On the identification of non-minimum phase non-gaussian MA and ARM A models using a third-order cumulant

Abstract

As higher-order cumulants preserve both the magnitude and the phase information of received signals, a higher-order cumulant has been considered as a powerful signal processing tool for a non-minimum phase system. This paper describes the development of a third-order cumulant-based adaptive recursive least-square algorithm for the identification of a time-invariant non-minimum phase system and a time-variant non-minimum phase with non-gaussian input. The third-order cumulant-based algorithm has its basis in a cost function defined in the third-order cumulant and the third-order cross cumulant. The algorithm is applied to non-minimum phase ARM A and MA models system identification. The experimental results indicate that the third-order cumulant-based adaptive algorithm is capable of identifying the non-minimum phase and time-varying system. In addition, because of the third-order cumulant properties, the algorithm can suppress the gaussian noise and is capable of providing an unbiased estimate under a noisy environment.