Recursive identification of errors-in-variables models with correlated output noise

Abstract: The identification of Errors-in-variables (EIV) models refers to systems where the available The identification of Errors-in-variables (EIV) models refers to systems where the available measurements of their inputs and outputs are corrupted by additive noise. A large variety of solutions are available when dealing with this estimation problem, in particular when the corrupting noises are white processes. However, the number of available solutions decreases when the output noise is assumed as a colored process, which is a case of great practical interest. On the other hand, many applications require estimation algorithms to work on-line, tracking a dynamical system behavior for control, signal processing, or diagnosis. In many cases, they even have to take into account computational constraints. In this paper, we propose an estimation method that is able to both lay out an algorithm to solve the identification problem of EIV systems with arbitrarily correlated output noise