Improving anchor position accuracy for 3-D localization in wireless sensor networks

Kegen Yu*, Y. Jay Guo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)

Abstract

Accuracy of ordinary sensor node localization in wireless sensor networks mainly depends on the signal parameter such as time-of-arrival and signal strength estimation errors and the accuracy of the anchor node locations. In this paper a low-complexity but efficient algorithm is derived to improve anchor location accuracy in the presence of both anchor-to-anchor distance and AOA estimates and GPS measurements. Also, a Lenvenberg-Marquardt (LM) optimization based algorithm is developed for accuracy improvement when anchor-to-anchor distance estimates and GPS measurements are provided. Further, we derive the Cramer-Rao lower bound (CRLB) to benchmark the anchor position accuracy. To our knowledge, improving anchor node location accuracy and deriving the CRLB in the presence of both GPS and anchor-to-anchor measurements in 3-D scenarios are not reported in the literature. Simulation results demonstrate that the proposed approaches can improve the anchor position accuracy substantially and that the accuracy of the two developed algorithms approaches the corresponding CRLB.

Original languageEnglish
Title of host publicationICC 2008 - IEEE International Conference on Communications, Proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages951-955
Number of pages5
ISBN (Electronic)9781424420759
ISBN (Print)9781424420742
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventIEEE International Conference on Communications, ICC 2008 - Beijing, China
Duration: 19 May 200823 May 2008

Other

OtherIEEE International Conference on Communications, ICC 2008
CountryChina
CityBeijing
Period19/05/0823/05/08

Keywords

  • 3-D localization
  • Anchor location accuracy improvement
  • Cramer-Rao lower bound
  • Least squares estimator
  • Lenvenberg-Marquardt method

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