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• W2149944854 abstract "We consider the problem of estimating the parameters of an unknown discrete linear system driven by a sequence of independent identically distributed (i.i.d.) random variables whose probability density function (PDF) may be non-Gaussian. We assume a general system structure that may contain causal and noncausal poles and zeros. The parameters characterizing the input PDF may also be unknown. We derive an asymptotic expression for the Cramer-Rao lower bound, and show that it is the highest (worst) in the Gaussian case, indicating that the estimation accuracy can only be improved when the input PDF is non-Gaussian. It is further shown that the asymptotic error variance in estimating the system parameters is unaffected by lack of knowledge of the PDF parameters, and vice verse. Computationally efficient gradient-based algorithms for finding the maximum likelihood estimate of the unknown system and PDF parameters, which incorporate backward filtering for the identification of non-causal parameters, are presented. The dual problem of blind deconvolution/equalization is considered, and asymptotically attainable lower bounds on the equalization performance are derived. These bounds imply that it is preferable to work with compact equalizer structures characterized by a small number of parameters as the attainable performance depend only on the total number of equalizer parameters.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
• W2149944854 created "2016-06-24" @default.
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• W2149944854 date "1994-03-01" @default.
• W2149944854 modified "2023-09-27" @default.
• W2149944854 title "Maximum likelihood and lower bounds in system identification with non-Gaussian inputs" @default.
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• W2149944854 doi "https://doi.org/10.1109/18.312156" @default.
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