A nonparametric identification method for highly nonlinear systems is presented that is able to
reconstruct the underlying nonlinearities without a priori knowledge of the describing
nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the
well-known state augmentation technique that turns the filter into a dual state and parameter
estimator of which an extension towards nonparametric identification is proposed in the
present work.
