Data Filtering Based Recursive Least Squares Parameter Estimation for ARMAX Models

This paper uses an estimated noise transfer function to filter the input-output data and presents a filtering based recursive least squares algorithm for ARMAX models. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the recursive least squares method can estimate the parameters of these two identification models, respectively, by replacing unmeasurable noise terms in the information vectors with their estimates. The proposed F-RLS algorithm has high computational efficiency because the dimensions of its covariance matrices become small and can generate more accurate parameter estimation compared with other existing algorithms.