DL-SDE:一个深度学习框架,用于在浅水中使用垂直线性阵列进行源深度估计
Zhen Li1,2,3, Shengchun Piao1,2,3, Jiankang Zhan1,2,3
1Country National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
The Journal of the Acoustical Society of America
|October 28, 2025
概括
一个新的深度学习框架通过分析声干扰模式,准确地估计了水下源的深度. 这种方法比传统技术提供了更好的稳定性和性能,增强了声学定位能力.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器学习是机器学习.
背景情况:
- 准确的源深度估计至关重要,但在水下声学方面具有挑战性.
- 垂直线阵列跨光谱密度矩阵中的干扰模式包含深度敏感信息.
- 像匹配场处理 (MFP) 这样的现有方法可能对环境不匹配很敏感.
研究的目的:
- 开发一个强大而准确的基于深度学习的源深度估计 (DL-SDE) 框架.
- 为了提高深度估计,利用多规模的本地和非均的全球干扰模式.
- 为了证明框架的优越性比传统的方法,如MFP.
主要方法:
- 提出了一个基于深度学习的源深度估计 (DL-SDE) 框架.
- 集成了一个多尺度卷积模块来捕获局部干扰模式.
- 整合了一个剩余的多头自我注意模块来建模全球干扰关系.
主要成果:
- 对于环境不匹配,DL-SDE表现出比MFP更强大的稳定性.
- 在100Hz以上的频率和覆盖至少50%水柱的阵列深度下观察到稳定的性能.
- 萨克兰特1993年实验显示,平均绝对误差减少了11.63m,可信本地化概率比MFP增加了71%.
结论:
- 拟议的DL-SDE框架有效地利用物理引导的组件从多尺度干扰模式中学习.
- DL-SDE为水下源深度估计提供了强大而准确的解决方案.
- 该框架在具有挑战性的声学环境中明显优于传统的MFP.
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