An alternative method for identification of linear systems in the presence of input and output noises

An alternative method is proposed for identification of linear noisy input-output systems. Central to this method is the point that the variances of the input and output noise, which determines 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