A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system  

作　　者：LYU Xu MENG Ziyang LI Chunyu CAI Zhenyu HUANG Yi LI Xiaoyong YU Xingkai 

机构地区：[1]Department of Precision Instrument,Tsinghua University,Beijing 100084,China [2]College of Mechanical and Power Engineering,Three Gorges University,Yichang 443002,China [3]Unit 91001 of the PLA,Beijing 100161,China [4]School of Control and Computer Engineering,North China Electric Power University,Beijing 100096,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第3期732-740,共9页系统工程与电子技术（英文版）

基　　金：supported by China Postdoctoral Science Foundation(2023M741882);the National Natural Science Foundation of China(62103222,62273195)。

摘　　要：In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

关 键 词：Kalman filter dual-adaptive integrated navigation unscented Kalman filter(UKF) ROBUST 

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]