Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise

This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.