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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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
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Abstract	•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.
AbstractList	•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. 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.
ArticleNumber	107619
Author	Tsubakino, Daisuke Yamada, Keigo Asai, Keisuke Nankai, Koki Nonomura, Taku Saito, Yuji
Author_xml	– sequence: 1 givenname: Keigo surname: Yamada fullname: Yamada, Keigo email: keigo.yamada.t5@dc.tohoku.ac.jp organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 2 givenname: Yuji surname: Saito fullname: Saito, Yuji organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 3 givenname: Koki surname: Nankai fullname: Nankai, Koki organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 4 givenname: Taku surname: Nonomura fullname: Nonomura, Taku organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 5 givenname: Keisuke surname: Asai fullname: Asai, Keisuke organization: Department of Aerospace Engineering, Tohoku University, Sendai, Japan – sequence: 6 givenname: Daisuke surname: Tsubakino fullname: Tsubakino, Daisuke organization: Department of Aerospace Engineering, Nagoya University, Nagoya, Japan
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Snippet	•A sub-optimal sensor selection method was introduced for sensing high-dimensional, low-ranked phenomena.•Maximization on the determinant of the Fisher... A greedy algorithm is proposed for sparse-sensor selection in reduced-order sensing that contains correlated noise in measurement. The sensor selection is...
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SubjectTerms	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
Title	Fast greedy optimization of sensor selection in measurement with correlated noise
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