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应用深度学习用于使用球形阵列的水下宽带源检测.

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概括
此摘要是机器生成的。

一种新的深度神经网络 (DNN) 方法改进了使用球形阵列的水下宽带源检测和方向估计. 这种方法提高了检测率,并抑制了虚假警报,而不需要源光谱信息.

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科学领域:

  • 水下声学 水下声学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 对水下宽带源的被动检测对于声纳和监视至关重要.
  • 精确的源检测和到达方向 (DOA) 估计仍然具有挑战性,特别是对于宽带信号.

研究的目的:

  • 开发一种基于深度神经网络 (DNN) 的新方法,用于使用球形阵列的水下宽带源的被动检测和DOA估计.
  • 提高水下声学检测系统的稳定性和准确性.

主要方法:

  • 使用球形阵列捕获水下声学信号.
  • 应用球体里叶变换,将元素压力信号转换为球体里叶系数,用于DNN输入.
  • 使用高斯分布设计的DNN标签,具有类似空间频谱的形式.
  • 开发了一个物理模型,模拟水下声学传播和球形阵列信号,用于DNN训练.
  • 将白噪声纳入训练数据,以改善检测和减少错误估计.

主要成果:

  • 该DNN方法证明了增强的检测能力和有效抑制错误估计.
  • 性能被评估使用检测率在一个恒定的错误报警率检测率.
  • 该模型在不同的信号噪声比率上实现了宽带检测能力.
  • DNN显示了多源检测的潜力,即使在单一源上进行训练.
  • 模拟和实验结果验证了拟议方法的有效性.

结论:

  • 开发的基于DNN的方法为被动检测和水下宽带源的DOA估计提供了强大的解决方案.
  • 该方法不需要对源光谱特征的先验知识,从而增加了其适用性.
  • 该方法在水下声学和监视方面显示出对现实世界的应用有希望.