Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise

Abstract

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.

Presented in APS-DFD 2019 Accepted for publication at 3 Jan 2021

References in corpus (4)

• Determinant-based Fast Greedy Sensor Selection Algorithm
• Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
• Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
• Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

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• Determinant-based Fast Greedy Sensor Selection Algorithm
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• Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
• Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem
• 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
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• 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