Adaptive Filtering via Cumulants and LMS Algorithm

A new adaptive identification scheme is introduced for a non-Gaussian white noise driven linear, nonminimum phase FIR system. The adaptive scheme is based on non-causal auto-regressive (AR) modeling of higher-order cumulants of the system output. In particular, the matnitude and phase response estimates at each iteration are expressed in terms of the updated parameters of the non-causal AR model. The set of updated AR parameters is obtained by employing the LMS 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 outperform the modified (autocorrelation-based) LMS algorithm for nonminimum phase system identification.