Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS  

作　　者：LYU Xu HU Baiqing DAI Yongbin SUN Mingfang LIU Yi GAO Duanyang 

机构地区：[1]College of Electrical Engineering,Naval University of Engineering,Wuhan 430033,China [2]Beijing Huahang Radio Measurement Research Institute,Beijing 100000,China [3]School of Electrical Engineering,Liaoning University of Technology,Jinzhou 121001,China [4]School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第5期1079-1088,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61873275,61703419,425317829).

摘　　要：High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.

关 键 词：integrated navigation Gaussian process regression(GPR) QUATERNION Kalman filter ROBUSTNESS 

分 类 号：TN967.2[电子电信—信号与信息处理] TN713[电子电信—信息与通信工程]