基于自适应卡尔曼算法的稀土悬浮永久磁铁磁铁列车的多传感器信息融合本地化
Yiwei Xu1,2, Kuangang Fan1,2,3, Qian Hu1,2
1School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China.
PloS one
|November 28, 2023
概括
这项研究引入了一种多传感器融合方法,具有自我校正的权重系统,以提高永久磁悬浮列车的定位精度. 增强的协差自适应卡尔曼算法显著提高了适应性和精度.
科学领域:
- 工程 工程师 工程师 工程师
- 控制系统 控制系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 磁悬浮列车的单传感器定位受到噪音和环境干扰的影响,降低了准确性.
- 传统的卡尔曼过在过程噪声存在时容易产生不准确性.
研究的目的:
- 提出一种多传感器信息融合方法,以提高永久磁悬浮列车的定位精度.
- 开发一个强大的聚变算法,使用自纠正权重和衰变记忆因子.
- 通过采用共变量适应方案来改善卡尔曼过,以减轻工艺噪声的影响.
主要方法:
- 实施了多传感器信息融合方法,具有自我校正权重.
- 引入了衰变记忆因子,以减少历史数据对核聚变估计的影响.
- 利用一个共变量适应方案,取代卡尔曼波器的预测步骤,在线重建先前的错误共变量.
主要成果:
- 拟议的多传感器融合方法提高了定位的稳定性和准确性.
- 协差自适应卡尔曼算法显著减少了不匹配的噪声统计的负面影响.
- 与传统方法相比,模拟和实验结果显示出更高的适应性,准确性和简单性.
结论:
- 多传感器信息融合与协变适应卡尔曼算法相结合,为磁悬浮列车定位提供了显著的优势.
- 该方法为磁悬浮系统的实时定位挑战提供了更具适应性,准确性和简单性的解决方案.
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