Basic Positioning Techniques

  • Martin Werner
Chapter

Abstract

Positioning technology can be organized by the underlying geometric principles such as triangulation, dead reckoning, or presence detection which can be further subdivided. Alternatively, the algorithms can be organized along observable variables (time, time difference, angle-of-arrival, angle-of-emission, signal strength, acceleration, rotation, etc.). This chapter provides basic algorithms for positioning organized along the underlying geometric principles. After explaining the basic algorithms, a description of real-world approaches is given, organized by the employed sensor technology and observable variables.

Keywords

Mobile Device Signal Strength Time Synchronization Inertial Navigation System Mobile Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bahl, P., Padmanabhan, V.N.: Radar: an in-building rf-based user location and tracking system. In: Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), vol. 2, pp. 775–784 (2000)Google Scholar
  2. 2.
    Boggs, J.: Geolocation of an audio source in a multipath environment using time-of-arrival. Tech. rep., DTIC Document (2004)Google Scholar
  3. 3.
    Cobb, H.S.: Gps pseudolites: theory, design, and applications. Ph.D. thesis, Stanford University (1997)Google Scholar
  4. 4.
    Davidson, P., Collin, J., Takala, J.: Application of particle filters for indoor positioning using floor plans. In: Ubiquitous Positioning, Indoor Navigation, and Location Based Service. IEEE, New York (2010)Google Scholar
  5. 5.
    Dille, M., Grocholsky, B., Singh, S.: Outdoor downward-facing optical flow odometry with commodity sensors. In: Field and Service Robotics, pp. 183–193. Springer, Berlin (2010)Google Scholar
  6. 6.
    Evennou, F., Marx, F.: Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. Hindawi Publishing Corporation EURASIP J. Appl. Signal Process, 2006, 1–11 (2006). doi:10.1155/ASP/2006/86706CrossRefGoogle Scholar
  7. 7.
    Freund, R.W., Hoppe, R.H.W.: Stoer/Bulirsch: Numerische Mathematik 1. Zehnte, neu bearbeitete Auflage. Springer, Berlin/Heidelberg (2007)Google Scholar
  8. 8.
    Goyal, P., Ribeiro, V., Saran, H., Kumar, A.: Strap-down pedestrian dead-reckoning system. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation. IEEE, New York (2011)Google Scholar
  9. 9.
    Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34(8), 57–66 (2001)CrossRefGoogle Scholar
  10. 10.
    Huang, H., Gartner, G.: A survey of mobile indoor navigation systems. In: Cartography in Central and Eastern Europe, pp. 305–319. Springer, Berlin/Heidelberg (2010)Google Scholar
  11. 11.
    Kee, C., Yun, D., Jun, H., Parkinson, B., Pullen, S., Lagenstein, T.: Centimeter-accuracy indoor navigation using GPS-like pseudolites. In: GPSWorld (2001)Google Scholar
  12. 12.
    Küpper, A.: Location-Based Services: Fundamentals and Operation. Wiley, New York (2005)CrossRefGoogle Scholar
  13. 13.
    Link, J.A.B., Smith, P., Viol, N., Wehrle, K.: FootPath: accurate map-based indoor navigation using smartphones. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (2011)Google Scholar
  14. 14.
    Ludwig, R.: Methoden der Fehler- und Ausgleichsrechnung. Deutscher Verlag der Wissenschaften, Berlin (1971)Google Scholar
  15. 15.
    Nagatani, K., Tachibana, S., Sofne, M., Tanaka, Y.: Improvement of odometry for omnidirectional vehicle using optical flow information. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 468–473 (2000)Google Scholar
  16. 16.
    Orr, R., Abowd, G.: The smart floor: a mechanism for natural user identification and tracking. In: CHI: Extended Abstracts on Human Factors in Computing Systems, pp. 275–276. ACM, New York (2000)Google Scholar
  17. 17.
    Rizos, C., Roberts, G., Barnes, J., Gambale, N.: Locata: a new high accuracy indoor positioning system. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (2010)Google Scholar
  18. 18.
    Schwarz, H.R., Köckler, N.: Numerische Mathematik. Fünfte Auflage, B.G. Teubner-Verlag, Wiesbaden (2004)Google Scholar
  19. 19.
    Storms, W., Shockley, J., Raquet, J.: Magnetic field navigation in an indoor environment. In: Ubiquitous Positioning, Indoor Navigation, and Location Based Service. IEEE, New York (2010)Google Scholar
  20. 20.
    Travis, W., Simmons, A., Bevly, D.: Corridor navigation with a lidar/ins kalman filter solution. In: Intelligent Vehicles Symposium, pp. 343–348. IEEE, New York (2005)Google Scholar
  21. 21.
    van Diggelen, F.: Indoor GPS theory & implementation. In: Position, Location and Navigation Symposium, PLANS, pp. 240–247 (2002)Google Scholar
  22. 22.
    Ward, A., Jones, A., Hopper, A.: A new location technique for the active office. IEEE Pers. Commun. 4(5), 42–47 (1997)CrossRefGoogle Scholar
  23. 23.
    Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 114–123. ACM, New York (2008)Google Scholar
  24. 24.
    Xiao, W., Ni, W., Toh, Y.: Integrated wi-fi fingerprinting and inertial sensing for indoor positioning. In: International Conference on Indoor Positioning and Indoor Navigation. IEEE, New York (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Werner
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany

Personalised recommendations