一个基于向量重构的粗对齐算法,通过Sage-Husa AKF对SINS在摇摆基上的SINS进行重建.
Yongyun Zhu1,2, Bingbo Cui1,2, Dianlei Han1,2
1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种新型粗对齐算法,用于紧固式惯性导航系统 (SINSs). 该方法通过使用自适应卡尔曼波器重建观测向量,提高了初始态度确定速度和准确性,即使在具有挑战性的摇摆基础条件下.
科学领域:
- 导航系统工程 导航系统工程
- 惯性导航 惯性导航 惯性导航
- 信号处理 信号处理
背景情况:
- 压缩惯性导航系统 (SINS) 需要快速初始定位.
- 粗对齐方法对于在没有外部辅助的情况下提高SINS初始化速度至关重要.
- 惯性传感器错误和外部干扰限制了传统粗对齐观测向量模型的准确性.
研究的目的:
- 为SINSs开发一个改进的粗对齐算法.
- 为了解决现有方法中观察矢量模型不准确性的局限性.
- 为了提高在摇摆基底条件下初始态度确定的速度和准确性.
主要方法:
- 建立了一个表面速度向量的观测模型.
- 设计了一个移动窗口向量集成算法,以减轻累积的观测向量错误.
- 实现了一个矢量重建模型,使用Sage-Husa自适应卡尔曼波器 (AKF) 进行自我调整.
主要成果:
- 模拟和转盘实验验证实了拟议的算法.
- 矢量重建方法显著提高了对齐精度.
- 该方法与现有的粗对齐技术相比,显示出更高的性能.
结论:
- 提出的基于Sage-Husa AKF的矢量重建粗对齐算法有效地增强了SINS初始态度的确定.
- 该方法克服了与观察向量的不准确性和外部干扰相关的局限性.
- 这种方法提供了一个强大的解决方案,可以在动态环境中快速准确地对准SINS.
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