The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

The Indoor Localization Algorithm using the Difference Means based on Fingerprint in Moving Wi-Fi Environment

이동 Wi-Fi 환경에서 핑거프린트 기반의 Difference Means를 이용한 실내 위치추정 알고리즘

  • Kim, Tae-Wan (Department of Computer & Media, Tongmyong University) ;
  • Lee, Dong Myung (Department of Computer Engineering, Tongmyong University)
  • Received : 2015.12.17
  • Accepted : 2016.10.31
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

The indoor localization algorithm using the Difference Means based on Fingerprint (DMFPA) to improve the performance of indoor localization in moving Wi-Fi environment is proposed in this paper. In addition to this, the performance of the proposed algorithm is also compared with the Original Fingerprint Algorithm (OFPA) and the Gaussian Distribution Fingerprint Algorithm (GDFPA) by our developed indoor localization simulator. The performance metrics are defined as the accuracy of the average localization accuracy; the average/maximum cumulative distance of the occurred errors and the average measurement time in each reference point.

본 논문에서는 Wi-Fi환경에서 실내 위치추정의 성능 향상을 위해 이동 Wi-Fi 환경에서 핑거프린트 기반의 Difference Means를 이용한 실내 위치추정 알고리즘 (Algorithm using the Difference Means based on Fingerprint, DMFPA)을 제안하였다. 그리고 자체 개발한 실내 위치추정 시뮬레이터를 사용하여 제안한 DMFPA의 성능을 일반적인 핑거프린트 알고리즘 (OFPA), 가우시안 분포를 핑거프린트 알고리즘 (GDFPA)의 성능을 서로 비교하였다. 성능분석 항목은 각 참조구역에서의 평균위치추정 정확도, 발생된 오차의 평균오차 누적거리와 최대오차 누적거리, 그리고 평균측정시간으로 정의하였다.

Keywords

References

  1. M. Weiser, "The Computer for the 21th Centry," Scientific American, pp. 94-104, Sep. 1991.
  2. S. W. Lee and S. W. Kim, "A trend and vision for the indoor positioning technology," KICS Inf. and Commun. Mag., vol. 32, no. 2, pp. 81-88, Jan. 2015.
  3. H. Y. Kim, et al., "Localization and tracking technologies in near fields," Weekly Technol. Trends, IITA, pp. 1-13, 2007.
  4. H. Y. Kim, Internet of Things - Concept, Implementation Technologies and Business, Hongrung Publishing Company, 2014.
  5. M. I. Ji, et al., "Development trends of the indoor localization technology based on Wi-Fi," KICS Inf. and Commun. Mag., vol. 28, no. 7, pp. 52-58, Jun. 2011.
  6. Y. Kim, H. Shin, and H. Cha, "Smartphonebased Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variation problem," Pervasive Mob. Comput., vol. 9, no. 3, pp. 406-420, Jun. 2013. https://doi.org/10.1016/j.pmcj.2012.12.003
  7. C. Laoudias, R, Pich, and C. G. Panayiotou, "Device self-calibration in location systems using signal strength histograms," Location Based Serv., vol. 7, no. 3, pp. 165-181, Aug. 2013. https://doi.org/10.1080/17489725.2013.816792
  8. E. E. L. Lau, B. G. Lee, S. C. Lee, and W. Y. Chung, "Enhanced RSSI-Based high accuracy real-time user location tracking system for indoor and outdoor environments," Smart Sens. and Intell. Syst., vol. 1, no. 2, Jun. 2008.
  9. S. Son, Y. Park, B. Kim, and Y. B, "Wi-Fi fingerprint location estimation system based on reliability," J. KICS, vol. 38, no. 6, pp. 531-539, Jun. 2013.
  10. I. Ha, Z. Zhang, H. Park, and C. Kim, "Analysis of Wi-Fi signal characteristics for indoor positioning measurement," J. KIICE, vol. 16, no. 10, pp. 2177-2184, Oct. 2012.
  11. K. Kaemarungsi and P. Krishnamurthy, "Modeling of indoor positioning system based on location fingerprinting," in Proc. IEEE. INFOCOM 2004, vol. 2, pp. 1012-1022, Mar. 2004.
  12. T. Lin and P. Lin, "Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks," in Proc. IEEE Wireless Netw., Commun. and Mob. Comput. 2005, vol. 2, pp. 1569-1574, Jun. 2005.
  13. A. Hatami and K. Pahlavan, "Comparative statistical analysis of indoor positioning using empirical data and indoor radio channel models," in Proc. IEEE CCNC 2006, vol. 2, pp. 1018-1022, Jan. 2006.
  14. I. H. Jeoung, C. M. Kim, Y. S. Choi, S. B. Kim, and Y. Lee, "A study of establishment on radiomap that utilizes the mobile device indoor positioning DB based on Wi-Fi," J. KOGSIS, vol. 22, no. 3, pp. 57-69, Sep. 2014. https://doi.org/10.7319/kogsis.2014.22.3.057
  15. H. K. Oh and I. C. Kim, "WiFi location estimation in indoor environments using gaussian processes," J. KIISE: Comput. Practices and Lett., vol. 17, no. 10, pp. 564-568, Oct. 2011.
  16. Normal Distribution, Retrieved Jul. 15, 2015, from https://en.wikipediaorg/wiki/Normal_distribution