Wireless Networks

, Volume 11, Issue 1–2, pp 189–204 | Cite as

Robotics-Based Location Sensing Using Wireless Ethernet

  • Andrew M. Ladd
  • Kostas E. Bekris
  • Algis RudysEmail author
  • Lydia E. Kavraki
  • Dan S. Wallach


A key subproblem in the construction of location-aware systems is the determination of the position of a mobile device. This article describes the design, implementation and analysis of a system for determining position inside a building from measured RF signal strengths of packets on an IEEE 802.11b wireless Ethernet network. Previous approaches to location-awareness with RF signals have been severely hampered by non-Gaussian signals, noise, and complex correlations due to multi-path effects, interference and absorption. The design of our system begins with the observation that determining position from complex, noisy and non-Gaussian signals is a well-studied problem in the field of robotics. Using only off-the-shelf hardware, we achieve robust position estimation to within a meter in our experimental context and after adequate training of our system. We can also coarsely determine our orientation and can track our position as we move. Our results show that we can localize a stationary device to within 1.5 meters over 80% of the time and track a moving device to within 1 meter over 50% of the time. Both localization and tracking run in real-time. By applying recent advances in probabilistic inference of position and sensor fusion from noisy signals, we show that the RF emissions from base stations as measured by off-the-shelf wireless Ethernet cards are sufficiently rich in information to permit a mobile device to reliably track its location.


