Sparsity-aware field estimation via ordinary Kriging

Sijia Liu, Engin Masazade, Makan Fardad, Pramod K. Varshney

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

18 Scopus citations

Abstract

In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights, we propose a sparsity-promoting ordinary Kriging approach where we minimize the Kriging error variance while penalizing the number of nonzero Kriging weights. This yields a combinatorial optimization problem, which is intractable in general. To solve the proposed non-convex optimization problem, we employ the alternating direction method of multipliers (ADMM) and the reweighted ℓ1 minimization method, respectively. Numerical results are provided to illustrate the effectiveness of our proposed approaches that provide a balance between the estimation accuracy and the number of selected sensors.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3948-3952
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Field estimation
  • alternating direction method of multipliers
  • convex optimization
  • sensor networks
  • sparsity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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