Identification of systems with direction-dependent dynamics

In this paper, the identification of systems with direction-dependent dynamics by means of bilinear models and Wiener models is considered. It is shown that when such a system is perturbed by a pseudo-random binary signal based on a maximum-length sequence, distinctive patterns are observed in the cross-correlation function between the system input and the system output. These patterns are not present when other kinds of pseudo-random binary signals are used. The patterns obtained for bilinear models and Wiener models are similar, and both depend on the characteristic polynomial of the maximum-length sequence used. For the case in which the dynamics involved are first-order, analytical results are obtained which allow the patterns to be compared in detail. The results expected when the pseudo-random signals used are inverse-repeat are also described. It is concluded that both kinds of model are suitable for use in this application, provided that the model parameters are appropriately chosen