Frequency domain identification of autoregressive models in the presence of additive noise

This paper describes a new approach for identifying autoregressive models from a finite number of measurements, in presence of additive and uncorrelated white noise. As a major novelty, the proposed approach deals with frequency domain data. In particular, two different frequency domain algorithms are proposed. The first algorithm is based on some theoretical results concerning the so--called dynamic Frisch Scheme. The second algorithm maps the AR identification problem into a quadratic eigenvalue problem. Both methods resemble in many aspects some other identification algorithms, originally developed in the time domain. The features of the proposed methods are compared each other and with those of other time domain algorithms by means of Monte Carlo simulations.