海洋工程装备与技术

海洋工程装备与技术 ›› 2025, Vol. 12 ›› Issue (1): 133-140.doi: 10.12087/oeet.2095-7297.2025.01.18

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基于级联滤波与误差触发支持向量回归的海洋航行器运动预测研究

钟一鸣1，于曹阳1，2*，向先波2，3，连琏1，4   

  1. 1.上海交通大学海洋学院，上海 200030；2.华中科技大学水下无人运载平台及作业技术湖北省重点实验室，湖北 武汉 430074；3.华中科技大学船舶与海洋工程学院，湖北 武汉 430074；4.上海交通大学海洋工程全国重点实验室，上海 200240
  • 出版日期:2025-03-05 发布日期:2025-06-05
  • 通讯作者: 于曹阳 E-mail:eduzhongym@sjtu.edu.cn
  • 作者简介:钟一鸣（1997— ），男，博士研究生，主要从事海洋航行器系统辨识及运动控制研究。
  • 基金资助:
    国家自然科学基金（42376187）；水下无人运载平台及作业技术湖北省重点实验室开放课题（CHK202403）

Motion Prediction of Marine Vehicles Based on Cascaded Filter and Error-Triggered Support Vector Regression

ZHONG Yiming1， YU Caoyang1，2*， XIANG Xianbo2，3， LIAN Lian1，4   

  1. 1.School of Oceanography， Shanghai Jiao Tong University， Shanghai 200030， China; 2.Hubei Provincial Key Laboratory of Unmanned Underwater Vehicle and Manipulating Technology， Huazhong University of Science and Technology， Wuhan 430074， Hubei， China; 3.School of Naval Architecture and Ocean Engineering， Huazhong University of Science and Technology， Wuhan 430074， Hubei， China; 4.State Key Laboratory of Ocean Engineering， Shanghai Jiao Tong University， Shanghai 200240， China
  • Online:2025-03-05 Published:2025-06-05

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摘要： 为应对复杂多变的未知环境对海洋航行器运动预测所造成的挑战，提出了一种融合级联滤波与误差触发支持向量回归（error-triggered support vector regression， ETSVR）的智能预测系统。首先，该系统基于移动平均滤波对原始数据进行预处理，以剔除异常值并抑制高频噪声，为后续预测提供高质量的数据集；其次，引入二阶扩展卡尔曼滤波对系统状态进行精确估计，进一步增强数据的平稳度和可靠性；最后，设计ETSVR算法对处理后的高质量数据集进行学习，以构建海洋航行器的运动预测模型，实现精准运动预测，并借助误差触发机制提升系统的实时性与计算效率。基于湖试数据的实验结果表明，所提出的智能运动预测系统在多项误差指标上均显著优于传统的线性回归算法。例如，在侧向速度预测中，均方误差较线性回归算法降低约53.2%；在转艏角速度预测中，最大误差减少了约58.2%。这些结果表明，提出的级联滤波与ETSVR算法相结合的智能预测系统，能够显著提升海洋航行器在复杂未知环境中的运动预测精度，具有较好的应用前景和重要的研究意义。

关键词: 海洋航行器, 运动预测, 移动平均滤波, 二阶扩展卡尔曼滤波, 误差触发支持向量回归

Abstract: To tackle the challenges that complex and dynamic environments impose on motion prediction of marine vehicles， an intelligent prediction system integrating cascaded filter with error-triggered support vector regression （ETSVR） is proposed in this paper. Firstly， the system employs a moving average filter for preprocessing raw data， effectively eliminating outliers and high-frequency noise to establish a high-quality dataset for the following prediction. Subsequently， a second-order extended Kalman filter is incorporated for precise state estimation， further enhancing the data stability and reliability. Finally， the ETSVR algorithm works on the processed high-quality dataset to build an accurate motion prediction model for marine vehicles， with an error-triggered mechanism designed to improve the system's real-time performance and computational efficiency. Experimental results based on lake tests demonstrate that the proposed intelligent motion prediction system significantly outperforms traditional linear regression （LR） across various error metrics. For instance， in lateral velocity prediction， the mean squared error is reduced by approximately 53.2% compared to LR， while in yaw rate prediction， the maximum error decreases by around 58.2%. These results indicate that the proposed cascaded filter and ETSVR-based prediction system substantially improves the accuracy of motion prediction in complex unknown environments， highlighting its promising potential and significance.

Key words: marine vehicle, motion prediction, moving average filtering, second-order extended Kalman filter, error-triggered support vector regression

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