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arXiv · 2010.09329

Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM

Takayuki Nagata·Taku Nonomura·Kumi Nakai·Keigo Yamada·Yuji Saito·Shunsuke Ono
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Abstract

The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm. The performance of the proposed method was evaluated with a random sensor problem and compared with the previously proposed methods such as the greedy method and the convex relaxation. The performance of the proposed method is better than an existing method in terms of the A-optimality criterion. In addition, the proposed method requires longer computational time than the greedy method but it is quite shorter than the convex relaxation in large-scale problems. The proposed method was applied to the data-driven sparse-sensor-selection problem. A data set adopted is the NOAA OISST V2 mean sea surface temperature set. At the number of sensors larger than that of the latent state variables, the proposed method showed similar and better performances compared with previously proposed methods in terms of the A-optimality criterion and reconstruction error.

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Takayuki Nagata, Taku Nonomura, Kumi Nakai, Keigo Yamada, Yuji Saito, Shunsuke Ono. 2020-10-19. Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM. https://doi.org/10.1109/jsen.2021.3073978

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