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变量贝叶斯创新和卡尔曼波器用于微电机系统-惯性导航系统/极化指南针集成导航.

Yu Sun1, Xiaojie Liu1, Xiaochen Liu1

  • 1Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, School of Instruments and Electronics, North University of China, Taiyuan 030051, China.

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概括
此摘要是机器生成的。

这项研究引入了改进的变量贝叶斯创新和强大的自适应卡尔曼波器 (VISKF),以提高微电机系统-惯性导航系统 (MEMS-INS) 中的导航精度,以极化指南针 (PC) 干扰. 在复杂的环境中,VISKF算法显著提高了动态定位的准确性和稳定性.

关键词:
卡尔曼过器可以过.学生的 t 分布.综合导航系统 综合导航系统极化指南针的极化指南针.变量贝叶斯式贝叶斯式

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科学领域:

  • 导航系统工程 导航系统工程
  • 信号处理 信号处理
  • 强大的控制理论.

背景情况:

  • 与偏振指南针 (PC) 集成的微电机系统-惯性导航系统 (MEMS-INS) 面临时间变化的测量噪声,重尾分布和异常值的挑战,特别是在干扰下.
  • 这些噪声特征降低了集成导航系统中传统卡尔曼过方法的性能和准确性.

研究的目的:

  • 提出一个改进的变量贝叶斯创新和强大的自适应卡尔曼波器 (VISKF) 算法,以解决处理PC传感器噪音和异常损坏测量的现有方法的局限性.
  • 提高MEMS-INS/PC集成导航系统在复杂和乱的环境中运行的稳定性和准确性.

主要方法:

  • 使用变化贝叶斯 (VB) 方法与学生的t分布 (STD) 准确估计时间变化的PC测量噪声的统计性质.
  • 引入了创新和函数,对和边界采用适应性更新策略,采用双层结构来管理因异常值引起的创新价值分歧.
  • 在无人驾驶车辆上进行静态和动态实验,以验证拟议的VISKF算法的性能.

主要成果:

  • 与适应式卡尔曼波器 (AKF),变量贝叶斯稳定型适应式卡尔曼波器 (VBRAKF) 和创新和型稳定型适应式卡尔曼波器 (ISRAKF) 相比,VISKF算法在动态定向准确度方面取得了显著改善,分别实现了76.89%,67.23%和84.45%的改进.
  • 拟议的算法在复杂环境中比其他可比方法表现出更高的性能和稳定性.
  • 通过VB-STD方法实现了测量噪声统计参数的准确估计.

结论:

  • 开发的VISKF算法有效地减轻了MEMS-INS/PC集成导航系统中的重尾噪声和异常值的影响.
  • 适应性创新和机制显著提高了系统的稳定性和准确性.
  • 这种方法为在具有挑战性的条件下运行的INS/PC集成系统的导航准确性和可靠性提供了显著的改善.