Sensor selection via compressed sensing

Avishy Carmi, Pini Gurfil

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

Abstract

Sensor selection is an NP-hard problem involving the selection of S out of N sensors such that optimal filtering performance is attained. We present a novel approach for sensor selection that utilizes a heuristic measure quantifying the incoherence of the vector space spanned by the sensors with respect to the system's principal directions. This approach facilitates the formulation of a convex relaxation problem that can be efficiently modeled and solved using compressed sensing (CS) algorithms. We subsequently develop a new CS algorithm based on subgradient projections. The new CS algorithm for sensor selection is shown to outperform existing methods in a number of applications.

Original languageEnglish
Title of host publicationProceedings of the 18th IFAC World Congress
PublisherIFAC Secretariat
Pages7785-7790
Number of pages6
Edition1 PART 1
ISBN (Print)9783902661937
DOIs
StatePublished - 1 Jan 2011
Externally publishedYes

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume44
ISSN (Print)1474-6670

Keywords

  • Compressed sensing
  • Estimability
  • Sensor networks
  • Sensor selection
  • Subgradient projection methods

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