An Efficient dictionary refinement algorithm for multiple target counting and localization in wireless sensor networks

Baoming Sun, Yan Guo*, Gengfa Fang, Eryk Dutkiewicz

*Corresponding author for this work

Research output: Contribution to journalArticle

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Abstract

Many applications provided by wireless sensor networks rely heavily on the location information of the monitored targets. Since the number of targets in the region of interest is limited, localization benefits from compressive sensing, sampling number can be greatly reduced. Despite many compressive sensing–based localization methods proposed, existing solutions are based on the assumption that all targets fall on a sampled and fixed grid, performing poorly when there are targets deviating from the grid. To address such a problem, in this article, we propose a dictionary refinement algorithm where the grid is iteratively adjusted to alleviate the deviation. In each iteration, the representation coefficient and the grid parameters are updated in turn. After several iterations, the measurements can be sparsely represented by the representation coefficient which indicates the number and locations of multiple targets. Extensive simulation results show that the proposed dictionary refinement algorithm achieves more accurate counting and localization compared to the state-of-the-art compressive sensing reconstruction algorithms.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalInternational Journal of Distributed Sensor Networks
Volume13
Issue number8
DOIs
Publication statusPublished - 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • compressive sensing
  • counting
  • dictionary refinement
  • localization
  • wireless sensor networks

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