基于分离彩色噪声频谱信息的IFOG随机错误的抑制方法
Zhe Liang1, Zhili Zhang1, Zhaofa Zhou1
1Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi'an 710025, China.
Micromachines
|August 28, 2025
概括
这项研究引入了一种新的自适应卡尔曼波器,以减少光纤陀螺仪的随机误差. 该方法可将惯性导航系统的初始对齐精度提高48%.
科学领域:
- 工程
- 仪器设备
- 信号处理
背景情况:
- 高精度的惯性导航系统依赖于精确的光纤陀螺仪 (FOG) 数据.
- 在卡尔曼过中使用自回归移动平均 (ARMA) 模型进行FOG错误抑制的传统方法面临着彩色噪声的挑战,导致不准确的状态方程和有限的可扩展性.
研究的目的:
- 在静态条件下开发一种高精度建模和抑制光纤陀螺仪的随机误差的新方法.
- 克服基于ARMA的传统卡尔曼过在FOG信号中的彩色噪声的局限性.
主要方法:
- 对FOG信号噪声特征进行深入分析,以澄清随机错误模型形式.
- 为准确的随机错误建模提出新的模型顺序确定标准.
- 使用噪声频谱信息解来抑制角度随机步行误差和处理彩色噪声的自适应卡尔曼波器的设计.
主要成果:
- 提出的方法有效地模拟和抑制光纤陀螺仪中的随机错误,包括角度随机步行.
- 与传统方法相比,适应式卡尔曼波器在处理彩色噪声方面表现出卓越的性能.
- 实验验证显示使用5分钟FOG数据的初始对齐精度平均提高了48%.
结论:
- 开发的自适应卡尔曼波器为模拟和抑制FOG随机错误提供了强大的解决方案,特别是在静态条件下.
- 这种方法克服了传统方法的原则限制,提高了惯性导航系统的精度.
- 这些发现为提高FOG在苛刻应用中的性能提供了有价值的方案.
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