Sparse target counting and localization in sensor networks based on compressive sensing

Bowu Zhang, Xiuzhen Cheng, Nan Zhang, Yong Cui, Yingshu Li, Qilian Liang

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

175 Scopus citations


In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

Original languageEnglish (US)
Title of host publication2011 Proceedings IEEE INFOCOM
Number of pages9
StatePublished - 2011
EventIEEE INFOCOM 2011 - Shanghai, China
Duration: Apr 10 2011Apr 15 2011

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X



All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


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