New results on FIR system identification using higher-order statistics

The problem of estimating the parameters of a moving average model from the cumulant statistics of the noisy observations of the system output is considered. The system is driven by an i.i.d. (independent and identically distributed) non-Gaussian sequence that is not observed. The noise is additive and may be colored and non-Gaussian. Re-parametrization of an existing linear method, and a modification to it, are discussed. Simulation results show a distinct improvement in the numerical conditioning of both, the reparametrized algorithm and its modification, for the noisefree case. For the case of i.i.d. noise, the reparametrized algorithm shows a marked degradation in performance whereas its modification degrades far more gracefully.<>