wireless networks 802.11 mobile systems localization probabilistic analysis 


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  1. [1]
    A.A. Argyros, K.E. Bekris and S.C. Orphanoudakis, Robot homing based on corner tracking in a sequence of a panoramic images, in: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Vol. 2, Kauai, HI (December 2001) pp. 3–10. Google Scholar
  2. [2]
    P. Bahl and V.N. Padmanabhan, Enhancements to the RADAR user location and tracking system. Technical Report MSR-TR-2000-12, Microsoft Research (February 2000). Google Scholar
  3. [3]
    P. Bahl and V.N. Padmanabhan, RADAR: An in-building RF-based user location and tracking system, in: Proc. of IEEE Infocom 2000, Vol. 2, Tel Aviv, Israel (March 2000) pp. 775–784. Google Scholar
  4. [4]
    W. Burgard, A. Cremers, D. Fox, D. Hahnel, G. Lakemeyer, D. Schulz, W. Steiner and S. Thrun, The interactive museum tour-guide robot, in: Proc. of the 15th National Conference on Artificial Intelligence (AAAI-98), Madison, WI (July 1998), pp. 11–18. Outstanding paper award. Google Scholar
  5. [5]
    P. Castro, P. Chiu, T. Kremenek and R.R. Muntz, A probabilistic room location service for wireless networked environments, in: Proc. of the 3rd International Conference on Ubiquitous Computing (Ubicomp), Atlanta, GA (September 2001) pp. 18–24. Google Scholar
  6. [6]
    A. Chakraborty, A distributed architecture for mobile, location-dependent applications, Master’s thesis, Massachusetts Institute of Technology (May 2000). Google Scholar
  7. [7]
    H. Choset and K. Nagatani, Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization, IEEE Transactions on Robotics and Automation 17(2) (2001) 125–137. CrossRefGoogle Scholar
  8. [8]
    T.W. Christ and P.A. Godwin, A prison guard duress alarm location system, in: Proc. IEEE International Carnahan Conference on Security Technology (October 1993) pp. 106–116. Google Scholar
  9. [9]
    I. Cox, Blanche – an experiment in guidance and navigation of an autonomous robot vehicle, IEEE Transactions on Robotics and Automation 7(2) (1991) 193–204. CrossRefGoogle Scholar
  10. [10]
    T. Cutler, Wireless Ethernet and How to Use It, The Online Industrial Ethernet Book, Vol. 5 (1999). Google Scholar
  11. [11]
    A.J. Davison and N. Kita, 3D simultaneous localization and map-building using active vision for a robot moving on undulating terrain, in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Vol. 1, Kauai, HI (December 2001) pp. 384–391. Google Scholar
  12. [12]
    L. Doherty, K.S.J. Pister and L.E. Ghaoui, Convex position estimation in wireless sensor networks, in: Proc. of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies, Vol. 3, Anchorage, AK (April 2001) pp. 1655–1663. Google Scholar
  13. [13]
    G. Dudek and M. Jenkins, Computational Principles of Mobile Robotics (Cambridge University Press, 2000). Google Scholar
  14. [14]
    Federal Communications Commission Report and Order 96-264: Revision of the commission’s rules to ensure compatibility with Enhanced 911 emergency calling systems (July 1996),
  15. [15]
    D. Fox, W. Burgard, F. Dellaert and S. Thrun, Monte Carlo localization: Efficient position estimation for mobile robots, in: Proc. of the 16th National Conference on Artificial Intelligence (AAAI-99), Orlando, FL (1999) pp. 343–349. Google Scholar
  16. [16]
    D. Fox, W. Burgard, H. Kruppa and S. Thrun, A probabilistic approach to collaborative multi-robot localization, Autonomous Robots 8(3) (2000) 325–344. CrossRefGoogle Scholar
  17. [17]
    D. Fox, W. Burgard and S. Thrun, Markov localization for mobile robots in dynamic environments, Journal of Artificial Intelligence Research (JAIR) 11 (November 1999) 391–427. Google Scholar
  18. [18]
    J. Guivant and E. Nebot, Optimization of the simultaneous localization and map building algorithm for real time implementation, Journal of Robotics Research 17(10) (2000) 565–583. Google Scholar
  19. [19]
    J.-S. Gutmann and D. Fox, An experimental comparison of localization methods continued, in: Proc. of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Lausanne, Switzerland (September 2002) pp. 454–459. Google Scholar
  20. [20]
    A. Haeberlen, E. Flannery, A.M. Ladd, A. Rudys, D.S. Wallach and L.E. Kavraki, Practical robust location over large-scale 802.11 wireless networks, in: Proc. of the 10th ACM International Conference on Mobile Computing and Networking (MOBICOM), Philadelphia, PA (September 2004) pp. 70–84. Google Scholar
  21. [21]
    P. Harley, Short distance attenuation measurements at 900 MHz and 1.8 GHz using low antenna heights for microcells, IEEE Journal on Selected Areas in Communications (JSAC) 7(1) (1989) 5–11. CrossRefGoogle Scholar
  22. [22]
    A. Harter, A. Hopper, P. Steggles, A. Ward and P. Webster, The anatomy of a context-aware application, in: Proc. of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM 1999), Seattle, WA, (August 1999) pp. 59–68. Google Scholar
  23. [23]
    H. Hashemi, Impulse response modeling of indoor radio propagation channels, IEEE Journal on Selected Areas in Communications (JSAC) 11 (1993) 967–978. CrossRefGoogle Scholar
  24. [24]
    H. Hashemi, The indoor radio propagation channel, Proceedings of the IEEE 81(7) (1993) 943–968. CrossRefGoogle Scholar
  25. [25]
    J. Hightower and G. Borriello, Location systems for ubiquitous computing, IEEE Computer 34(8) (2001) 57–66. Google Scholar
  26. [26]
    J. Hightower, R. Want and G. Borriello, SpotON: An indoor 3D location sensing technology based on RF signal strength, Technical Report UW CSE 00-02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA (February 2000). Google Scholar
  27. [27]
