System identification from noisy measurements of inputs and outputs

In this paper a system identification method is described for the case of measurement errors on inputs and outputs. The method gives consistent estimates of the parameters and in case of normal measurement errors maximum likelihood estimates are obtained. More specific statistical properties of the estimators are also provided. Furthermore, the sensitivity of the results with to the assumptions is studied. Keywords: System identification, consistent estimates, maximum likelihood estimates, measurement errors on inputs and outputs, asymptotic statistical properties, sensitivity of estimates.