Information-based clustering and filtering for field reconstruction

Jia Chen, Akshay Malhotra, Ioannis D. Schizas

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

Abstract

A novel communication efficient scheme for field reconstruction is put forth. The proposed framework entails two steps. During the first step sparsity-inducing canonical correlation is utilized to determine different clusters of correlated sensors. The second step relies on least mean-squares adaptive filters to learn, at a cluster head sensor, the data model of all other cluster sensors. The cluster heads send their data and model parameters to a fusion center, which can use them to recover all sensor measurements without the need to talk to all of them. This way the communication cost can be significantly reduced. Numerical tests demonstrate the capability of the proposed scheme in field recovery.

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
Pages576-580
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
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

Keywords

  • Canonical correlation analysis
  • field reconstruction
  • least mean-squares adaptive filter
  • sparsity

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