Advertisement

Wireless Personal Communications

, Volume 44, Issue 3, pp 263–282 | Cite as

A Survey on Wireless Position Estimation

  • Sinan GeziciEmail author
Article

Abstract

In this paper, an overview of various algorithms for wireless position estimation is presented. Although the position of a node in a wireless network can be estimated directly from the signals traveling between that node and a number of reference nodes, it is more practical to estimate a set of signal parameters first, and then to obtain the final position estimation using those estimated parameters. In the first step of such a two-step positioning algorithm, various signal parameters such as time of arrival, angle of arrival or signal strength are estimated. In the second step, mapping, geometric or statistical approaches are commonly employed. In addition to various positioning algorithms, theoretical limits on their estimation accuracy are also presented in terms of Cramer–Rao lower bounds.

Keywords

Position estimation Cramer–Rao lower bound Mapping techniques Bayerian estimation Maximum likelihood estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Caffery J.J. (2000). Wireless location in CDMA cellular radio systems. Kluwer, BostonGoogle Scholar
  2. 2.
    “IEEE 15-03-0489-03-004a-application-requirement-analysis-031127 v0.4.” [Online]. Available: http://www.ieee802.org/15/pub/TG4.html
  3. 3.
    IEEE P802.15.4a/D4 (Amendment of IEEE Std 802.15.4), “Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LRW-PANs),” July 2006.Google Scholar
  4. 4.
    Commission, F. C. (1996). Revision of the commissions rules to insure compatibility with enhanced 911 emergency calling systems. FCC Docket No. 94–102.Google Scholar
  5. 5.
    Gezici S., Tian Z., Giannakis G.B., Kobayashi H., Molisch A.F., Poor H.V., Sahinoglu Z. (2005). Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine 22(4): 70–84CrossRefGoogle Scholar
  6. 6.
    Gustafsson F., Gunnarsson F. (2005). Mobile positioning using wireless networks. IEEE Signal Processing Magazine 22(4): 41–53CrossRefGoogle Scholar
  7. 7.
    Weiss A.J. (2004). Direct position determination of narrowband radio frequency transmitters. IEEE Signal Processing Letters 11(5): 513–516CrossRefGoogle Scholar
  8. 8.
    Qi Y., Kobayashi H., Suda H. (2006). Analysis of wireless geolocation in a non-line-of-sight environment. IEEE Transactions on Wireless Communications 5(3): 672–681CrossRefGoogle Scholar
  9. 9.
    Proakis J.G. (2000). Digital communications (4th ed.). Mc Graw Hill, New YorkGoogle Scholar
  10. 10.
    Qi, Y. (2004). Wireless geolocation in a non-line-of-sight environment. Ph.D. Dissertation, Princeton University.Google Scholar
  11. 11.
    Patwari N., Ash J.N., Kyperountas S., Hero A.O., Moses R.L., Correal N.S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine 22(4): 54–69CrossRefGoogle Scholar
  12. 12.
    Gezici S., Sahinoglu Z., Kobayashi H., Poor H.V. (2005) Ultra wideband geolocation. Wiley, in Ultrawideband Wireless Communications, New YorkCrossRefGoogle Scholar
  13. 13.
    Mallat, A., Louveaux, J., & Vandendorpe, L. (2007). UMB based positioning in multipath channels: CRBS for AOA and for hybrid TOA-AOA based methods. In: Proceedings of the IEEE international conference on communications (ICC), Glasgow, Scotland, June 2007.Google Scholar
  14. 14.
    Lee J.-Y., Scholtz R.A. (2002). Ranging in a dense multipath environment using an UWB radio link. IEEE Journal of Selected Areas Communications 20(9): 1677–1683CrossRefGoogle Scholar
  15. 15.
    Lindsey W.C., Simon M.K. (1991). Phase and Doppler measurements in two-way phase-coherent tracking systems. Dover, New YorkGoogle Scholar
  16. 16.
    Turin G.L. (1960). An introduction to matched filters. IRE Transactions on Information Theory IT-6(3): 311–329CrossRefMathSciNetGoogle Scholar
  17. 17.
    Pallas M.-A., Jourdain G. (1991). Active high resolution time delay estimation for large BT signals. IEEE Transactions on Signal Processing 39: 781–788CrossRefGoogle Scholar
