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Improved parameter estimation with noisy data for linear models using higher order statistics and inverse filter criteria

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1 Author(s)
Tugnait, J.K. ; Dept. of Electr. Eng., Auburn Univ., AL, USA

The problem of estimating the parameters of a non-Gaussian ARMA signal model using higher order statistics is considered. We propose and analyze a novel class of criteria involving explicit higher order whitening, where higher order cumulants of deconvolved data are exploited at a finite number of lags excluding the zero lag. In the presence of a class of measurement noise of unknown covariance/cumulant function, the proposed criteria are shown to yield strongly consistent parameter estimators unlike the Wiggins-Donoho-Shalvi-Weinstein class involving implicit higher order whitening, where higher order cumulants of deconvolved data are exploited only at zero lag.<>

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Signal Processing Letters, IEEE  (Volume:2 ,  Issue: 4 )