基于复杂网络和图形卷积网络的水下滑翔机的运动状态识别
Wei Guo1, Xinlin Sun1, Dongmei Lv1
1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Chaos (Woodbury, N.Y.)
|February 11, 2024
概括
这项研究引入了一种新的方法,VGSA-GCN,以准确识别水下滑翔机运动状态. 该方法通过分析用于潜水和爬机动的传感器数据来提高可靠性.
科学领域:
- 海洋学和海洋技术
- 复杂系统和网络科学 复杂系统和网络科学
- 人工智能和机器学习
背景情况:
- 水下滑翔机 (UG) 对海洋观测至关重要,但识别它们的运动状态对于可靠性至关重要.
- 及时的运动状态识别有助于在UG中调整姿态和检测异常.
- 现有的方法可能缺乏复杂的水下环境所需的精度.
研究的目的:
- 开发一种新的方法来准确识别水下滑翔机的潜水和爬状态.
- 通过精确的运动状态识别来提高水下滑翔机的操作可靠性和效率.
主要方法:
- 提出了一个基于可见度图和自我注意机制的图形卷积网络 (VGSA-GCN).
- 使用有限可穿透可见度图 (LPVG) 方法在UG态度角度数据 (pitch, roll, heading) 上构建复杂的网络.
- 采用基于自我注意力机制的GCN框架来从构造的图表中分类运动状态.
主要成果:
- 在区分潜水和爬状态方面,VGSA-GCN方法显著优于支持矢量机器,卷积神经网络和标准GCN.
- 分析显示,在潜水状态下,传感器协调更紧密,更有效.
- 与升状态相比,潜水状态显示出更高的网络过渡系数.
结论:
- VGSA-GCN方法为水下滑翔机运动状态识别提供了一个高度准确的方法.
- 在潜水过程中加强传感器协调和网络效率是该模型确定的主要特征.
- 这项研究有助于使用自主滑翔机进行更可靠,更有效的水下勘探.
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