Parameter Identification for Linear System Using Multiple Model Estimation

Abstract

Kalman filter (KF) has gained wide adoption in system identification of engineering systems. It is a recursive estimation method under linear and Gaussian assumptions. In practice, a single model based on KF may not be able to capture the structural performance well for complex systems. To address this problem, KF estimation using multiple models is proposed. This method employs KF with different transition and measurement matrices, each of which can be assigned (if necessary) with different initial states, process and measurement noises to describe the system. The outputs of these models are then integrated to obtain the overall estimates through a weighted combination, where the weights are determined using the likelihood function. A numerical model is employed to illustrate the procedure and evaluate the accuracy of the proposed KF estimation with multiple models. The estimated results indicate that the proposed method is robust and reliable, with potential for system identification under a wider variety of situations.