Nadège Thirion-Moreau

• Professor
• Professor (Full) at University of Toulon

In this article, we present a mission simulator developed for the Alseamar SeaExplorer underwater gliders. By taking into consideration a 4D time-varying environment, it provides an estimation of the most important metrics: the battery level, the mission duration and the distance traveled by the glider. The main strengths of this simulator are firs...

Efficiently representing Dynamic Textures (DTs) based on salient features is one of the considerable challenges in computer vision. Locating these features can be obstructed due to the impact of encoding factors. In this article, a novel concept of Robust Local Ternary Patterns (RLTP) is introduced in consideration of the invariance of Local Ternar...

The main purpose of this article is to study a new model of low rank tensor completion. The goal is to predict missing values from a small set of observations. More particular, we present a model of hyperspectral images completion based on a tensor factorization guaranteeing the low rank property and a bi-regularization term promising the preservat...

This work concerns the resolution of inverse problems encountered in multidi- mensional signal processing problems. Here, we address the problem of tensor decompo- sition, more precisely the Canonical Polyadic (CP) Decomposition also known as Parafac. Yet, when the amount of data is very large, this inverse problem may become numerically ill-posed...

The spatialization of halieutic data is an essential element to define and create efficient protected and managed areas. Moreover, the distribution of fish schools is not homogeneous in the water column and is strongly linked with marine habitats. It is thus necessary to develop techniques allowing a spatial evaluation of halieutic resources. Multi...

The spatialisation of halieutic data is an essential element to define and create efficient protected and managed areas. Moreover, the distribution of fish schools is not homogeneous in the water column and is strongly linked with marine habitats. It is thus necessary to develop techniques allowing a spatial evaluation of halieutic resources. Multi...

The modern multi-beam echo sounders (MBES) are advanced instrumentation for active underwater acoustic surveys that can be boarded on oceanic vessels as well on light crafts. Although their versatility allows scientists to perform various environmental studies, their potential is seldom fully exploited. A single data acquisition cruise is not only...

In this paper, the problem of the blind separation of complex-valued Satellite-AIS datafor marine surveillance is addressed. Due to the specific properties of the sources un-der consideration: they are cyclo-stationary signals with two close cyclic frequencies,we opt for spatial quadratic time-frequency domain methods. The use of an additionaldiver...

p>In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional d...

Modern multi-beams echo sounders (MBES) are advanced devices for active underwater acoustic that can be boarded either on oceanic vessels or on light crafts. Although their versatility allows scientists to perform various measurements and environmental studies, their whole potential are yet seldom fully used. Thus, a single data acquisition cruise...

This article addresses the problem of the Non Unitary Joint Block Diagonalization (\(\mathsf {NU-JBD}\)) of a given set of complex matrices for the blind separation of convolutive mixtures of sources. We propose new different iterative optimization schemes based on Conjugate Gradient, Preconditioned Conjugate Gradient, Levenberg–Marquardt and Quasi...

This paper investigates the use of Primal-Dual optimization algorithms on multidimensional signal processing problems. The data blocks interpreted in a tensor way can be modeled by means of multi-linear decomposition. Here we will focus on the Canonical Polyadic Decomposition (CPD), and we will present an application to fluorescence spectroscopy us...

This communication deals with N-th order tensor decompositions. More precisely, we are interested in the (Canonical) Polyadic Decomposition. In our case, this problem is formulated under a variational approach where the considered criterion to be minimized is composed of several terms: one accounting for the fidelity to data and others that can rep...

This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; fro...

Dans ce travail, nous nous intéressons au système d’identification automatique spatial lequel est dédié à la surveillance maritime par satellite. Ce système couvre une zone bien plus large que le système standard à terre correspondant à plusieurs cellules traditionnelles ce qui peut entraîner des risques de collision des données envoyées par des na...

In this article, we address the problem of tensor factorization subject to certain constraints. We focus on the canonical polyadic decomposition, also known as parallel factor analysis. The interest of this multilinear decomposition coupled with 3D fluorescence spectroscopy is now well established in the fields of environmental data analysis, bioch...

In this letter, the problem of nonnegative tensor decompositions is addressed. Classically, this problem is carried out using iterative (either alternating or global) deterministic optimization algorithms. Here, a rather different stochastic approach is suggested. In addition, the ever-increasing volume of data requires the development of new and m...

Dans cette communication, nous introduisons un nouveau problème de décomposition matricielle conjointe d'un ensemble de matrices complexes donné appelé Zéro-Bloc Diagonalisation Conjointe Non-Unitaire. Ce problème peut se rencontrer dans différents domaines d'applications dont la séparation aveugle de mélanges convolutifs de sources (ou déconvoluti...