  18. 18.
    Guvenc, I. & Sahinoglu, Z. (2005). Threshold-based TOA estimation for impulse radio UWB systems. In Proceedings of the IEEE int. conf. UWB (ICU), Zurich, Switzerland, pp. 420–425.Google Scholar
  19. 19.
    Gezici, S., Sahinoglu, Z., Kobayashi, H., Poor, H. V., & Molisch, A. F. (2005). A two-step time of arrival estimation algorithm for impulse radio ultrawideband systems. In Proceedings of the 13th European signal processing conf. (EUSIPCO 2005), Antalya, Turkey.Google Scholar
  20. 20.
    Yang, L., & Giannakis, G. B. (2004). Blind uwb timing with a dirty template. In Proceedings of the intl. conf. on acoustics, speech and signal processing, Montreal, Quebec, Canada, Vol. 4, pp. 509–512.Google Scholar
  21. 21.
    Poor H.V. (1994). An introduction to signal detection and estimation. Springer, New YorkzbMATHGoogle Scholar
  22. 22.
    Cook C.E., Bernfeld M. (1970). Radar signals: An introduction to theory and applications. Academic Press, New YorkGoogle Scholar
  23. 23.
    Botteron C., Host-Madsen A., Fattouche M. (2004). Cramer-rao bounds for the estimation of multipath parameters and mobiles’ positions in asynchronous ds-cdma systems. IEEE Transactions on Signal Processing 52(4): 862–875CrossRefMathSciNetGoogle Scholar
  24. 24.
    Caffery J.J., Stuber G.L. (1998). Subscriber location in CDMA cellular networks. IEEE Transactions on Vehicular Technology 47(2): 406–416CrossRefGoogle Scholar
  25. 25.
    Knapp C., Carter G. (1976). The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing 24: 320–327CrossRefGoogle Scholar
  26. 26.
    Champagne, B., Eizenman, M., & Pasupathy, S. (1989). Exact maximum likelihood time delay estimation. In Proceedings of the international conference of acoustics, speech, and signal processing (ICASSP 1989), Glasgow, Scotland, Vol. 4, pp. 23–26.Google Scholar
  27. 27.
    Belanger, S. P. (1995). Multisensor TDOA estimation in a multipath propagation environment using the em algorithm. In Proceedings of the 29th asilomar conference on signals, systems and computers (ASILOMAR 1995), Pacific Grove, CA, Vol. 2, pp. 1096–1100.Google Scholar
  28. 28.
    Aatique, M. (1997). Evaluation of TDOA techniques for position location in CDMA. Master’s thesis, Virginia Polytechnic Institute and State University.Google Scholar
  29. 29.
    Cong L., Zhuang W. (2002). Hybrid TOA/AOA mobile user location for wideband CDMA cellular systems. IEEE Transactions on Wireless Communications 1(3): 439–447CrossRefGoogle Scholar
  30. 30.
    Catovic A., Sahinoglu Z. (2004). The Cramer–Rao bounds of hybrid TOA/RSS and TDOA/RSS location estimation schemes. IEEE Communications Letters 8: 626–628CrossRefGoogle Scholar
  31. 31.
    Cong L., Zhuang W. (2002). Hybrid TDOA/AOA mobile user location for wide-band cdma cellular systems. IEEE Transactions on Wireless Communications 1: 439–447CrossRefGoogle Scholar
  32. 32.
    Reza, R. I. (2000). Data fusion for improved TOA/TDOA position determination in wireless systems. Ph.D. Dissertation, Virginia Tech.Google Scholar
  33. 33.
    Nerguizian, C., Despins, C., & Affes, S. (2001). Framework for indoor geolocation using an intelligent system. In Proceedings of the 3rd IEEE workshop on wireless LANs, Newton, MA.Google Scholar
  34. 34.
    Triki, M., Slock, D. T. M., Rigal, V., & Francois, P. (2006) Mobile terminal positioning via power delay profile fingerprinting: Reproducible validation simulations. In Proceedings of the IEEE vehicular technology conference (VTC 2006 Fall), Montreal, Canada.Google Scholar
  35. 35.
    Althaus, F., Troesch, F., & Wittneben, A. (2005). Uwb geo regioning in rich multipath environment. In Proceedings of the IEEE vehicular technology conference (VTC 2005 Fall), Dallas, TX.Google Scholar
  36. 36.
    Nerguizian C., Despins C., Affes S. (2006). Geolocation in mines with an impulse response fingerprinting technique and neural networks. IEEE Transactions on Wireless Communications 5: 603–611Google Scholar
  37. 37.
