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RAIMONDI Francesca

'Tensor Array Processing'


Directeur de thèse :     Pierre COMON

École doctorale : Electronique, electrotechnique, automatique, traitement du signal (EEATS)

Spécialité : Signal, image, parole, télécoms

Structure de rattachement : CNRS

Établissement d'origine : Politecnico di Milano (Italie)

Financement(s) : autres financements


Date d'entrée en thèse : 01/10/2014

Date de soutenance : 22/09/2017


Composition du jury :
HAARDT, Martin, professeur, Ilmenau University of Technology (Ilmenau, Germany) - rapporteur
CHEVALIER, Pascal, professeur du CNAM, titulaire de la chaire (Paris) - rapporteur
BRIE, David, professeur, CRAN (Nancy), examinateur
MICHEL, Olivier, professeur, Grenoble-INP, GIPSA-lab (Grenoble), examinateur
COMON Pierre, directeur de recherche, CNRS, GIPSA-lab (Grenoble), directeur de thèse


Résumé : Source estimation and localization are a central problem in array signal processing, in particular in telecommunications, seismology, acoustics, biomedical engineering, and astronomy. Sensor arrays, i.e. acquisition systems composed of multiple sensors that receive source signals from different directions, sample the impinging wavefields in space and time. Hence, high resolution techniques such as MUSIC make use of these two elements of diversity: space and time, in order to estimate the signal subspace generated by impinging sources, as well as their directions of arrival. This is generally done through the estimation of second or higher orders statistics, such as the array spatial covariance matrix, thus requiring sufficiently large data samples. Only recently, tensor analysis has been applied to array processing using as a third mode (or diversity), the space shift translation of a reference subarray, with no need for the estimation of statistical quantities. Tensor decompositions consist in the analysis of multidimensional data cubes of at least three dimensions through their decomposition into a sum of simpler constituents, thanks to the multilinearity and low rank structure of the underlying model. Thus, tensor methods provide us with an estimate of source signatures, together with directions of arrival, in a deterministic way. This can be achieved by virtue of the separable and low rank model followed by narrowband sources in the far field. This thesis deals with source estimation and localization of multiple sources via tensor methods for array processing. It introduces a general tensor model to include multiple physical diversities, such as space, time, space shift, polarization, gain patterns, and propagation speed of narrowband elastic waves. It also establishes a tensor model for the coherent array processing of wideband waves, leading to applications for seismology.

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