  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.
