Recursive least squares parameter estimation for non-uniformly sampled systems based on the data filtering

Abstract

By using an estimated noise transfer function to filter the input–output data, two identification models are obtained for the non-uniformly sampled Box–Jenkins system, one containing the parameters of the system model, and the other containing the parameters of the noise model. Then apply the recursive least squares method to identify the parameters of these two models, respectively, by replacing the unmeasurable terms in the information vectors with their estimates. Thus a filtering based recursive least squares algorithm is finally derived. The given illustrative example indicates that the proposed algorithm can generate more accurate parameter estimates compared with the auxiliary model based recursive generalized extended least squares algorithm.