TY - JOUR

T1 - Determinant-Based Fast Greedy Sensor Selection Algorithm

AU - Saito, Yuji

AU - Nonomura, Taku

AU - Yamada, Keigo

AU - Nakai, Kumi

AU - Nagata, Takayuki

AU - Asai, Keisuke

AU - Sasaki, Yasuo

AU - Tsubakino, Daisuke

N1 - Funding Information:
This work was supported in part by the JST ACT-X under Grant JPMJAX20AD, and in part by the JST CREST, Japan, under Grant JPMJCR1763. The work of Taku Nonomura was supported by the JST Presto under Grant JPMJPR1678.
Publisher Copyright:
© 2013 IEEE.

PY - 2021

Y1 - 2021

N2 - In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.

AB - In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.

KW - Optimization

KW - greedy algorithms

KW - sparse sensor selection

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U2 - 10.1109/ACCESS.2021.3076186

DO - 10.1109/ACCESS.2021.3076186

M3 - Article

AN - SCOPUS:85105104095

VL - 9

SP - 68535

EP - 68551

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 9417233

ER -