : 2017.11.06
  • Accepted : 2017.12.08
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

본 논문에서는 최근 들어 활발하게 연구되고 있는 WiFi fingerprint를 이용한 실내 위치 인식 기술에서, Weighted K-Nearest Neighbour 방식을 적용할 때 사용되는 가중치에 대한 분석 결과를 보이고 있다. W-KNN 방식은 그 간결함에도 불구하고 WiFi fingerprint를 이용하는 다른 복잡한 방식들과 유사한 성능을 보이고 있어, 실제적으로 실내 위치 인식 기술로 많이 사용되고 있다. 또한 사전 데이터 처리 방식이나 이 방식에서 사용되는 가중치에 따라 성능 차이를 보이고 있으므로, 이에 대한 연구 및 분석은 중요한 의미가 있다. 여기서는 실제로 측정된 WiFi fingerprint 데이터를 기반으로, 데이터 사전처리 경우와 가중치에 측정값의 분산 및 거리를 적용하는 경우, 지점 위치 평균 개수 K를 사용하는 경우 등에 대해 위치 추정 오차를 분석하고 성능을 비교한다. 이 연구 결과는 실제로 실내 위치 인식 시스템을 구축할 때에 실용적으로 활용될 수 있다.

Keywords

References

  1. Jichao Jiao, Fei Li, Zhongliang Deng, and Wenjing Ma, "A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN," Sensors Journal, Vol. 17, No. 4, 2017. doi:10.3390/s17040704
  2. Myung-Gwan Kim, Jin-Woo Kim, "Implementation of Location-Aware System based on Probability Distribution of RSSI," The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 8, No. 4, pp. 9-14, Aug. 2008.
  3. Fabian Höflinger, Rui Zhang, Joachim Hoppe, Amir Bannoura, Leonhard, M. Reindl Johannes Wendeberg, Manuel Bührer, and Christian Schindelhauer, "Acoustic Self-calibrating System for Indoor Smartphone Tracking (ASSIST)," International Conference on Indoor Positioning and Indoor Navigation, Nov. 2012.
  4. Ville Honkavirta, Tommi Perala, Simo Ali-Loytty, and Robert Piche, "A Comparative Survey of WLAN Location Fingerprinting Methods," 6th Workshop on Positioning, Navigation and Communication, pp. 243-251, Hannover, Germany, March 2009. doi:10.1109/WPNC.2009.4907834
  5. Marius H. Hennecke and Gernot A. Fink, "Towards Acoustic Self-Localization of Ad Hoc Smartphone Arrays," Third Joint Workshop on Hands-free Speech Communication and Microphone Arrays, May 2011.
  6. David Madigan, Eiman Elnahrawy, Richard P. Martin, Wen-Hua Ju, P. Krishnan, and A. Krishnakumar, "Bayesian Indoor Positioning Systems," Conference on IEEE Computer and Communications Societies, March 2005.
  7. Azadeh Kushki, Konstantinos Plataniotis, and Anastasios Venetsanopoulos, "Kernel-Based Positioning in Wireless Local Area Networks," IEEE Tr. Mobile Computing, Vol. 6, No. 6, pp. 689-705, June 2007. https://doi.org/10.1109/TMC.2007.1017
  8. Nattapong Swangmuang and Prashant Krishnamurthy, "An Effective Location Fingerprinting Model for Wireless Indoor Localization," Pervasive and Mobile Computing, Vol. 4, pp. 836-850, 2008. https://doi.org/10.1016/j.pmcj.2008.04.005
  9. Khuong Nguyen, "A Performance Guaranteed Indoor Positioning System using Conformal Prediction and the WiFi Signal Strength," Journal Information and Telecommunication, Vol. 1, No. 1, pp. 41-65, 2017. doi:10.1080/24751839.2017.1295659
  10. Kamol Kaemarungsi, "Design of Indoor Positioning Systems based on Location Fingerprinting Technique," PhD diss., University of Pittsburgh, 2005.
  11. S. Zekavat and R. Buehrer, Handbook of position location: Theory, practice, and Advances, IEEE Press, 2012.