Maximum likelihood based recursive parameter estimation for controlled autoregressive ARMA systems using the data filtering technique

Abstract

Using the maximum likelihood principle, a filtering based maximum likelihood recursive least squares parameter estimation algorithm is derived for controlled autoregressive ARMA systems. The basic idea is to use the noise transfer function to filter the input–output data and to replace the unmeasurable noise terms in the information vectors with their estimates. The simulation results indicate that the proposed estimation algorithm can effectively estimate the parameters of such systems and can generate more precise parameter estimates than the recursive maximum likelihood and the recursive generalized extended least squares algorithms.