Adaptive filtering via cumulants and LMS algorithm

Abstract

A novel adaptive identification scheme is introduced for a nonGaussian white-noise-driven linear, nonminimum-phase FIR (finite-impulse response) system. The adaptive scheme is based on noncausal autoregressive (AR) modeling of higher-order cumulants of the system output. In particular, the magnitude and phase response estimates at each iteration are expressed in terms of the updated parameters of the noncausal AR model. The set of updated AR parameters is obtained by using the LMS (least-mean-squares) algorithm and by using higher-order cumulants instead of time samples of the output signal. It is demonstrated by means of standard examples that the new adaptive scheme works well and, as expected, outperforms the modified (autocorrelation-based) LMS algorithm for nonminimum-phase system identification.<>