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A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine
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Tian–qi PEI1, Cao–yang YU1, Lian LIAN1, 2
Journal of Ship Mechanics | 2025, 29(12) : 1838 - 1847
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Journal of Ship Mechanics | 2025, 29(12): 1838-1847
• Hydrodynamics •
A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine
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Tian–qi PEI1, Cao–yang YU1, Lian LIAN1, 2
Affiliations
  • 1.School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China
  • 2.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Published: 2025-12-15 doi: 10.3969/j.issn.1007-7294.2025.12.002
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Accurate motion prediction is crucial for the safe navigation of Autonomous Underwater Vehicles (AUVs). A fast Least Squares-Support Vector Machine (LS-SVM) motion prediction approach based on ridge regression algorithm is proposed in this paper. Firstly, the ridge regression analysis was incorporated into the traditional hydrodynamic model and the correlation analysis was conducted on the acceleration time-series input variables to identify the relatively important components. This step effectively reduces computational complexity while maintaining prediction accuracy. Subsequently, for the issue of high dimensionality and computational complexity in the LS-SVM algorithm's kernel function matrix, an improved Lagrange function was designed to eliminate redundant bias terms. This modification lightens the burden of calculating high-dimensional kernel matrices and further enhances the speed of maneuvering prediction. Finally, case studies based on the REMUS model demonstrate that the proposed strategy, compared to the standard LS-SVM prediction method that relies on traditional hydrodynamic models, reduces computational runtime by 29.8% while ensuring prediction accuracy.

Autonomous Underwater Vehicle  /  motion prediction  /  Support Vector Machine  /  ridge regression algorithm
Tian–qi PEI, Cao–yang YU, Lian LIAN. A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine[J]. Journal of Ship Mechanics, 2025 , 29 (12) : 1838 -1847 . DOI: 10.3969/j.issn.1007-7294.2025.12.002
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Year 2025 volume 29 Issue 12
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Article Info
doi: 10.3969/j.issn.1007-7294.2025.12.002
  • Receive Date：2024-07-17
  • Online Date：2026-07-07
  • Published：2025-12-15
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  • Received：2024-07-17
Affiliations
    1.School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China
    2.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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