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使用EMD波段过和贝叶斯优化随机森林进行水下声学信号分类的传感器导向框架.

Sergii Babichev1,2, Oleg Yarema3, Yevheniy Khomenko1

  • 1Department of Physics, Kherson State University, 73008 Kherson, Ukraine.

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概括

本研究引入了一条用于船舶声信号分类的自动管道,增强船舶识别和海上安全. 该方法提高了实时应用的精度和噪声抑制.

关键词:
贝叶斯的优化是贝叶斯的优化.经验模式分解分解功能提取 特性提取非静止的信号处理器随机的森林随机的森林船舶声信号分类船只声信号分类水下声学传感器水下声学传感器波纹过器波纹过器波纹过器

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科学领域:

  • 海洋声学 海洋声学
  • 信号处理 信号处理
  • 机器学习是机器学习.

背景情况:

  • 船舶声信号分类对于海上运营至关重要,但受到信号噪声和非静止性的挑战.
  • 由于这些固有的信号复杂性,现有的方法往往会产生低于最佳的准确性.

研究的目的:

  • 开发一个自动化,强大的管道,用于准确的船舶声信号分类.
  • 克服传统方法在处理噪音和非静止水下声学数据方面的局限性.

主要方法:

  • 经验模式分解 (EMD) 用于信号分解和通过信号对噪声比 (SNR) 选择内在模式功能 (IMF).
  • 使用SNR和斯坦的无偏风险估计 (SURE) 优化了自适应波纹过,以减少噪音.
  • 特征提取 (统计,FFT,波形) 随后选择前11个特征和贝叶斯优化随机森林分类与多数投票.

主要成果:

  • 管道实现了高分类准确性,并显著改善了噪声抑制.
  • 在具有挑战性的条件下,在识别船舶声信号方面表现强.
  • 贝叶斯优化减少了计算复杂性,同时提高了分类精度.

结论:

  • 拟议的自动化管道为实时船舶声信号分类提供了可扩展,高效和准确的解决方案.
  • 这种方法增强了海上安全和水下航行能力.
  • 集成EMD,波纹过和优化机器学习,为声信号分析提供了强大的工具.