Based on the unscented Kalman filter(UKF),the self-calibration unscented Kalman filter(SUKF)and the multiple-model estimation(MME),considering the influences of unknown inputs(such as drift error of the IMU in medical manipulator,gust encountered by the running train and failure of onboard components)on the strongly nonlinear system state equation in engineering,the multiple-model self-calibration unscented Kalman filter(MSUKF)was proposed to expand the application scope of the multiple-model self-calibration Kalman filter(MSKF).According to the Bayes'theorem,this filtering method used the UKF and the SUKF whose weights were assigned automatically to obtain the final filtering result through weight-average way.A large number of simulation results showed that the accuracy of MSUKF was 50％higher than that of divergent UKF,and 4％higher than that of unbiased SUKF,presenting stronger adaptability and robustness.
NETL
NSTL
万方数据
