# Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm

@article{Saito2019DataDrivenVS, title={Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm}, author={Yuji Saito and Taku Nonomura and Koki Nankai and Keigo Yamada and Keisuke Asai and Yasuo Sasaki and Daisuke Tsubakino}, journal={IEEE Sensors Letters}, year={2019}, volume={4}, pages={1-4} }

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

## 22 Citations

### Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

- Computer ScienceIEEE Transactions on Signal Processing
- 2022

The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint and can avoid the difficulty of sensor selection with strongly correlated measurement noise.

### Greedy Sensor Selection for Weighted Linear Least Squares Estimation Under Correlated Noise

- Computer ScienceIEEE Access
- 2022

The present study reveals that the objective function with correlated noise is neither submodular nor supermodular, and the effectiveness of the selection algorithm in terms of accuracy in the estimation of the states of large-dimensional measurement data is shown.

### Data-Driven Sparse Sensor Selection Based on A-Optimal Design of Experiment With ADMM

- Computer ScienceIEEE Sensors Journal
- 2021

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 that is better than the existing greedy and convex relaxation methods in terms ofThe-optimality criterion.

### Effect of Objective Function on Data-Driven Sparse Sensor Optimization

- Computer Science
- 2020

The results indicate that the greedy method based on D-optimality is the most suitable for high accurate reconstruction with low computational cost.

### Greedy Sensor Placement for Weighted Linear-Least Squares Estimation under Correlated Noise

- MathematicsArXiv
- 2021

A fast algorithm for greedy sensor selection is presented for a linear reducedordered reconstruction under the assumption of correlated noise on the sensor signals, which accomplishes the maximization of the determinant of the Fisher information matrix in the linear inverse problem.

### Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

- MathematicsIEEE Signal Processing Letters
- 2021

The randomized subspace Newton convex methods for the sensor selection problem are proposed and the customized method shows superior performance to the straightforward implementation in terms of the quality of sensors and the computational time.

### Randomized Group-Greedy Method for Data-Driven Sensor Selection

- Computer Science
- 2022

Randomized group-greedy methods for sensor selection problems are proposed and can provide better optimization results than those obtained by the original group-Greedy method when a similar computational cost is spent as for the original Groupgreedy method.

### Data-driven optimal sensor placement for high-dimensional system using annealing machine

- Computer ScienceMechanical Systems and Signal Processing
- 2023

### Proof-of-concept study of sparse processing particle image velocimetry for real time flow observation

- EngineeringExperiments in Fluids
- 2022

In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image velocimetry (PIV),…

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