在RBF神经网络的帮助下,在城市环境中强大的自适应GNSS/INS集成导航算法
Jin Wang1,2, Ruoyi Li1, Rui Tu1
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了一种改进的全球导航卫星系统 (GNSS) /惯性导航系统 (INS) 算法. 这种新方法通过使用强大的自适应卡尔曼波器 (RAKF) 和辐射基函数 (RBF) 网络,提高了城市地区的定位精度,即使GNSS信号丢失.
科学领域:
- 导航系统工程 导航系统工程
- 在导航中使用人工智能
- 用于定位的信号处理.
背景情况:
- 城市环境中的精确定位受到全球导航卫星系统 (GNSS) 信号减弱和中断的挑战.
- 只有惯性导航系统 (INS) 在GNSS中断时会受到准确性降低.
- 现有的集成导航系统需要改进,以便在复杂的场景中提供强大的性能.
研究的目的:
- 开发一个先进的全球导航卫星系统/惯性导航系统 (GNSS/INS) 综合导航算法.
- 在GNSS信号退化条件下提高定位准确性和稳定性,特别是在城市环境中.
- 为了减轻GNSS信号中断对导航性能的影响.
主要方法:
- 开发了一个混合框架,将强大的自适应卡尔曼波器 (RAKF) 与辐射基函数 (RBF) 神经网络相结合.
- 根据GNSS数据质量指标 (PDOP,卫星计数,信号质量),RAKF可以自适应地调整测量噪声协差.
- RBF网络预测伪位置增量,以替代停机期间缺失的GNSS测量.
主要成果:
- 拟议的RBF辅助RAKF (RBF-RAKF) 在GNSS中断期间实现了0.94m (北),1.02m (东) 和0.21m (下) 的根平均平方 (RMS) 定位误差.
- 与传统的扩展卡尔曼波器 (EKF),标准RAKF和RBF辅助卡尔曼波器 (RBF-KF) 相比,在定位准确度上有超过90%的改进.
- 在严重的GNSS信号退化下,保持了仪表水平和子仪表垂直精度.
结论:
- 在具有挑战性的城市环境中,RBF-RAKF算法提供了稳定和高精度的导航性能.
- 混合方法有效地克服了GNSS信号中断和退化所带来的局限性.
- 这种方法显著提高了自主应用的集成导航系统的可靠性.
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