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Updated: Jan 16, 2026

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强大的跟踪缩小维度的CKF算法用于SINS在大不对齐角度下的初始对齐.

Yong Yang1,2, Donglin Li3,4, Yujie Fu3,4

  • 1Department of Flight Technology, Civil Aviation Flight University of China, Guanghan, 618307, China. yangyong@cafuc.edu.cn.

Scientific reports
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概括
此摘要是机器生成的。

一个新的强跟踪缩小尺寸立方卡尔曼波器 (STDR-CKF) 算法改善了Strapdown惯性导航系统 (SINS) 的初始对齐. 它减少了计算负载和计时器错误,以实现更快,更准确的导航.

关键词:
立方体卡尔曼波器的过器最初的对齐是初始对齐.紧固化的惯性导航系统.强大的追踪过器.

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科学领域:

  • 导航系统工程 导航系统工程
  • 控制理论 控制理论
  • 信号处理 信号处理

背景情况:

  • 准确的初始对齐对于Strapdown惯性导航系统 (SINS) 运行至关重要.
  • 卡尔曼过和进化算法是SINS初始对齐的常见但计算密集型方法.
  • 较大的近距离误差和公里计误差对传统方法构成重大挑战.

研究的目的:

  • 为SINS初始对齐开发一个计算效率高和强大的算法,特别是在大方位不对齐下.
  • 为了解决和减轻因脉冲计数切断而导致的公里计增量错误.
  • 为了提高SINS初始对齐过程的过稳定性和准确性.

主要方法:

  • 提出了一个强大的跟踪维度减小立方卡尔曼波器 (STDR-CKF) 算法.
  • 实现了状态变量分离框架 (6个非线性,12个线性状态) 以减少计算负载.
  • 利用SINS和计时器数据之间的差异计算进行反校正,以抑制计时器错误.
  • 集成了一个强跟踪波器 (STF) 减小因子,用于实时动态反校正和提高稳定性.

主要成果:

  • 与传统的立方卡尔曼波器 (CKF) 相比,STDR-CKF算法显著降低了计算负载.
  • 计时器的增量误差通过拟议的差异计算和反校正方法有效地被抑制.
  • 实验结果显示,在大方位错位条件下,提高了过稳定性和更高的准确性.
  • 与传统算法相比,拟议的方法实现了较短的初始对齐时间.

结论:

  • STDR-CKF算法为SINS初始对齐提供了卓越的解决方案,以高精度平衡计算效率.
  • 该方法有效地克服了传统方法的局限性,特别是在具有很大的自行方向错位和计时器不准确性的场景中.
  • 这一进步有助于提高导航系统的可靠性和效率.