模式告知复杂值神经过程用于匹配的场处理
Yining Liu1, Wei Gao2, Desheng Chen2
1School of Ocean Engineering and Technology, Sun Yat-sen University, Zhuhai 528478, People's Republic of China.
The Journal of the Acoustical Society of America
|January 23, 2025
概括
这项研究引入了一种新的神经过程方法,用于在海洋波导中重建声场. 该方法提高了被动源定位的准确性和稳定性,优于传统方法.
科学领域:
- 海洋声学 海洋声学
- 信号处理 信号处理
- 机器学习是机器学习.
背景情况:
- 准确的声场重建对于水下被动源定位至关重要.
- 传统方法面临着稀疏数据和计算效率的挑战.
研究的目的:
- 开发一种使用复杂值神经过程和模态深度函数 (MDFs) 来重建声场的先进方法.
- 提高在海洋环境中被动源定位的准确性和稳定性.
主要方法:
- 使用复杂值的神经网络,用高斯过程建模声场分布.
- 采用超学习策略进行参数优化,以防止过拟合和加速重建.
- 整合重建的声场与匹配的场处理 (MFP) 进行本地化.
主要成果:
- 拟议的方法显著提高了SWellEx-96波导上的本地化性能.
- 基于MDF的内核在表征海洋波导方面表现出高斯内核的优越性能.
- 该方法有效地拒绝,插入稀疏数据,并为虚拟数组生成密集的场数据.
结论:
- 使用MDF的复杂值神经过程方法提供了增强的声场预测和强大的被动源定位.
- 这种方法解决了传统的MFP和基于GP的MFP的局限性,特别是在复杂的海洋环境中.
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