Deng Zhongliang, Ma Ziyao, Luo Haiming, Guo Jilong, Tian Zidu
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2025 Jun 16;25(12):3753. doi: 10.3390/s25123753.
In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection mechanism is proposed to detect IMU rationality faults and consistency faults in 5G observation information. The main contributions are reflected in the following two aspects: first, by innovatively introducing Pearson VII-type distribution for noise modeling, dynamically adjusting the tail thickness characteristics of the probability density function through its shape parameter, and effectively capturing the distribution law of extreme values in the observation data. Afterward, this article combined the variational Bayesian strong tracking filtering algorithm to construct a robust state estimation framework, significantly improving the localization accuracy and continuity in non-Gaussian noise environments. Second, a hierarchical progressive fault detection mechanism is designed. A wavelet fault detection method based on a hierarchical voting mechanism is adopted for IMU data to extract the abrupt features of the observed data and quickly identify faults. In addition, a dual-channel consistency detection model with dynamic fault-tolerant management was constructed. Sudden and gradual faults were quickly detected through a dual-channel pre-check, and then, the fault source was identified through AIME. Based on the fault source detection results, corresponding compensation mechanisms were adopted to achieve robust continuous localization.
本文针对复杂环境下高精度、高连续性定位的需求，提出一种基于时变噪声和异常值噪声的建模方法，并将变分贝叶斯强跟踪滤波用于5G/INS融合定位。提出一种分层递进式故障检测机制，用于检测IMU合理性故障以及5G观测信息中的一致性故障。主要贡献体现在以下两个方面：一是创新性地引入Pearson VII型分布进行噪声建模，通过其形状参数动态调整概率密度函数的尾部厚度特性，有效捕捉观测数据中的极值分布规律。随后，本文结合变分贝叶斯强跟踪滤波算法构建了一个鲁棒的状态估计框架，显著提高了非高斯噪声环境下的定位精度和连续性。二是设计了一种分层递进式故障检测机制。对IMU数据采用基于分层投票机制的小波故障检测方法，提取观测数据的突变特征并快速识别故障。此外，构建了一个具有动态容错管理的双通道一致性检测模型。通过双通道预检查快速检测突发和渐变故障，然后通过AIME识别故障源。基于故障源检测结果，采用相应的补偿机制实现鲁棒的连续定位。
