Improved initial approximation for errors-in-variables system identification

Errors-in-variables system identification can be posed and solved as a Hankel structured low-rank approximation problem. In this paper different estimates based on suboptimal low-rank approximations are proposed. The estimates are shown to have almost the same efficiency and lead to the same minimum when supplied as an initial approximation for local optimization in the structured low-rank approximation problem. In this paper it is shown that increasing Hankel matrix window length improves initial approximation for autonomous systems and does not improve it in general for systems with inputs.