An improved compressive sensing and received signal strength-based target localization algorithm with unknown target population for wireless local area networks

Jun Yan, Kegen Yu, Ruizhi Chen, Liang Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Citations (Scopus)
29 Downloads (Pure)


In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)- based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared.

Original languageEnglish
Article number1246
Pages (from-to)1-18
Number of pages18
JournalSensors (Switzerland)
Issue number6
Publication statusPublished - Jun 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.


  • Compressive sensing
  • Positioning
  • Received signal strength
  • Target population
  • Wireless local area network

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