One-bit compressive sensing and source localization in wireless sensor networks

Yanning Shen, Jun Fang, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Additionally, in the proposed method, the sigmoid function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstructed signal. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function. This leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. The application of one-bit compressed sensing to source localization in wireless sensor networks is also discussed. Simulations are provided to illustrate the effectiveness of our proposed algorithm.

Original languageEnglish
Title of host publication2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
Pages379-383
Number of pages5
DOIs
StatePublished - 2013
Event2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, China
Duration: 6 Jul 201310 Jul 2013

Publication series

Name2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

Conference

Conference2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
Country/TerritoryChina
CityBeijing
Period6/07/1310/07/13

Keywords

  • Compressed sensing
  • one-bit quantization
  • source localization
  • surrogate function

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