Based on the self-calibration Kalman filter(SKF)and the multiple-model estimation(MME),considering the influence of unknown inputs(such as gusts,faults,unknown system errors,etc.)on the system state equation in Engineering,the multiple-model self-calibration Kalman filter(MSKF)was proposed.According to the Bayes'theorem,this filtering method used the SKF and the standard Kalman filter(KF)whose weights were assigned automatically to obtain the final filtering result through weight-average way.Compared with the SKF,the MSKF can not only effectively compensate the effects of non-zero unknown inputs,but also improve the estimation accuracy when unknown inputs were zero.A large number of simulation results showed that accuracy can be improved by more than 10％,using the proposed method.In summary,the MSKF has stronger adaptability and robustness.
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