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

  • 제41권9호
  • /
  • Pages.1060-1068
  • /
  • 2016
  • /
  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지
DOI QR코드

DOI QR Code

실제 네트워크를 고려한 베이지안 필터 기반 이동단말 위치 추적

Bayesian Filter-Based Mobile Tracking under Realistic Network Setting

  • Kim, Hyowon (Hanyang University Department of Electronics and Computer Engineering) ;
  • Kim, Sunwoo (Hanyang University Department of Electronics Engineering)
  • 투고 : 2016.07.21
  • 심사 : 2016.09.01
  • 발행 : 2016.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

연결정보만을 이용하는 range-free 측위 기법의 성능은 이동성을 갖는 무선 단말 움직임에 취약한 문제점이 있다. 본 논문은 실제 전파 환경을 고려한 실내 네트워크에서 베이지안 필터를 사용하여 실시간으로 움직이는 무선장치를 추적하는 두 가지 알고리즘을 제안하였다. 제안하는 알고리즘은 측정 모델의 선형성에 따라 Kalman filter 와 Markov Chain Monte Carlo (MCMC) particle filter를 적용하였다. Kalman과 MCMC particle filter 기반 알고리즘은 각각 무선단말 간 연결정보를, 이동 단말의 한 홉 간격 내 단말로부터 수신하는 신호의 세기 (RSS: received signal strength)와 연결정보를 혼합한 융합정보를 측정 모델로 사용하였다. 정확한 시뮬레이션을 위해 실내 쇼핑몰 지도를 구현한 네트워크 지형, 그리고 라디오 불규칙도 모델을 적용하였다. 또한, 장애물 존재 여부에 따라 라디오 불규칙도를 분류하였다. 성능평가를 위해 MATLAB 시뮬레이션을 수행하였으며, 기존 range-free 측위 기법보다 향상된 위치정확도를 확인하였다.

The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.

키워드

참고문헌

  1. W. H. Chin, Z. Fan, and R. Haines, "Emerging technologies and research challenges for 5G wireless networks," IEEE Wirel. Commun. Mag., vol. 21, no. 2, pp. 106-112, Apr. 2014.
  2. S. Lee and S. Kim, "Prospect and trend of indoor localization," KICS Inf. Commun. Mag., vol. 32, no. 2, pp. 81-47, Feb. 2015.
  3. B. Bangerter, S. Talwar, R. Arefi, and K. Stewart, "Networks and devices for the 5G era," IEEE Commun. Mag., vol. 52, no. 2, pp. 90-96, Feb. 2014. https://doi.org/10.1109/MCOM.2014.6736748
  4. F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski, "Five disruptive technology direction for 5G," IEEE Commun. Mag., vol. 52, no. 2, pp. 74-80, Feb. 2014.
  5. X. Shen, "Device-to-device communications in 5G cellular networks," IEEE Network Mag., vol. 29, no. 2, pp. 2-3, Mar. 2015. https://doi.org/10.1109/MNET.2015.7293295
  6. M. N. Tehrani, M. Uysal, and H. Yanikomeroglu, "Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions," IEEE Commun. Mag., vol. 52, no. 5, pp. 86-92, May. 2014.
  7. D. Niculescu and B. Nath, "Ad Hoc positioning system," in Proc. IEEE GLOBECOM, pp. 25-29, San Antonio, United States, Nov. 2001.
  8. Y. Shang, W. Ruml, and Y. Zhang, "Improved MDS-based localization," in Proc. IEEE INFOCOM, pp. 2640-2651, Hong Kong, Mar. 2004.
  9. Y. Wang, X. Wang, D. Wang, and D. P. Agrawal, "Range-free localization using expected hop progress in wireless sensor networks," IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 10, pp. 1540-1552, Oct. 2009. https://doi.org/10.1109/TPDS.2008.239
  10. H. Kim, J. Lee, S. Lee, and S. Kim, "Performance analysis of range-free localization algorithm based on grid-scan," in Proc. KICS Winter Conf., vol. 1, pp. 396-397, Gangwon, Korea, Jan. 2016.
  11. S. Lee, J. Choi, and S. Kim, "Probabilistic-based range-free localization for anisotropic sensor networks," in Proc. KICS Winter Conf., vol. 1, pp. 856-857, Jan. 2013.
  12. S. Lee, C. Park, M. J. Lee, and S. Kim, "Multihop range-free localization with approximate shortest path in anisotropic wireless sensor networks," EURASIP J. Wirel. Commun. Netw., no. 1, pp. 1-12, May 2014.
  13. S. Lee, B. Koo, and S. Kim, "RAPS: reliable anchor pair selection for range-free localization in anisotropic networks," IEEE Commun. Lett., vol. 18, no. 8, pp. 1403-1406, Aug. 2014. https://doi.org/10.1109/LCOMM.2014.2332156
  14. H. Kim, J. Kim, C. Sin, C. Park, M. Lee, and S. Kim, "Kalman-based mobility tracking with range-free localization under a realistic device distribution," in Proc. Int. Conf. Localization GNSS, pp. 1-6, Barcelona, Spain, Jun. 2016.
  15. G. F. Welch, Kalman Filter, Springer, 2014.
  16. B. Ristic, S. Arulampalam, and N. J. Gordon, Beyond the Kalman filter: Particle filters for tracking applications, Artech house, 2004.
  17. Y. C. Tseng, S. Y. Ni, and E. Y. Shih, "adaptive approaches to relieving broadcast storms in a wireless multihop mobile ad hoc network," IEEE Trans. Comput., vol. 52, no. 5, pp. 545-557, May 2003. https://doi.org/10.1109/TC.2003.1197122
  18. D. Gao, P. Chen, C. H. Foh, and Y. Niu, "Hop-distance relationship analysis with quasi-UDG model for node localization in wireless sensor networks," EURASIP J. Wirel. Commun. Netw., no. 1, pp. 1-11, Sept. 2011.
  19. L. Mihaylova, D. Angelova, S. Honary, D. R. Bull, C. N. Canagarajah, and B. Ristic, "Mobility tracking in cellular networks using particle filtering," IEEE Trans. Wireless Commun., vol. 6, no. 10 , pp. 3589-3599, Oct. 2007. https://doi.org/10.1109/TWC.2007.05912