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An indoor tracking algorithm with the virtual reference based positioning

Published: 22 March 2010 Publication History

Abstract

The widespread popularization of ubiquitous devices promotes fervent demands for location-based services (LBS). This paper technically proposes a methodology named virtual reference based positioning (VRBP) for the location positioning, which models the RFID positioning systems with virtual reference tags and constructs such information with a classical radio propagation model in order to enhance positioning accuracy. Instead of deploying a high density of RFID tags, the notion of VRBP decreases the possibility of signal interference. Furthermore, we propose the VT-LANDMARC, a tracking algorithm which applies the VRBP and manifests a superior performance over the original LANDMARC in positioning accuracy. Besides these advantages, with the help of the VRBP, the VT-LANDMARC ameliorates the defect in original LANDMARC that the location error may increase significantly in the margin area. And the VRBP methodology offers a practical solution for the location estimation with a scarce density of reference tags.

References

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Borriello, G. A Survey and Taxonomy of Location Systems for Ubiquitous Computing IEEE Computer, 2001, 57--66.
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Deng, P. and Fan, P. Z., An AOA assisted TOA positioning system. in International Conference on Communication Technology, 2000 (WCC-ICCT 2000), (2000), 1501--1504 vol. 1502.
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Guang-yao, J., Xiao-yi, L. and Myong-Soon, P., An indoor localization mechanism using active RFID tag. in IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006 (SUTC 2006), (2006), 40--43.
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      cover image ACM Conferences
      SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
      March 2010
      2712 pages
      ISBN:9781605586397
      DOI:10.1145/1774088
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      Published: 22 March 2010

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      Author Tags

      1. VRBP
      2. VT-LANDMARC
      3. tracking algorithm
      4. virtual reference tags

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      March 22 - 26, 2010
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      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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