Statistical efficiency of structured cpd estimation applied to Wiener-Hammerstein modeling - UGA-TEST-TER Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

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

Résumé

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.
Fichier principal
Vignette du fichier
crb-cpd.pdf (143.95 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01118725 , version 1 (19-02-2015)
hal-01118725 , version 2 (24-06-2015)

Licence

Paternité - Partage selon les Conditions Initiales

Identifiants

Citer

José Henrique de Morais Goulart, Mélanie Boizard, Remy 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⟩
627 Consultations
187 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More