    Institute of Electrical and Electronics Engineers, Inc., ANSI/IEEE Standard 802.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications (1999). Google Scholar
  28. [28]
    K. Konolige and K. Chou, Markov localization using correlation, in: Proc. of the 17th International Joint Conference on Artificial Intelligence (IJCAI), Seattle, WA (August 1999) pp. 1154–1159. Google Scholar
  29. [29]
    J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale and S. Shafer, Multi-camera multi-person tracking for EasyLiving, in: Proc. of the 3rd IEEE International Workshop on Visual Surveillance, Dublin (July 2000) pp. 3–10. Google Scholar
  30. [30]
    J. Krumm and J. Platt, Minimizing calibration effort for an indoor 802.11 device location measurement system, Technical Report MSR-TR-2003-82, Microsoft Research, Seattle, WA (November 2003). Google Scholar
  31. [31]
    B. Kuipers and Y.T. Byan, A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Journal on Robotics and Automatic Systems 8 (1991) 47–63. CrossRefGoogle Scholar
  32. [32]
    J.J. Leonard and H.F. Durrant-Whyte, Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation 7(3) (1991) 376–382. CrossRefGoogle Scholar
  33. [33]
    J.J. Leonard and H.F. Durrant-Whyte, Simultaneous map building and localization for an autonomous mobile robot, in: Proc. of the IEEE/RSJ International Workshop on Intelligent Robots and Systems (IROS), Vol. 3, Osaka, Japan (November 1991) pp. 1442–1447. Google Scholar
  34. [34]
    T. Liu, P. Bahl and I. Chlamtac, Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks, IEEE Journal on Selected Areas in Communications 16(6) (1998) 922–936. CrossRefGoogle Scholar
  35. [35]
    T. Logsdon, Understanding the Navstar: GPS, GIS and IVHS, 2nd ed. (Van Nostrand-Reinhold, New York, 1995). Google Scholar
  36. [36]
    G. Marceau, The McGill’s RedDogs legged league system, in: Proc. of Robocup 2000 (2000) pp. 627–630. Google Scholar
  37. [37]
    A. Neskovic, N. Neskovic and G. Paunovic, Modern approaches in modeling of mobile radio systems propagation environment, IEEE Communications Surveys and Tutorials 3(3) (3rd Quarter 2000) pp. 2–12. Google Scholar
  38. [38]
    A.B. Poritz, Hidden Markov models: a guided tour, in: Proc. of the International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, New York (April 1988) pp. 7–13. Google Scholar
  39. [39]
    N. Priyantha, A. Chakraborty and H. Balakrishman, The Cricket location support system, in: Proc. of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM 2000), Boston, MA (August 2000) pp. 32–43. Google Scholar
  40. [40]
    N. Priyantha, A. Miu, H. Balakrishman and S. Teller, The Cricket compass for context-aware mobile applications, in: Proc. of the 7th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM 2001), Rome, Italy (July 2001) pp. 1–14. Google Scholar
  41. [41]
    L.R. Rabiner and B.-H. Juang, An introduction to hidden Markov models, IEEE ASSP Magazine 3(1) (1986) 4–16. Google Scholar
  42. [42]
    T. Roos, P. Myllymaki, H. Tirri, P. Misikangas and J. Sievanan, A probabilistic approach to WLAN user location estimation, International Journal of Wireless Information Networks 9(3) (2002) 155–164. CrossRefGoogle Scholar
  43. [43]
    A. Savvides, C.-C. Han and M.B. Strivastava, Dynamic fine-grained localization in ad-hoc networks of sensors, in: Proc. of the 7th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Rome, Italy (July 2001) pp. 166–179. Google Scholar
  44. [44]
    R. Smith and P. Cheeseman, On the representation of spatial uncertainty, Journal of Robotics Research 5(4) (1987) 56–68. Google Scholar
  45. [45]
    P. Tao, A. Rudys, A.M. Ladd and D.S. Wallach, Wireless LAN location-sensing for security applications, in: Proc. of the ACM Workshop on Wireless Security, San Diego, CA (September 2003) pp. 11–20. Google Scholar
  46. [46]
    S. Thrun, Probabilistic algorithms in robotics, AI Magazine 21(4) (2000) 93–109. Google Scholar
  47. [47]
    S. Thrun, W. Burgard and D. Fox, A probabilistic approach to concurrent mapping and localization for mobile robots, Machine Learning 31(1–3) (1998) 29–53. CrossRefGoogle Scholar
  48. [48]
    S. Thrun, D. Fox, W. Burgard and F. Dellaert, Robust Monte Carlo localization for mobile robots, Artificial Intelligence 101 (2000) 99–141. Google Scholar
  49. [49]
    F. van Diggelen and C. Abraham, Indoor GPS technology, in: Proc. of CTIA Wireless-Agenda, Dallas, TX (May 2001). Google Scholar
  50. [50]
    R. Want, A. Hopper, V. Falco and J. Gibbons, The Active Badge location system, ACM Transactions on Information Systems 10 (1992) 91–102. CrossRefGoogle Scholar
  51. [51]
    A. Ward, A. Jones and A. Hopper, A new location technique for the active office, IEEE Personal Communications 4(5) (1997) 42–47. CrossRefGoogle Scholar
  52. [52]
    J. Werb and C. Lanzl, Designing a positioning system for finding things and people indoors, IEEE Spectrum 35(9) (1998) 71–78. CrossRefGoogle Scholar
  53. [53]
    R. Yamamoto, H. Matsutani, H. Matsuki, T. Oono and H. Ohtsuka, Position location technologies using signal strength in cellular systems, in: Proc. of the 53rd IEEE Vehicular Technology Conference, Vol. 4 (May 2001) pp. 2570–2574. Google Scholar
  54. [54]
    M. Youseff, A. Agrawala and A.U. Shankar, WLAN location determination via clustering and probability distributions, in: Proc. of the IEEE International Conference on Pervasive Computing and Communications (PerCom), Forth Worth, TX (March 2003) pp. 143–150. Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Andrew M. Ladd
    • 1
  • Kostas E. Bekris
    • 1
  • Algis Rudys
    • 1
    Email author
  • Lydia E. Kavraki
    • 1
  • Dan S. Wallach
    • 1
  1. 1.Department of Computer ScienceRice UniversityHoustonUSA

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