适应性过器参数重建技术用于火箭惯性导航/卫星综合导航系统
Zhijie Yang1, Guoguang Chen1, Mingli Niu1,2
1College of Mechanical and Electrical Engineering, North University of China, Taiyuan, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种适应性重新配置的扩展卡尔曼波器 (AREKF) 用于微电机系统 (MEMS) 束式惯性导航系统 (SINS) /全球导航卫星系统 (GNSS) 集成导航. 在高过载条件下,AREKF提高了火箭的实时导航精度.
科学领域:
- 导航系统工程 导航系统工程
- 航空航天工程 航空航天工程
- 信号处理 信号处理
背景情况:
- 微电机系统 (MEMS) 带式惯性导航系统 (SINS) 与全球导航卫星系统 (GNSS) 集成,提供紧,经济实惠,高精度的导航.
- 火箭载MEMS-SINS/GNSS系统需要高超负荷,精度和实时性能,由于不断变化的MEMS噪音和动态飞行环境,这带来了挑战.
- 传统的卡尔曼过方法与适应性参数建模作斗争,以在高超载下实时导航.
研究的目的:
- 开发用于火箭上的MEMS-SINS/GNSS集成导航系统的先进过方法.
- 为了应对火箭飞行导航中的噪声变化和实时需求的挑战.
- 在高超载条件下提高导航解决方案的准确性和实时能力.
主要方法:
- 针对火箭飞行动态量身定制的精确系统状态模型的开发.
- 在火箭对齐阶段实施实时过器参数重建.
- 介绍了自适应可重新配置的扩展卡尔曼波器 (AREKF) 算法.
主要成果:
- 通过AREKF方法,可以证明过过程的快速趋同.
- 导航参数的自适应建模确保了更低的计算成本和更高的准确性.
- 与传统EKF和其他改进的算法相比,AREKF在高超载场景中显著提高了实时导航精度.
结论:
- 在火箭载MEMS-SINS/GNSS系统中,AREKF方法为高过载的实时导航提供了强大的解决方案.
- 这种方法有效地模拟了不断变化的噪声特征和动态飞行环境.
- 通过模拟和实验验验证的AREKF提高了导航精度和实时输出.
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