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Communication dans un congrès

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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Contributeur : Remy Boyer <>
Soumis le : jeudi 19 février 2015 - 20:27:53
Dernière modification le : jeudi 19 novembre 2020 - 13:01:15
Archivage à long terme le : : mercredi 20 mai 2015 - 11:05:54


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Distributed under a Creative Commons Paternité - Partage selon les Conditions Initiales 4.0 International License


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



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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