Skip to main content
SpringerLink
Log in

On Data Fusion for Parametric-Model-Based Wireless Localization

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

In this paper, we present a data fusion framework for parametric-model-based wireless localization where the mobile station location is treated as a deterministic unknown vector. Three types of fusion schemes are presented: measurement fusion, estimate fusion and mixed fusion. Theoretical performance comparison among these schemes in terms of the estimation root mean square error via the weighted least square estimator (WLSE) is conducted. Such a performance metric coincides with the Cramer-Rao lower bound (CRLB) in the case of Gaussian noise. We show that, if the raw measurement vectors are correlated, then measurement fusion achieves the best performance, mixed fusion follows and estimate fusion is the worst. If the raw measurement vectors are uncorrelated, then these different fusion schemes achieve the same performance. Benefits that can be earned from data fusion for wireless localization are also investigated and numerical examples are presented to validate our theoretical analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. J. Caffery and G. L. Stuber, Subscriber location in CDMA cellular networks, IEEE Transactions on Vehicular Technology, Vol. 47, No. 2, pp. 406–416, 1998.

    Article  Google Scholar 

  2. M. Vossiek, L. Wiebking, P. Gulden, J. Wieghardt, C. Hoffmann, and P. Heide, Wireless local positioning, IEEE Microwave Magazine, Vol. 4, No. 4, pp. 77–86, 2003.

    Article  Google Scholar 

  3. F. Gustafsson and F. Gunnarsson, Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements, IEEE Signal Processing Magazine, Vol. 22, No. 4, pp. 41–53, 2005.

    Article  Google Scholar 

  4. Y. Zhao, Standardization of mobile phone positioning for 3G systems, IEEE Communications Magazine, Vol. 40, No. 7, pp. 108–116, 2002.

    Article  Google Scholar 

  5. H. Liu, H. Darabi, P. Banerjee, and J. Liu, Survey of wireless indoor positioning techniques and systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 37, No. 6, pp. 1067–1080, 2007.

    Article  Google Scholar 

  6. B. Hofmann-Wellenhof, H. Lichtenegger, and J. Collins, Global Positioning System (GPS). Theory and Practice. Springer, Wien, 1992.

    Book  Google Scholar 

  7. J. Caffery, A new approach to the geometry of TOA Location, in Vehicular Technology Conference Vtc 2000—Fall: 52nd Vehicular Technology Conference Boston, MA, USA, September 24–28, 2000, Vol. 4, pp. 1943–1949, 2000.

    Google Scholar 

  8. L. Cong and W. Zhuang, Hybrid TDOA/AOA mobile user location for wideband cdma cellular systems, IEEE Transactions on Wireless Communications, Vol. 1, No. 3, pp. 439–447, 2002.

    Article  Google Scholar 

  9. C. Drane, M. Macnaughtan, and C. Scott, Positioning gsm telephones, IEEE Communications Magazine, Vol. 36, No. 4, pp. 46–54, 1998.

    Article  Google Scholar 

  10. N. Patwari, A. O. Hero, M. Perkins, N. S. Correal, and R. J. O’Dea, Relative location estimation in wireless sensor networks, IEEE Transactions on Signal Processing, Vol. 51, No. 8, pp. 2137–2148, 2003.

    Article  Google Scholar 

  11. C. Fritsche and A. Klein, Cramer-rao lower bounds for hybrid localization of mobile terminals, in The 5th Workshop on Positioning, Navigation and Communication 2008 (WPNC'08), March 2008, pp. 157–164, 2008.

  12. P. Deng and P. Fan, An AOA assisted TOA positioning system, in Communication Technology Proceedings, 2000. WCC-ICCT 2000. International Conference on. 21–25, Vol. 2, 2000.

  13. S. Al-Jazzar and M. Ghogho, A joint TOA/AOA constrained minimization method for locating wireless devices in non-line-of-sight environment, in Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007 IEEE 66th, pp. 496–500, 2007.

  14. S. Venkatraman and J. Caffery, Hybrid TOA/AOA techniques for mobile location in non-line-of-sight environments, Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE, Vol. 1, pp. 274–278, 2004.

    Google Scholar 

  15. M. Wylie and J. Holtzman, The non-line of sight problem in mobile location estimation, in 5th IEEE International Conference on Universal Personal Communications, Vol. 2, pp. 827–831, 1996.

  16. R. Juang, D. Lin, and H. Lin, Hybrid SADOA/TDOA mobile positioning for cellular networks, IET Communications, Vol. 1, No. 2, pp. 282–287, 2007.

    Article  MathSciNet  Google Scholar 

  17. K. Yu and Y. Guo, Improving anchor position accuracy for 3-d localization in wireless sensor networks, in Communications, 2008. ICC ’08. IEEE International Conference on, May 2008, pp. 951–955, 2008.

  18. T. Kleine-Ostmann and A. Bell, A data fusion architecture for enhanced position estimation inwireless networks, IEEE communications letters, Vol. 5, No. 8, pp. 343–345, 2001.

    Article  Google Scholar 

  19. H. Mitchell, Multi-Sensor Data Fusion: An Introduction. Springer, New York, 2007.

  20. S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice-Hall, Upper Saddle River, 1993.

  21. M. A. Spirito, On the accuracy of cellular mobile station location estimation, IEEE Transactions on Vehicular Technology, Vol. 50, No. 3, pp. 674–685, 2001.

    Article  Google Scholar 

  22. A. Urruela, J. Sala, and J. Riba, Average performance analysis of circular and hyperbolic geolocation, IEEE Transactions on Vehicular Technology, Vol. 55, No. 1, pp. 52–66, 2006.

    Article  Google Scholar 

  23. G. Seber, A Matrix Handbook for Statisticians. Wiley-Interscience, Hoboken, 2007.

  24. W. Sun and Y.-x. Yuan, Optimization Theory and Methods. Springer, New York, 2006.

  25. Y. Qi, H. Kobayashi, and H. Suda, Analysis of wireless geolocation in a non-line-of-sight environment, IEEE Transactions on Wireless Communications, Vol. 5, No. 3, pp. 672–681, 2006.

    Article  Google Scholar 

  26. S. Golden and S. Bateman, Sensor measurements for Wi-Fi location with emphasis on time-of-arrival ranging, IEEE Transactions on Mobile Computing, Vol. 6, No. 10, pp. 1185–1198, 2007.

    Article  Google Scholar 

  27. A. Catovic and Z. Sahinoglu, The Cramer-Rao bounds of hybrid TOA/RSS and TDOA/RSS location estimation schemes, IEEE Communications Letters, Vol. 8, No. 10, pp. 626–628, 2004.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Robin Wentao Ouyang & Albert Kai-Sun Wong

Authors
  1. Robin Wentao Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Albert Kai-Sun Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robin Wentao Ouyang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ouyang, R.W., Wong, A.KS. On Data Fusion for Parametric-Model-Based Wireless Localization. Int J Wireless Inf Networks 19, 22–37 (2012). https://doi.org/10.1007/s10776-011-0161-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-011-0161-1

Keywords

Search

Navigation