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一个多任务学习深层复杂的网络,用于浅水源,范围在复杂的环境中.

Qianqian Li1,2,3, Zhihao Juan1, Zhenglin Li1,2,3

  • 1College of Ocean Engineering and Technology, Sun Yat-sen University, Zhuhai 510275, China.

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概括
此摘要是机器生成的。

本研究介绍了一种多任务学习深层复杂网络 (MTL-DCN),用于准确的水下声源范围. MTL-DCN有效地解决了由内部孤独波 (ISW) 引起的环境不匹配问题,比现有方法提高了性能.

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科学领域:

  • 海洋声学 海洋声学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 浅水中的内部单一波 (ISW) 导致环境不匹配,降低了水下声波范围的性能.
  • 声音速度配置 (SSP) 的不匹配是单任务学习深度复杂网络 (STL-DCN) 和传统匹配场处理 (CMFP) 的性能退化的主要因素.

研究的目的:

  • 开发一种先进的测距算法,能够减轻浅水声学中的环境不匹配.
  • 提高水下声源定位在受到ISWs影响的动态环境中的准确性.

主要方法:

  • 提出了一个多任务学习深度复杂网络 (MTL-DCN),集成了一个自适应加权多任务学习机制.
  • 该MTL-DCN同时估计水下声源范围和声速谱 (SSP) 沿传播路径.
  • 该算法使用模拟分析和来自南中国海的实验数据进行了验证.

主要成果:

  • 模拟分析证实SSP不匹配是降低范围性能的关键因素.
  • 与STL-DCN和CMFP相比,MTL-DCN展示了复杂的声学数据与相位关系的优越表示和处理.
  • 实验结果显示,使用MTL-DCN显著提高了范围估计的准确性.

结论:

  • 拟议的MTL-DCN算法可靠地估计了由ISWs影响的复杂,波动的环境中的水下声源范围.
  • 适应式加权多任务学习方法有效地解决了环境不匹配问题,为浅水声学提供了强大的解决方案.