Parametric identification of linear noisy input-output systems

An efficient method is proposed for identification of linear noisy input-output systems. Central to this method is that the variances of the input and output noises, which determine the bias in the ordinary least-squares (LS) estimator, are estimated in the way of increasing the degrees of both the denominator and the numerator of the system transfer function by one, but with no need to evaluate the average LS errors. While achieving estimation unbiasedness, the proposed method exhibits algorithmic advantages over the LS-based algorithms recently developed. Performance comparisons with other existing estimation algorithms based upon computer simulations are given.