SYSTEM-IDENTIFICATION WITH NOISY INPUT-OUTPUT DATA USING A CUMULANT-BASED STEIGLITZ-MCBRIDE-ALGORITHM

In this brief, we propose a cumulant-based, iterative method for ident ifying a linear time-invariant system from its noisy input/output data . The input and output are assumed to be non-Gaussian, while the input and output noises are assumed to be mutually correlated, colored, and Gaussian. At each iteration, the proposed method minimizes an objecti ve function that asymptotically is equal to a scalar multiple of Steig litz and McBride's (ensemble average version) objective function for n oise-free data. Unlike Steiglitz and McBride's method, the proposed on e is consistent for inputs that are persistently exciting of sufficien t order.