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一种深度学习模型,用于检测流域声学断层扫描系统中弱水下信号的到达时间
Weicong Zheng1, Xiaojian Yu2, Xuming Peng3,4,5
1School of Information, Xiamen University, Xiamen 361102, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一个新的深度学习模型,DCA-Net,在具有挑战性的水下条件下准确地检测声信号到达时间. 这一进步提高了流域声学断层扫描 (FAT) 系统的性能,即使信号噪声比 (SNR) 非常低.
科学领域:
- 声学 声学 在声学方面
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 流体声学断层扫描 (FAT) 系统需要精确的信号到达时间检测来计算参数.
- 传统的匹配过方法在复杂的水下环境中扎,其信号噪声比率 (SNR) 较低.
研究的目的:
- 开发一种先进的方法,在低SNR环境中精确检测声信号到达时间.
- 为了提高流体声学断层扫描 (FAT) 系统的性能.
主要方法:
- 提出了一个双通道深度学习模型,DCA-Net.
- 设计了一个交互式模块,通过传输辅助信息来增强特征提取.
- 整合了注意力模块,使选择性关注能够集中在关键信号特征上.
主要成果:
- 与传统匹配过方法相比,DCA-Net表现出更高的性能.
- 该模型在检测低SNR水平 (-10, -15, -20dB) 的声信号方面表现优于其他深度神经网络.
- 实验使用了从现场收集的声学断层扫描信号中合成的低SNR数据.
结论:
- 拟议的DCA-Net有效地解决了在低SNR场景中传统方法的局限性.
- 在具有挑战性的水环境中,DCA-Net为声信号到达时间检测提供了强大的解决方案.
- 这种深度学习方法显著提升了河流声学断层扫描的能力.
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