This communication addresses a new problem which is the Non-Unitary Joint Zero-Block Diagonalization of a given set of complex matrices. This problem can occur in fields of applications such as blind separation of convolutive mixtures of sources and generalizes the non unitary Joint Zero-Diagonalization problem. We present a new method based on the...

Nous considérons le problème de la séparation aveugle de mélanges de sources par bloc-diagonalisation conjointe non unitaire d'un ensemble de matrices issues de transformées temps-fréquence spatiales quadratiques. Nous utilisons un critère de sélection automatique de points temps-fréquence permettant la construction de l'ensemble des matrices à blo...

We consider blind source separation in chemical analysis focussing on the 3D fluorescence spectroscopy framework. We present an alternative method to process the Fluorescence Excitation-Emission Matrices (FEEM): first, a preprocessing is applied to eliminate the Raman and Rayleigh scattering peaks that clutter the FEEM. To improve its robustness ve...

In this communication, the problem of blind source separation in chemical analysis and more precisely in the fluorescence spectroscopy framework is addressed. Classically multi-linear Canonical Polyadic (CP or Candecomp/Parafac) decomposition algorithms are used to perform that task. Yet, as the constituent vectors of the loading matrices should be...

In tensor factorization approach to blind separation of multidimensional sources two formulas for calculating the source tensor have emerged. In practice, it is observed that these two schemes exhibit different levels of robustness against perturbations of the factors involved in the tensor model. Motivated by both practical reasons and the will to...

This communication addresses the Successive Interference Cancellation (SIC) technique of decollision of AIS (Automatic Identification System) signals in maritime surveillance context. AIS is a Self-Organized Time Division Multiple Access (SO-TDMA) system, in which vessels periodically transmit information. Researchers are confronted with the increa...

In this article, we describe the role of time-frequency distributions (TFDs) in array processing. We particularly focus on quadratic TFDs (QTFDs). We demonstrate how these distributions can be properly integrated with the spatial dimension to enhance individual source signal recovery and angular estimation. The framework that enables such integrati...

This communication addresses the problem of the Non-Unitary Joint Block Diagonalization (NU − JBD) of a given set of complex matrices. This problem occurs in various fields of applications, among which is the blind separation of con-volutive mixtures of sources. We present a new method for the NU − JBD based on the Levenberg-Marquardt algorithm (LM...

This paper deals with the problem of incomplete data i.e. data with missing, unknown or unreliable values, in the polyadic decomposition of a nonnegative three-way tensor. The main advantage of the nonnegativity constraint is that the approximation problem becomes well posed. To tackle simultaneously these two problems, we suggest the use of a weig...

Computing the minimal polyadic decomposition (also often referred to as canonical decomposition or sometimes Parafac) amounts to finding the global minimum of a coercive polynomial in many variables. In the case of arrays with nonnegative entries, the low-rank approximation problem is well posed. In addition, due to the large dimension of the probl...

This paper deals with the minimum polyadic decomposition of a nonnegative three-way array. The main advantage of the nonnegativity constraint is that the approximation problem becomes well posed. To tackle this problem, we suggest the use of a cost function including penalty terms built with matrix exponentials. Gradient components are then derived...

This paper deals with the minimal polyadic decomposition (also known as canonical decomposition or Parafac) of a 3way array, assuming each entry is positive. In this case, the low-rank approximation problem becomes well-posed. The suggested approach consists of taking into account the nonnegativenatureof the loadingmatricesdirectlyin the problem pa...

This chapter presents the principles of blind separation and recovery of nonstationary signals incident on sensor arrays, specifically those characterized by their instantaneous frequencies. It also provides the fundamental approach to nonstationary source separation based on spatial quadratic time-frequency distributions applied to deterministic s...

This article addresses the problem of the non-unitary joint block diagonalization of a given set of complex matrices. Two new algorithms are provided: the first is based on a classical gradient approach and the second is based on a relative gradient approach. For each algorithm, two versions are provided: the fixed stepsize and the optimal stepsize...

Raw data are collected in five measurement locations along the Var river. It is assumed that some locations interact with each other, whereas others do not. In such a context, we are interested in determining the contribution of each location and in better understanding the water exchanges that are involved. Organic components can also be identifie...

This paper addresses the problem of the non unitary joint diagonalization of a given set of complex matrices. We focus on gradient based algorithms. A new algorithm based on a relative gradient approach is suggested. Its algorithmic complexity is established and the optimal stepsize is calculated algebraically at each iteration to decrease the numb...

