Adaptive filtering based on polycepstra

An adaptive filtering method is proposed for the identification of a non-Gaussian, white-noise-driven, linear, generally non-minimum-phase system. The non-minimum-phase impulse response of the system is estimated from the updated differential cepstrum parameters of the higher order cumulants (polycepstra) of the system output. The updated cepstrum parameters are obtained by utilizing higher order cumulants and an LMS (least mean squares) type algorithm. It is shown, using Monte-Carlo simulation examples, that the proposed method would perform very well at the expense of more computations. The use and effectiveness of the method in blind linear-equalization applications is demonstrated