Tensor methods for multisensor signal processing
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Tensor methods for multisensor signal processing
Auteur(s) :
Miron, Sebastian [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Zniyed, Yassine [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
de Almeida, André [Auteur]
Universidade Federal do Ceará = Federal University of Ceará [UFC]
Favier, Gérard [Auteur]
Signal, Images et Systèmes [Laboratoire I3S - SIS]
Brie, David [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Comon, Pierre [Auteur]
GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes [GIPSA-GAIA]
Centre de Recherche en Automatique de Nancy [CRAN]
Zniyed, Yassine [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
de Almeida, André [Auteur]
Universidade Federal do Ceará = Federal University of Ceará [UFC]
Favier, Gérard [Auteur]
Signal, Images et Systèmes [Laboratoire I3S - SIS]
Brie, David [Auteur]
Centre de Recherche en Automatique de Nancy [CRAN]
Comon, Pierre [Auteur]
GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes [GIPSA-GAIA]
Titre de la revue :
IET Signal Processing
Pagination :
693-709
Éditeur :
Institution of Engineering and Technology
Date de publication :
2021-01-08
ISSN :
1751-9675
Mot(s)-clé(s) en anglais :
direction-of-arrival estimation
tensors
MIMO communication
singular value decomposition
sensor fusion
least squares approximations
optimisation
wireless channels
tensors
MIMO communication
singular value decomposition
sensor fusion
least squares approximations
optimisation
wireless channels
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, we propose a comprehensive overview of tensor-based models and methods for multisensor signal ...
Lire la suite >Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, we propose a comprehensive overview of tensor-based models and methods for multisensor signal processing. We present for instance the Tucker decomposition, the Canonical Polyadic Decomposition (CPD), the Tensor-Train Decomposition (TTD), the Structured TTD, including Nested Tucker Train (NTT), as well as the associated optimization strategies. More precisely, we give synthetic descriptions of state-of-art estimators as the Alternating Least Square (ALS) algorithm, the High-Order SVD (HOSVD), and of more advanced algorithms as the Rectified ALS, the TT-SVD/TT-HSVD and the Joint dImensionally Reduction And Factor retrieval Estimator (JIRAFE) scheme. We illustrate the efficiency of the introduced methodological and algorithmic concepts in the context of three important and timely signal processing-based applications: the Direction-Of-Arrival (DOA) estimation based on sensor arrays, multidimensional harmonic retrieval and MIMO wireless communication systems.Lire moins >
Lire la suite >Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, we propose a comprehensive overview of tensor-based models and methods for multisensor signal processing. We present for instance the Tucker decomposition, the Canonical Polyadic Decomposition (CPD), the Tensor-Train Decomposition (TTD), the Structured TTD, including Nested Tucker Train (NTT), as well as the associated optimization strategies. More precisely, we give synthetic descriptions of state-of-art estimators as the Alternating Least Square (ALS) algorithm, the High-Order SVD (HOSVD), and of more advanced algorithms as the Rectified ALS, the TT-SVD/TT-HSVD and the Joint dImensionally Reduction And Factor retrieval Estimator (JIRAFE) scheme. We illustrate the efficiency of the introduced methodological and algorithmic concepts in the context of three important and timely signal processing-based applications: the Direction-Of-Arrival (DOA) estimation based on sensor arrays, multidimensional harmonic retrieval and MIMO wireless communication systems.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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