基于强轨道和平方根UKF的人工神经网络,用于GPS中断期间的INS/GNSS情报综合系统
Yi Yang1, Xueyao Wang2, Nan Zhang2
1School of Electronic Engineering, Xi'an Shiyou University, Xi'an, 710065, China. yiyang@xsyu.edu.cn.
Scientific reports
|June 17, 2024
概括
本研究引入了一种新的神经网络学习算法,以提高在卫星信号丢失期间的INS/GNSS集成精度. 该方法提高了稳定性,并弥补了INS错误的积累,提高了导航性能.
科学领域:
- 导航系统 导航系统
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 惯性导航系统/全球导航卫星系统 (INS/GNSS) 的整合对于准确的定位至关重要.
- 在卫星信号中断时,INS/GNSS系统的性能下降,并出现累积的错误.
研究的目的:
- 为神经网络开发一种新的学习算法,以提高卫星信号中断期间的INS/GNSS集成性能.
- 提高智能集成导航系统的稳定性和稳定性.
主要方法:
- 设计了一个神经网络学习算法,使用一个非线性模型来更新体重.
- 该算法使用强度跟踪和平方根无气味卡尔曼波器 (UKF) 进行非线性模型代.
- 为了减少计算复杂性,实施了改进的强度跟踪系数计算方法.
主要成果:
- 拟议的算法证明了神经网络的计算稳定性和稳定性得到了改善.
- 实现了INS/GNSS集成错误累积的有效补偿.
- 观察到INS/GNSS智能集成系统的定位准确度得到了显著改善.
结论:
- 这种新的神经网络学习算法有效地解决了在信号中断期间的INS/GNSS性能下降问题.
- 强大的跟踪和平方根UKF的整合提高了导航系统的可靠性.
- 这种方法提供了一个强大的解决方案,可以在具有挑战性的导航环境中提高定位准确性.
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