Identification of errors-in-variables models with colored output noise

This paper deals with the problem of identifying linear errors-in-variables (EIV) models corrupted by white noise on the input and colored noise on the output. This allows to take into account the presence of both measurement errors and process disturbances. The proposed approach is based on a nonlinear system of equations whose unkowns are the system parameters and the input noise variance. The obtained set of equations allows mapping the EIV identification problem into a quadratic eigenvalue problem that, in turn, can be mapped into a linear generalized eigenvalue problem. The performance of the proposed approach is illustrated by means of Monte Carlo simulations and compared with those of existing techniques.