深度贝叶斯神经网络用于UWB定位系统的相位错误校正
Jianjia Li1,2, Baoguo Yu3,4, Menghuan Yang1,2
1The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050081, China.
Scientific reports
|November 4, 2025
概括
一个新的双层贝叶斯神经网络融合框架 (DBNNFF) 显著提高了室内定位的基于角度的定位精度. 这种创新方法可以将角度误差减少94%以上,从而提高机器人和医疗保健的精度.
科学领域:
- 机器人和自动化 机器人和自动化
- 传感器融合式传感器
- 室内定位技术 室内定位技术
背景情况:
- 基于角度的定位系统对于机器人,医疗保健和工业自动化中的精确室内定位至关重要.
- 超宽带 (UWB) 基于相位的角度测量承诺高精度,但受到道不一致错误的阻碍.
- 现有的方法在实际的UWB角度测量中努力减轻系统错误.
研究的目的:
- 引入一种新的双层贝叶斯神经网络融合框架 (DBNNFF),以解决UWB角度测量的系统错误.
- 为了提高基于角度的室内定位系统的准确性和稳定性.
- 在具有挑战性的环境中提高UWB基于阶段的本地化可靠性.
主要方法:
- 开发一个双层贝叶斯神经网络融合框架 (DBNNFF).
- 在DBNNFF中整合物理约束和不确定性意识建模.
- 在无声室和多路径环境中使用5通道UWB基站和单通道标签进行实验验证.
- 通过冷启动循环在各种阿齐木斯角度收集数据.
主要成果:
- 在受控环境中,DBNNFF框架将角度误差降低了94.7%,至0.1036°±0.0182°.
- 性能超过了现有的算法25-42.1%.
- 在多路径环境 (办公室,走廊) 中表现出强大的性能,误差保持在0.17°以内.
- 双网络架构提供了精确校准的置信区间和特殊的噪声稳定性.
结论:
- DBNNFF有效地减轻了UWB角度测量的系统错误,显著提高了室内定位的准确性.
- 该框架的不确定性意识建模和融合方法提供了卓越的噪声稳定性和可靠的置信区间.
- 在各种应用中,DBNNFF为高精度基于角度的定位提供了显著的进步.
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