Data-driven determinant-based greedy under/oversampling vector sensor placement

Research output: Contribution to journalArticlepeer-review

Abstract

A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches: a greedy method based on D-optimality and a noise-robust greedy method in this paper. Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement.

Original languageEnglish
Pages (from-to)1-30
Number of pages30
JournalCMES - Computer Modeling in Engineering and Sciences
Volume129
Issue number1
DOIs
Publication statusPublished - 2021

Keywords

  • Sparse sensor selection
  • Vector-sensor measurement

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Science Applications

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