    McGuire M., Plataniotis K.N., Venetsanopoulos A.N. (2003). Location of mobile terminals using time measurements and survey points. IEEE Transactions on Vehicular Technology 52(4): 999–1011CrossRefGoogle Scholar
  38. 38.
    Gezici, S., Kobayashi, H., & Poor, H. V. (2003). A new approach to mobile position tracking. In Proceedings of the IEEE sarnoff symposium on advances in wired and wireless communications, Ewing, NJ, pp. 204–207.Google Scholar
  39. 39.
    Lin, T.-N., & Lin, P.-C. (2005). Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks. In Proceedings of the international conference on wireless networks, communications and mobile computing, Maui, Hawaii, Vol. 2, pp. 1569–1574.Google Scholar
  40. 40.
    Duda R.O., Hart P.E., Stork D.G. (2000). Pattern classification (2nd ed.). Wiley- Interscience, New YorkGoogle Scholar
  41. 41.
    Sayed A.H., Taroghat A., Khajehnouri N. (2005). Network-based wireless location. IEEE Signal Processing Magazine 22(4): 24–40CrossRefGoogle Scholar
  42. 42.
    Cong L., Zhuang W. (2005). Non-line-of-sight error mitigation in mobile location. IEEE Transactions on Wireless Communications 4: 560–573CrossRefGoogle Scholar
  43. 43.
    Casas, R., Marco, A., Guerrero, J. J., & Falco, J. (2006). Robust estimator for non-line-of-sight error mitigation in indoor localization. EURASIP Journal on Applied Signal Processing, 2006, Article ID 43 429, 8 pages, doi:  10.1155/ASP/2006/43429.
  44. 44.
    Chen, P. C. (1999). A non-line-of-sight error mitigation algorithm in location estimation. In Proceedings of the IEEE wireless communications and networking conference (WCNC 1999), New Orleans, LA, Vol. 1, pp. 316–320.Google Scholar
  45. 45.
    Caffery J.J., Stuber G.L. (1998). Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36(4): 38–45CrossRefGoogle Scholar
  46. 46.
    Al-Jazzar, S., & Caffery, J. J. (2002). ML and bayesian toa location estimators for NLOS environments. In Proceedings of the IEEE vehicular technology conference (VTC 2002) Fall, Vancouver, BC, Vol. 2, 1178–1181.Google Scholar
  47. 47.
    Kim W., Lee J.G., Jee G.I. (2006). The interior-point method for an optimal treatment of bias in trilateration location. IEEE Transactions on Vehicular Technology 55(4): 1291–1301CrossRefGoogle Scholar
  48. 48.
    Borras, J., Hatrack, P., & Mandayam, N. B. (1998). Decision theoretic framework for NLOS identification. In Proceedings of the IEEE vehicular technology conference (VTC 1998), Ottowa, ON, Canada, Vol. 2, pp. 1583–1587.Google Scholar
  49. 49.
    Venkatraman, S., & Caffery, J. (2002). A statistical approach to non-line-of-sight BS identification. In Proceedings of the 25th international symposium on wireless personal multimedia communications, Honolulu, HI, pp. 296–300.Google Scholar
  50. 50.
    Gezici, S., Kobayashi, H., & Poor, H. V. (2003). Non-parametric non-line-of-sight identification. In Proceedings of the IEEE 58th vehicular technology conference (VTC 2003 Fall), Orlando, FL, Vol. 4, pp. 2544–2548.Google Scholar
  51. 51.
    Al-Jazzar, S., Caffery, J. J., & You, H.-R. (2002). A scattering model based approach to NLOS mitigation in TOA location systems. In Proceedings of the IEEE vehicular technology conference (VTC 2002) spring, Birmingham, AL, pp. 861–865.Google Scholar
  52. 52.
    Qi Y., Kobayashi H., Suda H. (2006). On time-of-arrival positioning in a multipath environment. IEEE Transactions on Vehicular Technology 55(5): 1516–1526CrossRefGoogle Scholar
  53. 53.
    Qi, Y., & Kobayashi, H. (2003). On relation among time delay and signal strength based geolocation methods. In Proceedings of the IEEE global communications conference, San Francisco, CA, Vol. 7, pp. 4079–4083.Google Scholar
  54. 54.
    Arulampalam S., Maskell S., Gordon N., Clapp T. (2002). A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2): 174–188CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2007

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringBilkent UniversityBilkentTurkey

Personalised recommendations