在浅海中使用深度学习方法定位源,采用范围依赖的声音速度配置模型建模
1Key Laboratory of Underwater Acoustic Environment, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
JASA express letters
|August 8, 2025
概括
以模型为基础的源本地化深度学习与声音速度配置不匹配作斗争. 整合一个取决于范围的模型和转移学习提高了性能,即使在有限的浅水实验数据.
科学领域:
- 声学信号处理 声学信号处理
- 机器学习在海洋学中的应用.
背景情况:
- 基于模型的深度学习为源本地化中的有限训练数据提供了解决方案.
- 音速概况 (SSP) 的不匹配,特别是在带内波的浅水中,会降低性能.
研究的目的:
- 将范围依赖的SSP模型集成到深度学习中,以改进源本地化.
- 为了解决SSP在水下声学变化引起的性能恶化.
主要方法:
- 开发了一种深度学习方法,包括一个简单的范围依赖SSP模型.
- 在使用范围依赖SSP模型生成的模拟数据上训练网络.
- 应用转移学习使用有限的实验数据进行概括.
主要成果:
- 该网络在验证数据方面表现良好.
- 该模型在转移学习后有效地对实验测试数据进行了概括.
- 成功地缓解了由于SSP在浅水环境中的不匹配而导致的性能下降.
结论:
- 整合范围依赖的SSP模型可以增强源本地化深度学习.
- 转移学习是有效的适应模型,以现实世界的实验数据与有限的样本.
- 拟议的方法显示了强大的水下声源定位的前景.
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