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Indoor Network Map Matching by Hidden Markov Model

은닉 마르코프 모델을 이용한 실내 네트워크 맵 매칭

  • Kim, Tae Hoon (Dept. of Computer Engineering, Pusan National University) ;
  • Li, Ki-Joune (Dept. of Computer Engineering, Pusan National University)
  • Received : 2015.01.09
  • Accepted : 2015.04.29
  • Published : 2015.06.30

Abstract

Due to recent improvement of various sensor technologies, indoor positioning becomes available. However, Indoor positioning technologies by Wi-Fi radio map and acceleration sensor and digital campus still have a certain level of errors and a number of researches have been done to increase the positioning accuracy of the indoor positioning. If we could provide a room level accuracy, indoor location based services with current indoor positioning methods such as Wi-Fi radio map and acceleration sensors would be possible. In this paper, we propose an indoor map matching method to provide a room level accuracy based on hidden markov model.

최근 다양한 센서들의 성능 개선으로 실내측위가 가능해졌다. 하지만 Wi-Fi 라디오 맵을 이용한 실내 측위나 가속도 센서와 디지털 캠퍼스를 이용한 실내 측위는 아직 상당한 오차를 가지고 있어 지금까지의 연구는 실내 측위의 정확성을 높이는 측위 기술에 대해 많이 진행되었다. 하지만 좌표단위가 아닌 방 단위의 정확성을 가진 실내 맵 매칭이 가능하다면 Wi-Fi 라디오 맵, 가속도 센서 기반의 현재 실내측위기술로도 실내 서비스가 가능하다. 이에 본 연구는 방 단위의 정확성을 가지는 실내 맵 매칭을 위해, 실내 네트워크 맵 매칭에 대해 정의하고, 이를 수행하며 생기는 이슈들에 대해 살펴보고, 이를 해결하기 위해 은닉 마르코프 모델을 사용한 방안에 대해 제시한다.

Keywords

References

  1. BuildNGO, 2014, Accessed October 1. http://www.sailstech.com/#!buildngo-en/c1hnx.
  2. Chung, Y. S; Yoon, H. M; Choi, K. C. 2000, Classification of Map-matching Techniques and A Development, Journal of the Korean society for geospatial information system, 8(1):73-84.
  3. Elliott, R. J; Aggoun, L; Moore, J. B. 1994, Hidden Markov Models: Estimation and Control, Springer, New York.
  4. JOSM, 2014, Accessed October 1. https://josm.openstreetmap.de.
  5. Kang, H. Y; Hwang, J. R; Li, K. J. 2011, Location Tracking in Indoor Symbolic Space with RFID Sensors, Journal of Korea Spaial Information Society, KSIS, 19(3):53-62.
  6. Kim, D. H; Hwang, K. B. 2008, Indoor Location Estimation Using Hidden Markov Models with Reliable Variance Estimates, Proceedings of Korea Institute of Information Scientsts and Engineers, KISISE, 35(2):311-315.
  7. Laura, R; Yael, M; Christian, S. J. 2014, Using Cameras to Improve Wi-Fi Based Indoor Positioning, Paper presented at the annual meeting for Web and Wireless Geographical Information Systems, Springer, May 29-30.
  8. Liu, H; Darabi, H; Banerjee, P; Liu, J. 2007, Survey of Wireless Indoor Positioning Techniques and Systems, IEEE Transaction on System, Man, and Cybernetics, Part C, 37(6):1067-1080. https://doi.org/10.1109/TSMCC.2007.905750
  9. Li, K. J; Lee, J. Y. 2013, Basic Concepts of Indoor Spatial Information Candidate Standard IndoorGML and its Applications, Journal of Korea Spaial Information Society, KSIS, 21(3):1-10.
  10. OGC IndoorGML, 2014, Accessed October 1, http://www.opengeospatial.org/projects/groups/indoorgmlswg.
  11. Wu, C; Yang, Z; Liu, Y; Xi, W. 2013, WILL: Wireless Indoor Localization without Site Survey, TPDS Journal, 24(4):839-848.
  12. Xiao, Z; Wen, H; Markham, A; Trigoni N. 2014, Lightweight Map Matching for Indoor Localisation Using Conditional Random Fields, Paper presented at the annual meeting for International Conference on Information Processing in Sensor Networks, IEEE Press, April 15-17.