LAUSR

This letter deals with the identification of dynamical systems corrupted by additive and independent identically distributed Gaussian noise sources when the noise-free-input is an arbitrary signal. We review two stochastic models: 1) the errors-in-variables (EIV) and 2) the random variable setting. We derive a maximum likelihood (ML) estimator for the latter and compare with a previously proposed EIV ML method. The problem is formulated in the frequency domain and it is assumed that the ratio of the noise variances is known. We discuss the similarity between the two approaches and prove that one can be seen as the regularized counterpart of the other, advocating the suggested estimator when only few data samples are available.