SEMIBLIND IDENTIFICATION OF NONMINIMUM-PHASE ARMA MODELS VIA ORDER RECURSION WITH HIGHER-ORDER CUMULANTS

This paper develops a novel identification methodology for nonminimum- phase autoregressive moving average (ARMA) models of which the models' orders are not given. It is based on the third-order statistics of th e given noisy output observations and assumed input random sequences, The semiblind identification approach is thereby named. By the order-r ecursive technique, the model orders and parameters can be determined simultaneously by minimizing well-defined cost functions. At each upda ted order, the AR and MA parameters are estimated without computing th e residual time series (RTS), with the result of decreasing the comput ational complexity and memory consumption. Effects of the AR estimatio n error on the MA parameters estimation are also reduced. Theoretical statements and simulations results, together with practical applicatio n to the train vibration signals' modeling, illustrate that the method provides accurate estimates of unknown linear models, despite the out put measurements being corrupted by arbitrary Gaussian noises of unkno wn pdf.