  A greedy algorithm is proposed for sparse-sensor selection in reduced-order
sensing that contains correlated noise in measurement. The sensor selection is
carried out by maximizing the determinant of the Fisher information matrix in a
Bayesian estimation operator.The Bayesian estimation with a covariance matrix
of the measurement noise and a prior probability distribution of estimating
parameters, which are given by the modal decomposition of high dimensional
data, robustly works even in the presence of the correlated noise. After
computational efficiency of the algorithm is improved by a low-rank
approximation of the noise covariance matrix, the proposed algorithms are
applied to various problems. The proposed method yields more accurate
reconstruction than the previously presented method with the determinant-based
greedy algorithm, with reasonable increase in computational time.
