优化的分数顺序扩展卡尔曼过用于IMU基于态度估计使用河马算法
Xiaoping Yang1,2,3, Gangwang Lin4, Jianqi Wang5
1College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了HO-FEKF,自动化分数顺序扩展卡尔曼波器调整,以准确地估计态度. 这种新的框架在现实世界的传感器融合中显著优于现有的方法.
科学领域:
- 控制系统工程 控制系统工程
- 优化算法 优化算法
- 机器人技术和自主系统
背景情况:
- 分数顺序扩展卡尔曼波器 (FEKF) 的性能受到其分数顺序参数的手动调节的限制.
- 准确的态度估计对于各种应用中的传感器融合至关重要.
研究的目的:
- 开发一个自动化框架 (HO-FEKF) 来优化FEKF的分数顺序参数.
- 增强FEKF模型,以更好地处理非线性系统动态和传感器融合.
主要方法:
- 集成海马优化 (HO) 算法用于自动参数调整.
- 实施层次优化策略以尽量减少态度估计错误.
- 对FEKF模型的改进,包括改进的雅可比式计算,处理跨因素相互作用,以及滑动剩余窗口.
主要成果:
- 与传统的FEKF相比,增强的FEKF模型表现出优越的性能.
- 完整的HO-FEKF框架显著超过了FEKF与其他优化算法 (GA,GWO,HHO,HiPPO-LegS) 相结合.
- 对公开基准和定制数据集的验证证实了拟议方法的有效性.
结论:
- HO-FEKF为适应性,高精度的态度估计提供了有效的解决方案.
- 自动调整方法克服了手动参数调整的局限性.
- 该框架显示了现实世界传感器融合应用的重大实际潜力.
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