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

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Bibliographic Details
Title:	Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation.
Authors:	CAO, Longpan¹, ZHOU, Xin², SI, Yongbo², YAN, Yuqian², CHEN, Guangwu^1,2 cgwyjh1976@126.com
Source:	Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p243-253. 11p.
Subjects:	Kalman filtering, Global Positioning System, Random noise theory, Wireless geolocation systems, Multisensor data fusion, Outlier detection, Monte Carlo method
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	16748042
DOI:	10.62756/jmsi.1674-8042.2026021