Abstract
In this paper, two bias-compensated least-squares (BCLS) methods (BCLS-α method and BCLS-β method) are proposed for identification of linear discrete-time system in the case where the input measurement is corrupted by white noise and the output measurement is corrupted by colored noise. It is well known that BCLS method is based on compensation of asymptotic bias on the least-squares (LS) estimate by making use of noise variances estimates. The main feature of proposed algorithms is to introduce an auxiliary multivariate estimator using filter (α-filter or β-filter) in order to estimate input noise variance and output noise covariances. Some simulation results indicate that the proposed methods provide good estimates.
