Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks

Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Sijia Liu, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations

Abstract

We study the problem of joint sparsity pattern recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but unknown sparsity pattern. Each sensor quantizes its measurement vector element-wise to 1-bit and transmits the quantized observations to a fusion center. We develop a computationally tractable support recovery algorithm which minimizes a cost function defined in terms of the likelihood function and the ℓ1,∞ norm. We observe that even with noisy 1-bit measurements, joint sparsity pattern can be recovered accurately with multiple sensors each collecting only a small number of measurements.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1472-1476
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove
Period11/8/1511/11/15

Keywords

  • Compressed sensing
  • maximum-likelihood estimation
  • quantization
  • support recovery

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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