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Efficient Sensor Node Selection for Observability Gramian Optimization
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DOI：10.3390/s23135961.png)
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摘要
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Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.
Keyword:
monitoring
state estimation
scheduling
optimization
sensor networks
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期刊
IF:
3.5
论文数:
7.3W
被引数:
23.4W
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引用论文
Greedy Sensor Selection for Weighted Linear Least Squares Estimation Under Correlated Noise相关噪声下加权线性最小二乘估计的贪婪传感器选择.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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