Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm

@article{Saito2019DataDrivenVS,
  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={IEEE Sensors Letters},
  year={2019},
  volume={4},
  pages={1-4}
}
A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. 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. 

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