测试科学与仪器2026，Vol.17Issue(1)：61-71,11.DOI:10.62756/jmsi.1674-8042.2026005
基于SINS/GNSS/ODO组合导航的改进强跟踪卡尔曼滤波算法
Improved strong tracking Kalman filter algorithm based SINS/GNSS/ODO integrated navigation
摘要
Abstract
The combination of strapdown inertial navigation system(SINS),global navigation satellite system(GNSS),and odometer(ODO)is the most practical and cost-effective way to implement a multi-source fusion automotive navigation system.However,the traditional Kalman filtering(KF)algorithm suffers from the inaccuracy of the system state matrix and the measurement noise covariance matrix during vehicle operation,which leads to a decrease in navigation and positioning accuracy.To solve this problem,a measurement adaptive strong tracking Kalman filter(MA-STKF)algorithm is proposed.The algorithm adopts an asymptotic weighting approach to estimate the measurement covariance array by considering new interest time series being actually filtered,introduces a measurement forgetting factor,perform real-time estimation and correction combines with the decay factor of the strong tracking filter,and takes advantage of the difference between the actual measurement error and the predicted covariance to reset the decay factor,which improves the tracking performance of the algorithm.The proposed algorithm is applied to the SINS/GNSS/ODO integrated navigation system,and simulation and vehicle experiments were conducted,improving the positioning longitude by 52.48%and 30.96%,and the positioning latitude by 63.27%and 37.64%,compared to KF and STKF,respectively.关键词
卡尔曼滤波/组合导航/强跟踪滤波/量测自适应/遗忘因子Key words
Kalman filtering(KF)/integrated navigation/strong tracking filter(STF)/measurement adaptation/forgetting factor引用本文复制引用
春意,陈光武,司涌波,周鑫,严玉乾..基于SINS/GNSS/ODO组合导航的改进强跟踪卡尔曼滤波算法[J].测试科学与仪器,2026,17(1):61-71,11.基金项目
This work was supported by Natural Science Foundation of Gansu Province(No.23JRRA869),Gansu Provincial Science and Technology Guidance Programme(No.2020-61-14),Gansu Province University Industry Support Programme(No.2023CYZC-32),Major Cultivation Project of Scientific Research and Innovation Platform of Universities(No.2024CXPT-17),and National Railway Administration Project(No.KF2022-021). （No.23JRRA869）
