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一个集成算法用于SINS/GNSS/Airdata导航系统使用自适应超扭曲方法 + 优化混合型GRU-LSTM序列模型
Sadra Rafatnia1, Elahe Sadat Abdolkarimi2
1Faculty of Mechanical Engineering, Sahand University of Technology, Tabriz, 513351996, Iran.
ISA transactions
|October 24, 2025
概括
这项研究引入了一个使用混合GRU-LSTM模型进行可靠导航的自适应观测器,即使全球导航卫星系统 (GNSS) 信号丢失. 该系统准确地估计了车辆的位置,并在信号中断时纠正惯性导航错误.
科学领域:
- 导航系统工程 导航系统工程
- 控制系统理论 控制系统理论
- 机器学习用于传感器融合
背景情况:
- 综合导航系统在很大程度上依赖全球导航卫星系统 (GNSS) 数据.
- 由于环境因素或干扰导致的GNSS信号丢失严重降低了导航准确度.
- 缩惯性导航系统 (SINS) 容易发生累积错误和未建模的惯性测量偏差.
研究的目的:
- 设计和实施一个强大的集成导航系统,适应全球导航卫星系统 (GNSS) 信号中断.
- 通过解决 Strap-down 惯性导航系统 (SINS) 干扰和惯性测量错误来提高导航准确性.
- 在适应性超扭曲观察器框架内利用优化的混合GRU-LSTM序列模型 (OHGLSM).
主要方法:
- 适应性超扭曲观察器用于状态和扰动估计.
- 集成了一个优化的混合GRU-LSTM序列模型 (OHGLSM),用于增强位置估计.
- 空气数据传感器 (皮托管,气压传感器) 提供速度和高度数据,利用非全学约束.
- 额外的状态被纳入名义模型,以解释未知的惯性测量误差.
主要成果:
- 拟议的方法在集成导航中显示出高精度和可靠性,即使在全球导航卫星系统 (GNSS) 信号阻塞期间.
- 使用现实世界的车辆测试进行的实验评估验证了系统在各种场景中的性能.
- 相对分析显示,与现有的估计方法相比,精度更高,特别是在GNSS中断期间.
结论:
- 使用OHGLSM开发的自适应观测器在全球导航卫星系统 (GNSS) 信号丢失时为集成导航提供了可靠的解决方案.
- 空气数据传感器的集成通过在GNSS无法使用时提供关键信息来提高可靠性.
- 拟议的方法显著提高了在具有挑战性的环境中导航系统的精度和可靠性.
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