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贝叶斯优化-调整机器学习用于水下声学目标定位.

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  • 1College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, People's Republic of China.

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概括
此摘要是机器生成的。

这项研究介绍了一种贝叶斯优化调整的机器学习方法,用于水下声学目标定位 (UATL),优于传统方法. 新方法在定位水下目标方面实现了高精度,即使在环境噪音下也是如此.

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科学领域:

  • 海洋学 海洋学 海洋学
  • 声学 声学 在声学方面
  • 机器学习 机器学习

背景情况:

  • 水下声学目标定位 (UATL) 面临挑战,因为环境噪音和处理效率低下,与现有方法如匹配场处理 (MFP) 面临挑战.
  • 准确和实时的UATL对于各种应用程序至关重要,但仍然难以实现.

研究的目的:

  • 开发和评估一种新的贝叶斯优化调整机器学习方法,用于增强UATL.
  • 将这种新方法的性能与传统的MFP和其他具有不同超参数调整策略的机器学习方法进行比较.

主要方法:

  • 使用KRAKEN传播代码为特定的浅海环境生成模拟训练数据.
  • 采用了两种机器学习模型:k-最近邻居和支持向量回归.
  • 利用贝叶斯优化进行超参数调整,并将其与替代方法进行比较.

主要成果:

  • 与MFP相比,机器学习方法显示出更高的本地化准确性.
  • 成功识别了5.6公里 (误差<0.1公里) 和79米深度 (误差<0.5米) 的水下目标.
  • 贝叶斯优化在超参数调整方面比其他方法更有效.

结论:

  • 提出的贝叶斯优化调整机器学习方法为UATL提供了更准确,更有效的解决方案.
  • 这种方法有效地解决了传统方法在杂和大规模数据场景中的局限性.