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

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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• Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
• Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
• Nondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments
• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
• Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model
• Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
• Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
• Fast Data-driven Greedy Sensor Selection for Ridge Regression