多传感器信息融合定位AUKF磁悬浮列车基于自我纠正权重
Qian Hu1,2, Hong Tang3, Kuangang Fan1,2
1School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种新的AUKF算法,用于精确的磁悬浮 (maglev) 列车定位. 它通过整合多传感器数据和采用自纠正权重与Sage-Husa噪声估计来提高准确性和稳定性.
科学领域:
- 运输工程 运输工程
- 控制系统 控制系统
- 信号处理 信号处理
背景情况:
- 精确的定位对于磁悬浮列车的安全性和调度至关重要.
- 现有的方法受到噪声干扰,单传感器依赖和历史数据偏差的影响.
- 传统的权重方案容易受到历史数据的影响,导致定位偏差.
研究的目的:
- 提出一个改进的多传感器信息融合和定位方法,用于磁升列车.
- 为了解决单传感器方法的局限性和磁电梯定位中的噪声干扰.
- 为了提高磁悬浮列车定位系统的准确性和可靠性.
主要方法:
- 开发了一个AUKF (自适应无气味卡尔曼波器) 算法,集成Sage-Husa噪声估计和自我校正权重.
- 利用来自交叉传感器线路的多传感器数据融合,惯性导航系统 (INS),多普勒雷达和全球导航卫星系统 (GNSS).
- 实现了对测量噪声的自适应统计特征估计,以克服单一功能的限制.
主要成果:
- 提出的基于自我校正的AUKF算法显示了更接近实际值的轨迹.
- 与传统方法相比,实现了降低平均误差 (ME) 和根平均平方误差 (RMSE).
- AUKF算法有效地消除了单个模块的单一功能和低集成性缺陷.
结论:
- 自行校正加权的AUKF算法在磁悬浮列车定位的稳定性,准确性和简单性方面提供了显著的优势.
- 这种多传感器融合方法增强了磁铁列车的精确定位能力.
- 该方法提供了一种强大的解决方案,用于克服马格莱夫系统中的噪音和数据集成挑战.
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