A wireless LAN-based indoor positioning technology

  • Authors:

  • Z. Xiang;S. Song;J. Chen;H. Wang;J. Huang;X. Gao

  • Affiliations:

  • IBM Research Division, IBM China Research Laboratory, 2/F, Haohai Building, No. 7, 5th Street, Shangdi, Haidian District, Beijing 100085, People's Republic of China;IBM Research Division, IBM China Research Laboratory, 2/F, Haohai Building, No. 7, 5th Street, Shangdi, Haidian District, Beijing 100085, People's Republic of China;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4;IBM Research Division, IBM China Research Laboratory, 2/F, Haohai Building, No. 7, 5th Street, Shangdi, Haidian District, Beijing 100085, People's Republic of China;IBM Research Division, IBM China Research Laboratory, 2/F, Haohai Building, No. 7, 5th Street, Shangdi, Haidian District, Beijing 100085, People's Republic of China;IBM Research Division, IBM China Research Laboratory, 2/F, Haohai Building, No. 7, 5th Street, Shangdi, Haidian District, Beijing 100085, People's Republic of China

  • Venue:
  • IBM Journal of Research and Development
  • Year:
  • 2004

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Abstract

Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms.