The Application of EKF in Parameter Identification of State-Space Model

Abstract

State-space model is an efficient tool for describing multiple input multiple output system, but the parameters identification of state-space model is a complicated problem, because in many cases, the parameters and state variables are all unknown in the model. Aiming at the shortcomings of the traditional identification method, in this paper, combined the unknown parameters and state variables of the state space model into a new state variable, then the linear state space model equation can be transformed into a nonlinear equation, and extended Kalman filtering(EKF) algorithm is used to estimate the new state variables. In this way, we can implement double estimates of the unknown parameters and state variables. Doing simulation and analysis with Matlab, the results show that the method can realize parameter identification and state estimation of state-space model effectively, which has higher precision and accuracy.