An identification method for nonminimum-phase autoregressive moving average system using phase-equivalent moving average system

One of the most important issues of signal processing is to identify the model that can describe the generation process for an observed signal through the statistical analysis of the random signal. Recently, as an identification method for such process, a method is considered in which the phase information of the signal using higher-order cumulants is paid close attention. This paper proposes a method in which the observed one-dimensinal signal is modeled as the output signal of a linear time-invariant system generated with the non-Gaussian white process. the generation systm for the signal (not constrained to be minimum phase) is identified using the third-order cumulant of the observed signal. Because the system characteristics are decomposed into their components in the cepstral domain, the proposed method does not require the factorization procedure. In other words, the feature of the method is that it is extendable to the multidimensional system identification, for which the factorization generally is impossible.