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一种基于学生的t噪声的新型变异贝叶斯方法,用于水下定位.

Haoqian Huang1, Yutong Zhang1, Chenhui Dong1

  • 1College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China.

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概括
此摘要是机器生成的。

这项研究引入了一种使用学生t分布 (SWVKF-ST) 的新型滑窗变异卡尔曼波器,以提高水下导航的准确性. 该SWVKF-ST方法有效地处理测量异常值,并在具有挑战性的声学环境中改进状态估计.

关键词:
多传感器融合融合技术多种色因素导致的色.滑动窗户是一个滑动窗户.学生的 t 分布.变量贝叶斯式贝叶斯式

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 信号处理 信号处理
  • 水下声学 水下声学

背景情况:

  • 水下导航系统容易受到声学传感器的多路径影响,导致测量异常值.
  • 这些异常值降低了综合导航状态估计的准确性.
  • 现有的卡尔曼波器方法与水下声学中常见的重尾噪声分布作斗争.

研究的目的:

  • 为水下综合导航提出一个强大的状态估计方法.
  • 提高对多路径效应引起的测量异常值的准确性和稳定性.
  • 为了减轻未知的噪声特征对导航准确性的影响.

主要方法:

  • 开发了一个基于学生的t分布 (SWVKF-ST) 的滑动窗口变化卡尔曼波器.
  • 利用Student的t分布来建模重尾噪声和逆Wishart分布用于噪声共变量之前.
  • 采用波动贝叶斯框架来共同估计状态变量和在移动窗口中的测量.
  • 包含多个色因子,以防止估计准确度下降.

主要成果:

  • 与传统过器相比,SWVKF-ST在模拟和实验中显示出更高的过精度和对异常值的稳定性.
  • 该方法有效地减少了测量噪声共变矩阵中的不确定性.
  • 验证了SWVKF-ST在复杂的水下环境中的出色适应性.

结论:

  • SWVKF-ST是提高水下综合导航状态估计准确性的高效方法.
  • 拟议的过器提供了针对异常值和未知的噪声特征的增强稳定性.
  • 这种方法显著提高了用于水下导航的声学传感的可靠性.