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ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Solutions
Volume 11 - 2024 |
doi: 10.3389/fmars.2024.1467519
This article is part of the Research Topic Data-Driven Ocean Environmental Perception with its Applications View all articles
Robust Sensor Selection based on Maximum Correntropy Criterion for Ocean Data Reconstruction
Provisionally accepted- Shanghai Maritime University, pudong, China
Selecting an optimal subset of sensors that can accurately reconstruct the full state of the ocean can reduce the cost of the monitoring system and improve monitoring efficiency. Typically, in data-driven sensor selection processes, the use of Euclidean distance to evaluate reconstruction error is susceptible to non-Gaussian noise and outliers present in ocean data. This paper proposes a Robust Sensor Selection (RSS) evaluation model based on the Maximum Correntropy Criterion (MCC) through subspace learning, enabling the selection of robust sensor measurement subsets and comprehensive data reconstruction. To more accurately quantify the impact of varying noise magnitudes, noise weights were incorporated into the model's objective function. Additionally, the local geometric structure of data samples is utilized to further enhance reconstruction accuracy through the selected sensors. Subsequently, the MCC_RSS algorithm is proposed, which employs the Block Coordinate Update (BCU) method to achieve the optimal solution for the proposed model. Experiments conducted using ocean temperature and salinity datasets validate the proposed MCC_RSS algorithm. The results demonstrate that the sensor selection method proposed in this paper exhibits strong robustness, outperforming comparative methods under varying proportions of outliers and non-Gaussian noise.
Keywords: Sensor selection1, Maximum Correntropy Criterion (MCC)2, robust3, data reconstruction4, ocean5, subspace learning6
Received: 20 Jul 2024; Accepted: 10 Sep 2024.
Copyright: © 2024 Zhang, Wu, Liang, Mei and Xian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Huafeng Wu, Shanghai Maritime University, pudong, China
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