Application of Blind Identification to Nonlinear Calibration

This paper handles the identification of nonlinear discrete-time Wiener systems from output measurements only (blind identification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum likelihood estimator is constructed. A two-step procedure for generating high quality starting values is presented as well. Finally, the proposed scheme is applied to both a simulation example and a laboratory experiment, illustrating the potential usefulness of the method for nonlinear calibration applications.