Multiarray signal processing: Tensor decomposition meets compressed sensing

Lek-Heng Lim; Pierre Comon

doi:10.1016/j.crme.2010.06.005

Multiarray signal processing: Tensor decomposition meets compressed sensing
[Traitement du signal multi-antenne : Les décompositions tensorielles rejoignent l'échantillonnage compressé]

Présenté par : Odile Macchi

Lek-Heng Lim ¹ ; Pierre Comon ²

¹ Department of Mathematics, University of California, Berkeley, CA 94720-3840, United States
² Laboratoire I3S, CNRS UMR6070, University of Nice, 06903, Sophia-Antipolis, France

Comptes Rendus. Mécanique, Volume 338 (2010) no. 6, pp. 311-320.

Résumé
Abstract

Nous décrivons comment les techniques et outils d'échantillonnage compressé récemment découverts peuvent être utilisés dans les décompositions tensorielles, avec pour illustration une modélisation des signaux provenant de plusieurs antennes multicapteurs. Nous montrons qu'en posant des bornes appropriées sur une certaine mesure de séparation entre les sources rayonnantes (appelée cohérence dans le jargon de l'échantillonnage compressé), on pouvait toujours garantir l'existence et l'unicité d'une meilleure approximation de rang r du tenseur représentant le signal. Nous en déduisons aussi une variante calculable de la condition d'unicité de Kruskal, où cette cohérence apparaît comme une mesure du k-rang. Les problèmes de récupération parcimonieuse avec un dictionnaire infini continu, de représentation tensorielle de plus bas rang, et de séparation aveugle de sources sont ainsi abordés d'une seule et même façon. La décomposition du tenseur de mesures conduit à la localisation et à l'extraction simultanées des sources rayonnantes, de manière entièrement déterministe.

We discuss how recently discovered techniques and tools from compressed sensing can be used in tensor decompositions, with a view towards modeling signals from multiple arrays of multiple sensors. We show that with appropriate bounds on a measure of separation between radiating sources called coherence, one could always guarantee the existence and uniqueness of a best rank-r approximation of the tensor representing the signal. We also deduce a computationally feasible variant of Kruskal's uniqueness condition, where the coherence appears as a proxy for k-rank. Problems of sparsest recovery with an infinite continuous dictionary, lowest-rank tensor representation, and blind source separation are treated in a uniform fashion. The decomposition of the measurement tensor leads to simultaneous localization and extraction of radiating sources, in an entirely deterministic manner.

Reçu le : 2009-12-16
Accepté le : 2010-06-29
Publié le : 2010-06-01

DOI : 10.1016/j.crme.2010.06.005

Keywords: Signal processing, Blind source separation, Blind channel identification, Tensors, Tensor rank, Polyadic tensor decompositions, Best rank-r approximations, Sparse representations, Spark, k-rank, Coherence, Multiarrays, Multisensors
Mots-clés : Traitement de signal, Séparation aveugle de sources, Identification aveugle de canal, Tenseurs, Rang tensoriel, Décompositions tensorielles polyadiques, Meilleure approximation de rang r, Représentations parcimonieuses, Spark, k-rang, Cohérence, Antennes multiples, Multicapteurs

Affiliations des auteurs :

Lek-Heng Lim ¹ ; Pierre Comon ²

¹ Department of Mathematics, University of California, Berkeley, CA 94720-3840, United States
² Laboratoire I3S, CNRS UMR6070, University of Nice, 06903, Sophia-Antipolis, France

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Lek-Heng Lim; Pierre Comon. Multiarray signal processing: Tensor decomposition meets compressed sensing. Comptes Rendus. Mécanique, Volume 338 (2010) no. 6, pp. 311-320. doi : 10.1016/j.crme.2010.06.005. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2010.06.005/

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