Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation

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.