Adaptive deconvolution and identification of nonminimum phase FIR systems based on cumulants

A novel adaptive deconvolution and system identification scheme is introduced for a linear, non-minimum-phase finite-impulse-response (FIR) system driven by non-Gaussian white noise. The adaptive scheme is based on approximating the FIR system by noncausal autoregressive (AR) models and using higher order cumulants of the system output. As such, it is a blind equalization (deconvolution) scheme. The set of updated AR parameters is obtained by using a gradient-type algorithm and by using higher order cumulants instead of time samples of the output signal. It is demonstrated by means of extensive simulations that the adaptive scheme works well for both stationary and nonstationary cases. As expected, it outperforms the autocorrelation-based gradient method for nonminimum-phase system identification and deconvolution. Performance comparisons to existing methods are given, using as figures of merit the probability of errors in the restored input sequence, computational complexity, and convergence rate