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Cramer-Rao bounds for a class of systems described by Wiener and Hammerstein models
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1997
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State EstimationNonlinear System IdentificationParameter IdentificationStatistical Signal ProcessingEngineeringParameter EstimationCramer-rao BoundsHammerstein ModelsAdditive Measurement NoiseSystems EngineeringStatistical InferenceEstimation TheoryStatic NonlinearitySignal ProcessingStatistics
Cramer-Rao bounds are derived for a class of systems that can be described by Wiener and Hammerstein models, where the linear part consists of a finite-impulseresponse filter and the static nonlinearity is a polynomial. The derivation of the formulae hinges on the assumptions that the input is a zero-mean stationary gaussian process with a power spectral density that is everywhere continuous, and that the additive measurement noise is also zero-mean gaussian. Computer simulations are presented that show a close agreement between the performance of the prediction error method and the calculated CRB's.