Estimating parameters of dynamic errors-in-variables systems with polynomial nonlinearities

An approach for identifying single-input single-output discrete-time dynamic nonlinear errors-in-variables systems is presented where the system model can be linearized such that it is expressed as a linear combination of polynomials of input and output observations. We assume white Gaussian noise on both input and output, characterized by a noise magnitude and a normalized noise covariance structure matrix, and employ a nonlinear extension of the generalized Koopmans-Levin method to estimate model parameters with an assumed noise structure and a subsequent covariance matching objective function minimization to estimate all noise parameters. The feasibility of the approach is demonstrated by Monte-Carlo simulations.