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在基于FGO的智能手机PDR+GNSS融合中,行走动力学,用户可变性和窗口大小效应
Amjad Hussain Magsi1, Luis Enrique Díez1
1Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48007 Bilbao, Spain.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
因子图形优化 (FGO) 通过平衡准确性和计算来提高行人死亡计算 (PDR). 一个10个姿势的窗口大小提供了最佳的性能,在各种步行动态中显著改善了卡尔曼波器 (KF) 的定位.
科学领域:
- * 导航和定位系统
- * 传感器融合技术
- * 人类运动分析.
背景情况:
- *基于智能手机的行人定位依赖于全球导航卫星系统 (GNSS) 和行人死亡计算 (PDR).
- *因子图形优化 (FGO) 对融合GNSS和PDR数据显示出希望,但其关于人类运动动态的最佳配置尚未得到充分探索.
- * 像卡尔曼波器 (KF) 这样的现有方法可能会在PDR中遇到运动依赖错误.
研究的目的:
- * 调查步行动态对行人定位的最佳滑动窗FGO (SWFGO) 配置的影响.
- * 为了在不同的运动条件下比较FGO与KF的误差缓解能力.
- * 为了确定行走速度和FGO窗口大小之间的关系,以获得稳定的定位准确性.
主要方法:
- *从十名行人收集了四种不同的运动类型的数据:慢步,正常步行,慢跑和跑步.
- *分析滑动窗FGO (SWFGO) 在不同窗口大小 (例如1,10,30个姿势) 的性能.
- *对FGO和KF在抑制运动不规则引起的PDR异常值的比较评估.
主要成果:
- *大约10个姿势的SWFGO窗口大小在定位精度和计算负载之间实现了有利的平衡.
- * 这种10个位置的窗口大小比1个位置的窗口提供了显著的精度改进,并且以更低的计算成本接近批量FGO性能.
- *将窗口大小增加到30个姿势提供了最小的额外精度增长,但增加了计算需求,这种趋势在所有运动类型中都是一致的.
- * 与KF相比,FGO和SWFGO都显示出更高的异常值减少,提高了对步态变化和短暂干扰的稳定性.
结论:
- *带有10个姿势窗口的SWFGO是基于智能手机的强大而准确的行人定位的有效策略.
- * 在缓解运动诱导的PDR错误方面,FGO显著优于KF,在各种步行动态中提供了更好的可靠性.
- *这些发现为为行人导航系统配置FGO提供了实际指导,根据预期的运动模式优化性能.
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