Based on the extended Kalman filter(EKF),the self-calibration extended Kalman filter(SEKF)and the multiple-model estimation(MME),and considering the influence of unknown inputs(such as gusts,faults,unknown system errors,etc.)on the nonlinear system state equation in Engineering,the multiple-model self-calibration extended Kalman filter(MSEKF)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 EKF and the SEKF whose weights were assigned automatically to obtain the final filtering result through weight-average way.The MSEKF can not only effectively compensate the effects of non-zero unknown inputs on the nonlinear system,but also improve the estimation accuracy compared with the SEKF when these effects were zero.A large number of simulation results using the proposed method showed that the accuracy can be improved by more than 4％,showing stronger adaptability and robustness.
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