基于GNSS/SINS组合导航的改进集合卡尔曼滤波算法OA

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.