Determinant-based Fast Greedy Sensor Selection Algorithm

Abstract

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.

17 pages, 20 figures

References in corpus (5)

• Determinant-based Fast Greedy Sensor Selection Algorithm
• Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise
• 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

Cited by in corpus (16)

• Determinant-based Fast Greedy Sensor Selection Algorithm
• Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise
• Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
• Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points
• Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
• Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
• 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
• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
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• Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
• 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
• Triple decomposition and sparse representation for noisy pressure-sensitive paint data