Identification of nonlinear cascade systems using paired multisine signals

Abstract

The identification of nonlinear cascade models has been widely studied, as they often reflect the physical structure of practical nonlinear systems. The problem when using such models is establishing their structure and then identifying their linear subsystems. Both can be obtained from measured Volterra kernels. By performing tests with a pair of input signals, specially designed in order to measure these kernels, enough information can be gathered to separate the linear systems. A brief introduction is given to the measurement of Volterra kernels using periodic multisine signals. A method using combined tests is then proposed to estimate the nonparametric and parametric models of the linear subsystems. An example is given for a simulated system with a second-order nonlinearity.