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Statistical efficiency of structured cpd estimation applied to Wiener-Hammerstein modeling

Abstract : The computation of a structured canonical polyadic de-composition (CPD) is useful to address several important modeling problems in real-world applications. In this paper, we consider the identification of a nonlinear system by means of a Wiener-Hammerstein model, assuming a high-order Volterra kernel of that system has been previously estimated. Such a kernel, viewed as a tensor, admits a CPD with banded circulant factors which comprise the model parameters. To estimate them, we formulate specialized estimators based on recently proposed algorithms for the computation of struc-tured CPDs. Then, considering the presence of additive white Gaussian noise, we derive a closed-form expression for the Cramer-Rao bound (CRB) associated with this estimation problem. Finally, we assess the statistical performance of the proposed estimators via Monte Carlo simulations, by comparing their mean-square error with the CRB.
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https://hal.archives-ouvertes.fr/hal-01118725
Contributor : Remy Boyer <>
Submitted on : Thursday, February 19, 2015 - 8:27:53 PM
Last modification on : Wednesday, December 30, 2020 - 1:08:05 PM
Long-term archiving on: : Wednesday, May 20, 2015 - 11:05:54 AM

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  • HAL Id : hal-01118725, version 1
  • ARXIV : 1502.06777

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José Henrique de Morais Goulart, Maxime Boizard, Rémy Boyer, Gérard Favier, Pierre Comon. Statistical efficiency of structured cpd estimation applied to Wiener-Hammerstein modeling. European Signal Processing Conference (EUSIPCO’15), Aug 2015, Nice, France. ⟨hal-01118725v1⟩

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