Blind maximum likelihood identification of Wiener systems

This paper handles the identification of 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. Its asymptotic properties are analyzed and the Cramér-Rao lower bound is calculated. A two-step procedure for generating high quality initial estimates is presented as well. The paper includes the illustration of the method on a simulation example.