Fast greedy optimization of sensor selection in measurement with correlated noise

•A sub-optimal sensor selection method was introduced for sensing high-dimensional, low-ranked phenomena.•Maximization on the determinant of the Fisher Information matrix was conducted.•Data-driven modeling of the correlated measurement noise was developed.•Mode information was utilized for efficien...

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Bibliographic Details
Published in:Mechanical systems and signal processing Vol. 158; p. 107619
Main Authors: Yamada, Keigo, Saito, Yuji, Nankai, Koki, Nonomura, Taku, Asai, Keisuke, Tsubakino, Daisuke
Format: Journal Article
Language:English
Published: Berlin Elsevier Ltd 01.09.2021
Elsevier BV
Subjects:
Algorithms
Bayesian analysis
Bayesian state estimation
Computational efficiency
Computing time
Conditional probability
Covariance matrix
Data processing
Fisher information
Greedy algorithm
Greedy algorithms
Noise
Noise measurement
Optimization
Parameter estimation
Sensor placement optimization
Sensors
Sensor placement optimization
37M05
Greedy algorithm
Data processing
90C27
62F15
Bayesian state estimation
ISSN:0888-3270, 1096-1216
Online Access:Get full text
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Summary:•A sub-optimal sensor selection method was introduced for sensing high-dimensional, low-ranked phenomena.•Maximization on the determinant of the Fisher Information matrix was conducted.•Data-driven modeling of the correlated measurement noise was developed.•Mode information was utilized for efficient algorithm computation. 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.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2021.107619