测试科学与仪器2026，Vol.17Issue(2)：243-253,11.DOI:10.62756/jmsi.1674-8042.2026021
基于GNSS/SINS组合导航的改进集合卡尔曼滤波算法
Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation
摘要
Abstract
The ensemble Kalman filter(EnKF)has emerged as a popular data fusion filtering method in vehicle-mounted global navigation satellite system/strapdown inertial navigation system(GNSS/SINS)integrated navigation systems.It employs Monte Carlo methods based on sample estimates to approximate the system's state distribution.However,the EnKF typically assumes a Gaussian distribution for the state distribution,and this assumption may fail in non-Gaussian scenarios.To address this issue,this paper proposes a Cauchy robust ensemble Kalman filter(CREnKF)that dynamically identifies and suppresses outliers through the Cauchy weighting function,and reduces the impact of non-Gaussian noise by combining residual direct weighting and observation covariance reconstruction dual-path robustness strategies.The algorithm was applied to a GNSS/SINS integrated navigation system and tested through simulation experiments and in-vehicle experiments.The experimental results show that the position RMSE of this scheme in a non-Gaussian noise environment is decreased by 82%,81%,and 63%relative to EKF,EnKF,and EnKF robust with Huber Kernel function,respectively,effectively enhancing the positioning accuracy of the integrated navigation system.关键词
集合卡尔曼滤波/非高斯噪声/组合导航/鲁棒滤波器/蒙特卡罗方法/Cauchy函数Key words
EnKF/non-Gaussian noise/integrated navigation/robust filter/Monte Carlo methods/Cauchy function引用本文复制引用
曹龙攀,周鑫,司涌波,严玉乾,陈光武..基于GNSS/SINS组合导航的改进集合卡尔曼滤波算法[J].测试科学与仪器,2026,17(2):243-253,11.基金项目
This work was supported by the National Natural Science Foundation of China(No.52472344) （No.52472344）
Major Cultivation Project of the University Scientific Research Innovation Platform(No.2024CXPT-17) （No.2024CXPT-17）
Lanzhou City Talent Innovation and Entrepreneurship Project(No.2022-RC-56) （No.2022-RC-56）
and Lanzhou Science and Technology Plan Project(No.2025-GN-1). （No.2025-GN-1）
