Identification for multirate multi-input systems using the multi-innovation identification theory ?
This paper considers identification problems of multirate multi-input sampled-data systems. Using the continuous-time system discretization technique with zero-order holds, the mapping relationship (statespace model) between available multirate input and output data is set up. The multi-innovation identification theory is applied to estimate the parameters of the obtained multirate models and to present a multi-innovation stochastic gradient algorithm for the multirate systems from the multirate inputoutput data. Furthermore, the convergence properties of the proposed algorithm are analyzed. An illustrative example is given.
multi-innovation identification statespace models multirate systems convergence properties parameter estimation filtering