Skip to content


  • Research Article
  • Open Access

Application of Beamforming in Wireless Location Estimation

EURASIP Journal on Advances in Signal Processing20062006:051673

  • Received: 1 June 2005
  • Accepted: 1 December 2005
  • Published:


A simple technique to estimate the position of a given mobile source inside a building is based on the received signal strength. For this methodology to have a reasonable accuracy, radio visibility of the mobile by at least three access points is required. To reduce the number of the required access points and therefore simplify the underlying coverage design problem, we propose a novel scheme that takes into account the distribution of RF energy around the receiver. In other words, we assume that the receiver is equipped with a circular array antenna with beamforming capability. In this way, the spatial spectrum of the received power can be measured by electronically rotating the main lobe around the 360-degree field of view. This spatial spectrum can be used by a single receiver as a means for estimating the position of the mobile transmitter. In this paper, we investigate the feasibility of this methodology, and show the improvement achieved in the positioning accuracy.


  • Signal Strength
  • Access Point
  • Array Antenna
  • Location Estimation
  • Receive Signal Strength

Authors’ Affiliations

National Institute of Standard and Technology, Gaithersburg, MD 20899, USA
Department of Computer Science, Swiss Federal Institute of Technology, Zurich, Switzerland


  1. Hightower J, Borriello G: Location systems for ubiquitous computing. IEEE Computer Magazine 2001, 34(8):57–66.View ArticleGoogle Scholar
  2. Bahl P, Padmanabhan VN: RADAR: an in-building RF-based user location and tracking system. Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), March 2000, Tel Aviv, Israel 2: 775–784.Google Scholar
  3. Ladd AM, Bekris KE, Marceau G, Rudys A, Wallach DS, Kavraki LE: Using wireless ethernet for localization. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System (IROS '02), September-October 2002 1: 402–408.View ArticleGoogle Scholar
  4. Ladd AM, Bekris KE, Rudys AP, Wallach DS, Kavraki LE: On the feasibility of using wireless ethernet for indoor localization. IEEE Transactions on Robotics and Automation 2004, 20(3):555–559. 10.1109/TRA.2004.824948View ArticleGoogle Scholar
  5. Elnahrawy E, Li X, Martin RP: The limits of localization using signal strength: a comparative study. Proceedings of 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON '04), October 2004, Santa Clara, Calif, USA 406–414.Google Scholar
  6. Hashemi H: The indoor radio propagation channel. Proceedings of the IEEE 1993, 81(7):943–968. 10.1109/5.231342View ArticleGoogle Scholar
  7. Spencer QH, Jeffs BD, Jensen MA, Swindlehurst AL: Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel. IEEE Journal on Selected Areas in Communications 2000, 18(3):347–360. 10.1109/49.840194View ArticleGoogle Scholar
  8. Ertel RB, Cardieri P, Sowerby KW, Rappaport TS, Reed JH: Overview of spatial channel models for antenna array communication systems. IEEE Personal Communications 1998, 5(1):10–22. 10.1109/98.656151View ArticleGoogle Scholar
  9. Fortune SJ, Gay DM, Kernighan BW, Landron O, Valenzuela RA, Wright MH: WISE design of indoor wireless systems: practical computation and optimization. IEEE Computational Science and Engineering 1995, 2(1):58–68. 10.1109/99.372944View ArticleGoogle Scholar
  10. Valenzuela RA, Landron O, Jacobs DL: Estimating local mean signal strength of indoor multipath propagation. IEEE Transactions on Vehicular Technology 1997, 46(1):203–212. 10.1109/25.554753View ArticleGoogle Scholar
  11. Rubner Y, Tomasi C, Guibas LJ: A metric for distributions with applications to image databases. The 6th International Conference on Computer Vision, January 1998, Bombay, India 59–66.Google Scholar
  12. Edgar GA: Measure, Topology, and Fractal Geometry. Springer UTM, New York, NY, USA; 1995.MATHGoogle Scholar
  13. Agarwal PK, Sharir M: Efficient algorithms for geometric optimization. ACM Computing Surveys 1998, 30(4):412–458. 10.1145/299917.299918View ArticleGoogle Scholar
  14. Huttenlocher DP, Rucklidge WJ: A multi-resolution technique for comparing images using the Hausdorff distance. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '93), June 1993, New York, NY, USA 705–706.Google Scholar
  15. Rucklidge WJ: Efficiently locating objects using the Hausdorff distance. International Journal of Computer Vision 1997, 24(3):251–270. 10.1023/A:1007975324482View ArticleGoogle Scholar
  16. Kullback S, Leibler RA: On information and sufficiency. Annals of Mathematical Statistics 1951, 22: 79–86. 10.1214/aoms/1177729694MathSciNetView ArticleGoogle Scholar
  17. Stylianou Y, Syrdal AK: Perceptual and objective detection of discontinuities in concatenative speech synthesis. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USAGoogle Scholar
  18. Goldberger J, Gordon S, Greenspan H: An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures. Proceedings of 9th IEEE International Conference on Computer Vision, October 2003, Nice, France 1: 487–493.View ArticleGoogle Scholar
  19. Niculescu D, Nath B: VOR base stations for indoor 802.11 positioning. Proceedings of the 10th Annual International Conference on Mobile computing and networking (MobiCom '04), September–October 2004, Philadelphia, Pa, USA 58–69.View ArticleGoogle Scholar


© Sayrafian-Pour and Kaspar 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.