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  • Research Article
  • Open Access

Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning

EURASIP Journal on Advances in Signal Processing20062006:086706

https://doi.org/10.1155/ASP/2006/86706

  • Received: 23 June 2005
  • Accepted: 29 January 2006
  • Published:

Abstract

This paper presents an aided dead-reckoning navigation structure and signal processing algorithms for self localization of an autonomous mobile device by fusing pedestrian dead reckoning and WiFi signal strength measurements. WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation. A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems.

Keywords

  • Mobile Device
  • Kalman Filter
  • Particle Filter
  • Strength Measurement
  • Inertial Navigation System

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Authors’ Affiliations

(1)
Division R&D, TECH/IDEA, France Telecom, Meylan, 38243, France

References

  1. Breaking news: Canada mandates 911 for VoIP TelecomWeb, April 2005, http://www.telecomweb.com/news/1112721769.htm
  2. Bahl P, Padmanabhan VN: RADAR: an in-building RF-based user location and tracking system. Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), March 2000, Tel Aviv, Israel 2: 775-784.Google Scholar
  3. Chen Y, Kobayashi H: Signal strength based indoor geolocation. Proceedings of the IEEE International Conference on Communications (ICC '02), April-May 2002, New York, NY, USA 1: 436-439.View ArticleGoogle Scholar
  4. Welch G, Bishop G: An introduction to the kalman filter. University of North Carolina, Chapel Hill, NC, USA; 2001.Google Scholar
  5. Kalman RE: A new approach to linear filtering and prediction problems. Transactions of the ASME—Journal of Basic Engineering 1960, 82: 35-45. 10.1115/1.3662552View ArticleGoogle Scholar
  6. Arulampalam MS, Maskell S, Gordon N, et al.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002, 50(2):174-188. 10.1109/78.978374View ArticleGoogle Scholar
  7. Gustafsson F, Gunnarsson F, Bergman N, et al.: Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing 2002, 50(2):425-437. 10.1109/78.978396View ArticleGoogle Scholar
  8. Doucet A, de Freitas N, Gordon N: Sequential Monte-Carlo Methods in Practice, Statistics for Engineering and Information Science. Springer, New York, NY, USA; 2001.View ArticleMATHGoogle Scholar
  9. Gilliéron P-Y, Buchel D, Spassov I, et al.: Indoor navigation performance analysis. Proceedings of the 8th European Navigation Conference (GNSS '04), May 2004, Rotterdam, The NetherlandsGoogle Scholar
  10. Gilliéron P-Y, Merminod B: Personal navigation system for indoor applications. Proceedings of the 11th IAIN World Congress, October 2003, Berlin, GermanyGoogle Scholar
  11. Motley AJ, Keenan JMP: Personal communication radio coverage in buildings at 900 MHz and 1700 MHz. Electronics Letters 1988, 24(12):763-764. 10.1049/el:19880515View ArticleGoogle Scholar
  12. Vaughan R, Andersen JB: Channels, Propagation and Antennas for Mobile Communications, Electromagnetic Waves Series 50. The Institution of Electrical Engineers, London, UK; 2003.View ArticleGoogle Scholar
  13. Smailagic A, Kogan D: Location sensing and privacy in a context-aware computing environment. IEEE Wireless Communications 2002, 9(5):10-17. 10.1109/MWC.2002.1043849View ArticleGoogle Scholar
  14. Battiti R, Nhat TL, Villani A: Location-aware computing: a neural network model for determining location in wireless LANs. Department of Information and Communication Technology, University of Trento, Trento, Italy; February 2002.Google Scholar
  15. Hatami A, Pahlavan K: A comparative performance evaluation of RSS-based positioning algorithms used in WLAN networks. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '05), March 2005, New Orleans, La, USA 4: 2331-2337.Google Scholar
  16. Roos T, Myllymäki P, Tirri H, Misikangas P, Sievänen J: A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks 2002, 9(3):155-164. 10.1023/A:1016003126882View ArticleGoogle Scholar
  17. Roos T, Myllymäki P, Tirri H: A statistical modeling approach to location estimation. IEEE Transactions on Mobile Computing 2002, 1(1):59-69. 10.1109/TMC.2002.1011059View ArticleGoogle Scholar
  18. Musso C, Oudjane N, Gland FL: Improving regularized particle filters. In Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science. Springer, New York, NY, USA; 2001:247-271. chapter 12View ArticleGoogle Scholar
  19. Evennou F, Marx F, Novakov E: Map-aided indoor mobile positioning system using particle filter. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '05), March 2005, New Orleans, La, USA 4: 2490-2494.Google Scholar
  20. Liao L, Fox D, Hightower J, et al.: Voronoi tracking: location estimation using sparse and noisy sensor data. Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03), October 2003, Las Vegas, Nev, USA 1: 723-728.Google Scholar
  21. Samsung : Samsung introduces world's first "3-dimensional movement recognition" phone. Website, January 2005Google Scholar
  22. Iribarne JV: Atmospheric Thermodynamics. D.Reidel, Dordrecht, Holland; 1973. chapter VIIView ArticleGoogle Scholar
  23. Beiser A: Earth Sciences. McGraw-Hill, New York, NY, USA; 1975. chapter 2Google Scholar
  24. Atmospheric pressure http://www.scubageek.com/geek/articles/wwwatm.html
  25. Robinson M, Psaromiligkos I: Received signal strength based location estimation of a wireless LAN client. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '05), March 2005, New Orleans, La, USA 4: 2350-2354.Google Scholar

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