Regional Ocean Current Field Construction based on an Empirical Bayesian Kriging Algorithm using Multiple Underwater Gliders

Zhang, Runfeng; Yang, Shaoqiong; Wang, Yanhui; Yin, Yongguang

doi:10.2112/si99-006.1

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…In this section, based on Lyapunov stability, a MIMO controller is designed for nonlinear control system and dual motion modes to realize dual-motion simulation of DUV. The state variables and control variables in a six-degrees-of-freedom equation of the vehicle are rewritten as Equations ( 26) and (27).…”

Section: Controller Designmentioning

confidence: 99%

“…Two DUVs collected 55 data profiles, covering seawater depths of 600 m to 1000 m during a period of 7 days. Based on the temperature and salinity data collected by DUVs (the temperature and salinity data collected by DUVs are shown in Figure 12), a three-dimensional temperature field and salinity field was constructed using the spatio-temporal Kriging method [27,28]. As shown in Figure 12a,b, two argo missions at a depth of 1100 m were performed by DUV1 first, and then glider missions at a depth of 1000 m were executed.…”

Section: The Network Sea Test With Two Duvsmentioning

confidence: 99%

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Modeling, Control and Experiments of a Novel Underwater Vehicle with Dual Operating Modes for Oceanographic Observation

Cao

Yao

et al. 2022

JMSE

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Multifarious and continuous oceanographic observation data is of considerable significance to oceanographic research and military application. In this paper, a novel oceanographic observation method with dual-modal underwater vehicle able to switch between the Argo and Glider modes is proposed and is studied experimentally in the South China Sea. Using the Newton–Euler formulation, a complete dynamic model considering the variation in gravity/buoyancy due to mode switching is thus established. Then, based on Lyapunov stability criterion, a nonlinear adaptive inverse controller with multiple inputs and multiple outputs is developed which enables the observation platform to freely switch between various modes. By analyzing the numerical and experimental data of a single mode, the results demonstrate that the proposed method can supplement the observation data and improve the accuracy of oceanographic data. Finally, based on experimental data acquired by the dual-modal underwater vehicle in the South China Sea, three-dimensional regional oceanic temperature and salinity fields are constructed with the spatio-temporal Kriging method, and feature surfaces are extracted from the oceanic fields. Furthermore, the proposed method can be applied in other stirring dynamic regions, such as ocean eddies, ocean fronts, filaments, etc.

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Section: Controller Designmentioning

confidence: 99%

Section: The Network Sea Test With Two Duvsmentioning

confidence: 99%

Modeling, Control and Experiments of a Novel Underwater Vehicle with Dual Operating Modes for Oceanographic Observation

Cao

Yao

et al. 2022

JMSE

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show abstract

“…Commonly used interpolation methods for SST include Kriging, inverse distance weighting, spline interpolation, and Lagrange interpolation, which generally fulfill most requirements. Yet, under extremely sparse sample conditions, their accuracy significantly diminishes [4][5][6]. For modern neural networks, larger datasets often enhance model performance and generalization capabilities.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

Wang,

Shi,

et al. 2024

J. Phys.: Conf. Ser.

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Addressing the issue of data sparsity and gaps caused by missing values, this study employs an information diffusion approach to effectively spread information from sparse sample points to monitoring locations. By thoroughly extracting insights from a limited dataset, it achieves more precise interpolation outcomes. To validate the superiority of the information diffusion interpolation technique under conditions of sparse samples, we utilize sea surface temperature (SST) data from the offshore waters of China as a case study. We compare three interpolation methods: Kriging, Gaussian information diffusion, and asymmetric information diffusion. The calculations and comparisons of interpolation results are conducted across varying sample sizes. The findings indicate that in situations with relatively sparse samples, asymmetric information diffusion yields the most favorable results, with Kriging and Gaussian diffusion exhibiting comparable performance. In cases of extremely sparse samples, asymmetric information diffusion yields the lowest interpolation error, followed by Gaussian diffusion, while Kriging performs the least effectively. Thus, when confronted with sample sparsity, the application of the information diffusion interpolation method can yield notably improved results.

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Three-dimensional regional oceanic element field reconstruction with multiple underwater gliders in the Northern South China Sea

Zhang

Yang

Wang

et al. 2020

Applied Ocean Research

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Regional Ocean Current Field Construction based on an Empirical Bayesian Kriging Algorithm using Multiple Underwater Gliders

Cited by 6 publications

References 16 publications

Modeling, Control and Experiments of a Novel Underwater Vehicle with Dual Operating Modes for Oceanographic Observation

Modeling, Control and Experiments of a Novel Underwater Vehicle with Dual Operating Modes for Oceanographic Observation

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

Three-dimensional regional oceanic element field reconstruction with multiple underwater gliders in the Northern South China Sea

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