半监督图表学习使用潜水源定位机会船谱
Jhon A Castro-Correa1, Mohsen Badiey1, Jhony H Giraldo2
1Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716, USA.
The Journal of the Acoustical Society of America
|September 5, 2025
概括
这项研究引入了一种新的图形学习方法,用于使用船舶噪声谱的水下来源定位. 该方法有效地捕获数据相关性,即使有有限的标记数据,也提高了准确性.
科学领域:
- 声信号处理
- 水下声学
- 机器学习用于本地化
背景情况:
- 传统的水下来源定位方法难以充分利用关键的数据相关性.
- 基于图形的方法可以在声学数据中捕捉空间关系.
- 对于声学定位的监督学习来说,有限的标记数据是一个重大挑战.
研究的目的:
- 开发一种新的图形学习模块,用于准确地定位水下源.
- 为了利用机会船的光谱图进行声学信号分析.
- 解决声学定位任务中有限的标记数据的挑战.
主要方法:
- 一种两步的方法,将预训练的卷积神经网络 (CNN) 结合为特征提取和图形神经网络 (GNN) 进行本地化.
- 自主监督学习用于CNN特征提取,半监督学习用于GNN培训.
- 在CNN从船舶噪声谱图中提取的特征上使用k-最近的邻居进行图形构造.
主要成果:
- 拟议的图形学习框架实现了与传统的监督学习模型相提并论的性能.
- 这种方法通过图形表示有效地利用数据相关性.
- 在合成和测量数据上展示了概括能力.
结论:
- 这种新型图形学习模块为水下源地定位提供了有效的解决方案,
- 整合CNN和GNN为声信号分析提供了一个强大的框架.
- 这种方法在水下声学监测和导航中具有前景.
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