Inverse filter criteria for estimation of linear parametric models using higher order statistics

The author considers the problem of estimating the parameters of a stable, scalar ARMA (autoregressive moving average) signal model (causal or noncausal, minimum phase or mixed phase) driven by an independent and identically distributed nonGaussian sequence. The driving noise sequence is not observed. The Wiggins-Donoho class of inverse filter criteria for estimation of model parameters are analyzed and extended to general ARMA inverses. A class of criteria for consistent parameter estimation in colored Gaussian noise is proposed and analyzed