A data-driven based hybrid multi-branch framework for AUV navigation

Xin Zhang

doi:10.1016/j.oceaneng.2025.120675

2025-04-01

PRE

张昕 (Xin Zhang)

He, Bo

Y. P. Lu

DOI：10.1016/j.oceaneng.2025.120675

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在Autonomous Underwater Vehicle (AUV)导航任务中，针对传统状态估计技术引入多重误差影响导航精度的问题，并考虑不同导航传感器参数的独特性，本文提出了一种用于高精度AUV导航的混合多分支网络框架，该框架能够在提取局部特征的同时捕捉输入序列的长时序依赖关系。首先，每个输入时间序列通过基于One Dimensional-Convolutional Neural Network (1D-CNN)的特征提取模块进行单独处理，以提供具有不同参数的特征表示。随后，提取的特征被拼接以获得新的时间序列，并输入到基于Long Short-Term Memory (LSTM)的特征融合模块中，以学习序列中的长时序依赖关系。最后，通过执行全连接（FC）层的回归计算可获得网络输出。采用Sailfish 210 AUV实际海试数据验证了所提算法的有效性。

Keyword:

Autonomous underwater vehicle

Navigation and localization

Extended Kalman filter

State estimation

Sequential learning

期刊

Ocean Engineering

IF:

5.5

论文数:

7.2K

被引数:

7.6W

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