An Adaptive Hybrid Filter for WiFi-Based Positioning Systems

와이파이 기반 측위 시스템을 위한 적응형 혼합 필터

  • 박남준 (한국과학기술원 정보통신공학과) ;
  • 정석훈 (한국과학기술원 정보통신공학과) ;
  • 문윤호 (전주기전대학교 부사관과) ;
  • 한동수 (한국과학기술원 전산학과)
  • Received : 2013.06.24
  • Accepted : 2013.07.08
  • Published : 2013.08.31


As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

기존의 와이파이 기반 측위 시스템에서 주로 사용되는 칼만필터와 파티클 필터는 실내공간의 구조적 특성을 반영하지 못해 정확도가 낮고, 계산 부하 또한 높기 때문에 휴대기기를 이용한 실내 측위에 적용하는데 한계를 지닌다. 이러한 한계를 극복하고자 본 논문은 와이파이 기반 측위 시스템을 위한 적응형 혼합필터를 제안한다. 제안된 필터는 칼만 필터의 일반적인 적용 체계를 활용하였으며, 적은 수의 파티클을 사용한 파티클 필터의 개념 또한 추가되었다. 제안된 필터는 일반 칼만 필터와는 달리 예측 가중치를 동적으로 변화시켜 동작하며, 위치 예측을 위한 파티클을 실내공간의 경로 네트워크상에 한정하는 특징을 지닌다. 검증결과 적응형 혼합 필터는 일반 칼만 필터에 비해 높은 정확도를 보이며, 일반 파티클 필터에 비해서도 정확도 및 계산시간의 측면에서 유의할만한 성능향상을 보였다.



  1. R. KALMAN, "A new approach to linear filtering and prediction problems", ASME DC Journal of Basic Engineering, vol. 82, no. 1, pp.35-45, Mar, 1960.
  2. N. Sheimy, E. Shin, X. Jiniu, "Kalman filter Face-Off: extended vs. unscented Kalman filters for integrated GPS and MEMS inertial", InsideGNSS, vol. 1, no. 2, pp.48-54, Mar. 2006.
  3. Y. Chiou, C. Wang, S. Yeh, "An adaptive location estimator using tracking algorithms for indoor WLANs", ACM Journals on Wireless Networks, vol. 16, no. 7, pp.1987-2012, Oct. 2010.
  4. 정승환, "Wi-Fi 기반 옥내측위를 위한 확장칼만 필터 방법", Journal of Information Technology Applications & Management, vol. 15, no. 2, pp51-65, 2008
  5. A. Doucet, S. Godsill, C. Andrieu, "On sequential monte carlo sampling methods for bayesian filtering", ACM Journals on Statistics and Computing, vol. 10, no. 3, pp.197-208, July 2000.
  6. F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, P. Nordlund, "Particle filters for positioning, navigation, and tracking", IEEE Transactions on Signal Processing, vol. 50, no. 2 pp. 425-437, Feb. 2002.
  7. F. Evennou, F. Marx, "Advanced integration of WiFi and inertial navigation systems for indoor mobile positioning", EURASIP Journals on Applied Signal Processing, vol. 2006, pp.164-174, Jan. 2006.
  8. R. Jirawimut, P. Ptasinski, V. Garaj, F. Cecelja, W. Balachandran, "A method for dead reckoning parameter correction in pedestrian navigation system", IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 1, pp.209-2015, Feb. 2003.
  9. L. Fang, P. Antsaklis, L. Montestruque, B. McMickell, M. Lemmon, Y. Sun, H. Fang, I. Koutroulis, M. Haenggi, M. Xie, X. Xie, "Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience", IEEE Transactions on Instrumentation and Measurement, vol. 54, no. 6, pp.2342-2358, Dec. 2005.
  10. A. Davison, I. Reid, N. Molton, O. Stasse, "MonoSLAM: Real-time single samera SLAM", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp.1052-1067, Jun. 2007.
  11. C. Tsai, "A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements", IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 5, pp.1399-1404, Oct. 1998.
  12. J. Hightower, G. Borriello, "Particle filters for location estimation in ubiquitous computing: A Case Study", in Proc. ACM Int. Conf. Ubiquitous Computing 2004 (ACM UbiComp 2004), pp.88-106, Nottingham, UK, Sep. 2004.
  13. A. Paul, E. Wan, "RSSI-based indoor localization and tracking using sigma-point Kalman smoothers", IEEE Journal of Selected Topics in Signal Processing, Vol. 3, no. 5, pp.860-873, Oct. 2009.
  14. J. Yim, S. Jeong, J. Joo, C. Park, "Utilizing map information for WLAN-based Kalman filter indoor tracking", in Porc. IEEE Int. Conf. Futrue Generation Communications and networking Symposia (IEEE FGCNS 2008), pp.58-62. Hainan Island, China, Dec. 2008.
  15. A. Bekkali, H. Sanson, M. Matsumoto, "RFID indoor positioning based on probabilistic RFID map and Kalman filtering", in Proc. IEEE Int. Conf. Wireless and Mobile Computing, Networking and Communications 2007 (IEEE WiMOB 2007), pp.21-27, White plains, USA, Oct. 2007.
  16. H. Lemelson, M. Kjaergaard, R. Hansen, T. King, "Error estimation for indoor 802.11 location fingerprinting", in Proc. ACM Int. Symp. Location and Context Awareness 2009 (ACM LoCA 2009), pp.138-155, Tokyo, Japan, May 2009.