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 independent and identically distributed (i.i.d.) non-Gaussian sequence that is not observed. The noise is additive and may be colored and non-Gaussian. Reparametrization of an existing linear method, and a modification to it, are discussed. Simulation results show a distinct improvement in the numerical conditioning of the reparametrized algorithm and its modification for the noise-free 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.<
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