最大电流度标准卡尔曼/艾伦变异辅助FIR集成过器用于室内定位
Manman Li1, Lei Deng1, Yide Zhang2
1School of Electrical Engineering, Shandong Huayu University of Technology, Dezhou 253034, China.
Micromachines
|March 27, 2025
概括
本研究引入了一种集成过器,该过器结合了最大电流度标准卡尔曼过器 (mccKF) 和有限脉冲响应 (FIR) 以实现精确的惯性导航系统 (INS) 定位. 该方法显著减少了医疗应用中的本地化错误.
科学领域:
- 机器人和自动化机器人与自动化
- 生物医学工程 生物医学工程
- 传感器融合式传感器
背景情况:
- 精确的定位对于医疗应用至关重要,包括仪器跟踪和程序监控.
- 惯性导航系统 (INS) 容易出现位置错误,需要传感器融合才能准确.
- 现有的卡尔曼波器 (KF) 方法在复杂,杂的环境中可能缺乏稳定性.
研究的目的:
- 为基于INS的多源传感器数据融合开发一个强大的集成本地化系统.
- 提高INS定位在医疗应用中的准确性和可靠性.
- 提出一种适应性过方法,适应严苛的医疗环境.
主要方法:
- 提出了一种集成过器,将最大电流度标准卡尔曼过器 (mccKF) 与有限冲动响应 (FIR) 过器相结合.
- 使用超宽带 (UWB) 技术来纠正INS位置错误,差异作为波器测量.
- 实施了一个自适应策略,在性能下降的情况下,Allan变异辅助的FIR过器取代mccKF.
主要成果:
- 采用mccKF/FIR综合方法,可以显著降低定位误差.
- 与标准卡尔曼波器 (KF) 相比,定位错误减少了约32.43%.
- 与独立的mccKF相比,定位错误减少了大约37.5%.
结论:
- 拟议的mccKF/FIR综合方法为基于INS的本地化提供了一个强大而准确的解决方案.
- 过器的适应性增强了系统在具有挑战性的医疗环境中的弹性.
- 这些发现验证了拟议的传感器融合技术在精确医疗任务中的有效性.
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