Nonlinear filtering based joint estimation of parameters and states in polynomial systems

Aiming to solve the problem of the accuracy of the states and parameters estimation are greatly influenced by initial values in the polynomial systems, this paper proposes a nonlinear filtering based joint state estimation and parameter identification method in the polynomial systems. Using the results of the least square as the initial values in the Extended Kalman Filtering (EKF) algorithm for estimating the states and parameters jointly in the polynomial systems. The results show that compared to the models obtained by using EKF, models obtained by the proposed method can greatly reduce the system state estimation error covariance. Meanwhile, the states and parameters of the system joint-estimation is also completed by the proposed method.