Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm
arXiv:1906.00778 · doi:10.1109/LSENS.2020.2999186
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.
This article mistakenly appeared as a replacement for the other article (arXiv:1911.08757v3)