Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm

Yuji Saito; Taku Nonomura; Koki Nankai; Keigo Yamada; Keisuke Asai; Yasuo Sasaki; Daisuke Tsubakino

doi:10.1109/LSENS.2020.2999186

Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm

Signal Processing 2020-07-01 v3 Data Analysis, Statistics and Probability Fluid Dynamics

Authors: Yuji Saito , Taku Nonomura , Koki Nankai , Keigo Yamada , Keisuke Asai , Yasuo Sasaki , Daisuke Tsubakino

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Abstract

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

Keywords

parameter estimation matrix optimization and algorithms

Cite

@article{arxiv.1906.00778,
  title  = {Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm},
  author = {Yuji Saito and Taku Nonomura and Koki Nankai and Keigo Yamada and Keisuke Asai and Yasuo Sasaki and Daisuke Tsubakino},
  journal= {arXiv preprint arXiv:1906.00778},
  year   = {2020}
}

Comments

This article mistakenly appeared as a replacement for the other article (arXiv:1911.08757v3)

Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm

Abstract

Keywords

Cite

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