Distributed sensor network localization with inaccurate anchor positions and noisy distance information

Seshan Srirangarajan, Ahmed H. Tewfik, Zhi Quan Luo

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

21 Scopus citations

Abstract

The goal of the sensor network localization problem is to determine positions of all the sensor nodes in a network given certain pairwise noisy distance measurements and inaccurate anchor node positions. A two-step distributed localization approach based on second-order cone programming (SOCP) relaxation is presented. In the first step, the sensor nodes determine their positions based on local information and in the second step, the anchor nodes refine their positions using information from the neighboring nodes. Our numerical study shows that the sensor and anchor positions cannot be estimated in a single step; the sensors must be estimated first for the results to converge. The second step enables anchors which are in the convex hull of their neighbors to refine their positions. Extensive simulation results with inaccurate anchor positions and noisy distance measurements are presented. These illustrate the robustness of the algorithm and the performance gains achievable in terms of problem size reduction, computational efficiency and localization accuracy.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIII521-III524
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Convex optimization
  • Distributed algorithms
  • Localization
  • Positioning
  • Relaxation methods

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