USING CONTINUOUS-TIME MODELING FOR ERRORS-IN-VARIABLES IDENTIFICATION

Continuous-time identification is applied to an errors-in-variables setting. A continuous-time model is fitted to data consisting of discrete-time noise corrupted input and output measurements. The noise-free input is modelled as a continuous-time ARMA process. It is described how the Cramér-Rao lower bound for the estimation problem can be computed. Several parameter estimation approaches for the problem are presented, and also illustrated in a short numerical study.