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 t he model that can describe the generation process for an observed sign al 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 ob served one-dimensional signal is modeled as the output signal of a lin ear time-invariant system generated with the non-Gaussian white proces s. The generation system for the signal (not constrained to be minimum phase) is identified using the third-order cumulant of the observed s ignal. Because the system characteristics are decomposed into their co mponents 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 identificatio n, for which the factorization generally is impossible.