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.

In order to diagnose the common faults of railway switch control circuit, a fault diagnosis method based on density-based spatial clustering of applications with noise (DBSCAN) and self-organizing feature map (SOM) is proposed. Firstly, the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine. Due to the high dimension of initial features, the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set. Then, the particle swarm optimization (PSO) algorithm is used to adjust the weight of SOM network to modify the rules to avoid "dead neurons". Finally, the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested. The experimental results show that this method can judge the fault mode of switch control circuit with less training samples, and the accuracy of fault diagnosis is higher than that of traditional SOM network.

Aiming at the problem that traditional vehicle trajectory matching algorithms based on hidden Markov model(HMM) cannot have both accuracy and time efficiency in complex and special road sections, a vehicle trajectory matching method based on improved HMM modeling was proposed. In the determination of candidate road sections, grid index was generated to improve the overall retrieval efficiency. The improved HMM model integrated heading angle factors in the calculation of launch probability, considered the deviation effect caused by vehicle speed on heading angle, and set empirical factors for adjustment. At the same time, considering the factors such as the excessive error of the observation value before and after and the curve section, the actual travel distance of the vehicle within the unit sampling interval was used instead of the observation distance value to ensure the accuracy of the calculation of the transfer probability. Finally, the measured data was used to conduct experiments to verify the performance of the improved algorithm. The experimental results indicated that the matching accuracy of this method was about 94.0%, which was 2.8% higher than that of the traditional HMM trajectory matching method. It also had certain advantages in improving time efficiency and matching accuracy of complex road sections. The single-point matching time was reduced by about 0.9 ms, suitable for matching under complex road conditions such as intersections, overpasses, and parallel sections.