用高斯过程和机器学习进行海底分类和源本地化
Christina Frederick1, Zoi-Heleni Michalopoulou1
1Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA christin@njit.edu, michalop@njit.edu.
高斯过程增强了声学数据,用于更高的海底分类和源范围估计. 这种机器学习方法可以改善各种水下环境中的沉积物范围绘制.
科学领域:
- 海洋声学 海洋声学
- 地物理信号处理的信号处理.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 研讨会'97声学数据提供了关于水下环境的见解.
- 在垂直分离的接收器上测量声场.
研究的目的:
- 使用声学数据对海底类型进行分类并估计源范围.
- 改进信号处理以进行增强的水下声学分析.
主要方法:
- 用于虚拟接收器的数据拒绝和预测的高斯过程.
- 机器学习用于将增强的声信号映射到沉积物范围的类别.
- 分析不同环境和范围的声场.
主要成果:
- 高斯过程显著改善了声学数据的无声化.
- 增强的声场导致了更高的分类准确性.
- 成功地将信号映射到15个不同的沉积物范围类别.
结论:
- 基于高斯过程的无声化增强了用于海底分类的声学数据.
- 机器学习与增强的声学数据相结合,为环境分析提供了强大的方法.
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