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

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.

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

References in corpus (3)

• Data-Driven Sparse Sensor Placement for Reconstruction
• A New Selection Operator for the Discrete Empirical Interpolation Method -- improved a priori error bound and extensions
• Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising

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