一种针对5G/INS的容错定位方法,基于变量贝叶斯强度跟踪融合选与多级别故障检测
Zhongliang Deng1, Ziyao Ma1, Haiming Luo1
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种新的噪声建模方法和故障检测系统,用于强大的5G/INS融合本地化. 它在复杂的环境中提高了准确性和连续性,即使有传感器故障.
科学领域:
- 机器人和导航 机器人和导航
- 信号处理 信号处理
- 传感器融合式传感器
背景情况:
- 高精度和连续定位在复杂的环境中至关重要.
- 传统方法与非高斯噪声和传感器故障作斗争.
研究的目的:
- 为复杂的环境开发一个强大的5G/INS融合本地化方法.
- 在非高斯噪声和传感器故障下提高定位准确性和连续性.
主要方法:
- 提出了一种使用Pearson VII类型分布用于时间变化和异常噪声的新型噪声建模方法.
- 实现了可变贝叶斯强度跟踪过,以实现稳定状态估计.
- 为IMU和5G数据设计了一个层次化的渐进式故障检测机制,包括波段分析和双通道一致性模型.
主要成果:
- 在非高斯噪声环境中显著提高了定位精度和连续性.
- 有效地捕获了使用Pearson VII型分布的极端值分布.
- 成功检测和补偿IMU和5G观测故障,从而实现了强大的连续定位.
结论:
- 拟议的噪声建模和故障检测框架增强了5G/INS融合本地化的稳定性.
- 该方法在具有挑战性噪声特征和传感器故障的复杂环境中有效.
- 这种方法为高精度和高连续性本地化应用提供了可靠的解决方案.
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