@article{CAO2026, 
author = {Longpan CAO and Xin ZHOU and Yongbo SI and Yuqian YAN and Guangwu CHEN},
title = {Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation},
year = {2026},
journal = {Journal of Measurement Science and Instrumentation},
volume = {17},
number = {2},
pages = {243-253},
keywords = {EnKF, non-Gaussian noise, integrated navigation, robust filter, Monte Carlo methods, Cauchy function},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2026021},
doi = {10.62756/jmsi.1674-8042.2026021},
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.}
}