文献类型：期刊文献

英文题名：Robust Sequential Variational Bayesian Filter for Tightly Coupled Navigation System Within GNSS Challenged Environment

作者：Zhong, Yulu Chen, Xiyuan Shi, Chunfeng Yao, Zhiting

第一作者：Zhong, Yulu

通信作者：Chen, XY[1];Chen, XY[2]

机构：[1]Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Minist Educ, Nanjing 210018, Peoples R China;[2]Southeast Univ, Sch Instrument Sci & Engn, Key Lab Microinertial Instrument & Adv Nav Technol, Minist Educ, Nanjing 210018, Peoples R China;[3]Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Peoples R China

第一机构：Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Minist Educ, Nanjing 210018, Peoples R China

通信机构：corresponding author), Southeast Univ, Sch Instrument Sci & Engn, State Key Lab Comprehens PNT Network & Equipment T, Minist Educ, Nanjing 210018, Peoples R China;corresponding author), Southeast Univ, Sch Instrument Sci & Engn, Key Lab Microinertial Instrument & Adv Nav Technol, Minist Educ, Nanjing 210018, Peoples R China.

年份：2025

卷号：12

期号：22

起止页码：47912-47926

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20253719164631);Scopus(收录号：2-s2.0-105015614020);WOS:【SCI-EXPANDED(收录号:WOS:001611085100023)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61873064; in part by the National Defense PreResearch Foundation of China under Grant 8922006150; in part by the Joint Fund Project under Grant 8091B042206; in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022139; and in part by the Guizhou Province Science and Technology Project XKBF[2025]032.

语种：英文

外文关键词：Noise; Noise measurement; Bayes methods; Adaptive filters; Information filters; Global navigation satellite system; Navigation; Kalman filters; Internet of Things; Current measurement; Outlier detection; sequential filter; tightly coupled navigation; variational Bayesian approximation

摘要：Navigation systems are commonly used in Internet of Things (IoT) devices to provide navigation information. For achieving accurate information on devices, this article describes a robust variational Bayesian filter with sequential processing to estimate the time-varying measurement noise covariance and reject the outliers within the global navigation satellite systems (GNSS)-challenged environment. Different from the variational Bayesian adaptive filter with heavy-tailed distribution, the proposed filter uses the beta-Bernoulli distribution to prevent the outliers from interfering with the time-varying measurement noise covariance estimation by the inverse Gamma distribution. Then, the sequential processing is modified for the proposed filter in the independent multimeasurement sensor system. Sequential processing variational Bayesian interference is first proposed and applied to the tightly coupled navigation system, so far as the authors know. To evaluate the proposed method, realtime experiments in a downtown area and forest park effectively validated the filter robustness estimation, especially in scenarios with GNSS satellite obstructions.