Least Squares Based Blind Identification Algorithms for Deterministic Systems from Multirate Sampled Data

This paper discusses the blind identification problem for deterministic systems by sampling the output with a higher frequency than that of the input and presents a least square based identification algorithm. By properly choosing the sampling rate and normalizing the model parameters or the input signals, the system parameters can be estimated only from the fast output measurements. Further, we derive two blind least squares identification methods under the two normalized conditions, respectively. The simulation results validate the algorithms proposed.