The Wigner function is used for detecting subsurface targets underneath a rough surface. The target is detected by averaging the Wigner functions of the scattered field obtained with different wavelength and source configurations.

The Wigner distribution function is investigated as a signal processing tool to detect subsurface targets closely located beneath a randomly rough surface. Information provided by a bistatic arrangement of sources and detectors can be used to discriminate target and surface response based on their scattering behavior. It is shown that the bilineari...

This paper adresses the problem of the joint zero-diagonalization of a given set of matrices. We establish the identiflability conditions of the zero-diagonalizer, and we propose a new algebraical algorithm based on the reformulation of the initial problem into a joint-diagonalization problem. The zero-diagonalizer is not constrained to be unitary....

In this paper, we address the problem of the blind separation of convolutive mixtures of sources also known as blind equal-ization of linear time invariant multi-input multi-output sys-tems in digital communications. In our case, the considered input signals are cyclo-stationary processes whose cyclic fre-quencies are not necessarily known. To tack...

This paper addresses the problem of the non-unitary joint block diagonalization (NU − JBD) of a given set of matrices. Such a problem arises in various fields of applications among which blind separation of convolutive mixtures of sources and array processing for wide-band signals. We present two new algorithms based respectively on (absolute) grad...

In this communication, we address the problem of the blind separation of convolutive mixtures of sources also known as blind equalization of linear time invariant multi-input multi-output systems in digital communications. The considered input signals are cyclo-stationary processes whose cyclic frequencies are not necessarily known. We propose a ne...

This paper addresses the problem of the non-unitary approximate joint block diagonalization (NU − JBD) of matrices. Such a problem occurs in various fields of applications among which blind separation of convolutive mixtures of sources and wide-band signals array processing. We present a new algorithm for the non-unitary joint block-diagonalizatio...

This paper deals with the problem of the blind separation of convolutive mixtures of sources. We present a novel method based on a new non orthogonal joint block diagonalization algorithm (NO − JBD) of a given set of matrices. The main advantages of the proposed method are that it is more general and a preliminary whitening stage is no more compul...

This paper deals with the blind separation of instantaneous mixtures of source signals using time-frequency distributions (TFDs). We propose iterative algorithms to perform the nonorthogonal zero diagonalization and/or joint diagonalization of given sets of matrices. As an application, we show that the source separation can be realized by applying...

This paper deals with the blind separation of instantaneous mixtures of source signals using time-frequency distributions (TFDs). We propose iterative algorithms to perform the nonorthogonal zero diagonalization and/or joint diagonalization of given sets of matrices. As an application, we show that the source separation can be realized by applying...

This article addresses the problem of the blind identification of the mixing matrix in the case of a possibly under-determined instantaneous linear mixture of sources. The considered input signals are cyclo-stationary processes with unknown cyclic frequencies. We propose a new method consisting of the application of a particular linear operator on...

In this paper, we consider the blind signal separation problem for convolutive mixtures, in the real case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms. We provide several examples of these new criteria which are useful for sources having different high-order statistics. We also perfo...

In this communication, we propose a new method to blindly identify the mixing matrix of a possibly under-determined mixture of cyclostationary source signals. It is based on the use of a linear operator applied on the observations correlation matrix. Exploiting the properties of the above transformed matrix, a set of cyclic frequencies is first est...

This communication is concerned with blind separation of instantaneous mixtures of source signals based on the use of spatial quadratic time-frequency (spectrum) distributions. First, we propose a new algorithm to perform the non orthogonal joint zero-diagonalization and joint-diagonalization of given sets of matrices. We also present a selection p...

The separation problem of an instantaneous mixture of deterministic or random source signals is addressed. We show that the separation can be realized through the nonorthogonal joint diagonalization of spatial quadratic time-frequency matrices. One advantage of the proposed method is that it does not require any whitening stage, and thus, it is int...

This paper is concerned with blind separation of source signals using time-frequency representations. We show that the separation can be realized through the non-orthogonal joint zero diagonalization of spatial quadratic time frequency matrices. One advantage of the proposed method is that it does not require any whitening stage and thus it is inte...

The paper is devoted to blind separation of deterministic source signals based on time-frequency representations. Our main result is to show that the joint-diagonalization of a number of spatial quadratic transform matrices of the observation signals is sufficient for separation and we give the minimum number. A computer simulation illustrate the r...

In this paper, we are interested in blind source separation methods based on joint-diagonalization of combined sets of "spatial t-f distributions" matrices (STFD). Our aim is to perform source separation with no pre-whitening, thanks to the nonorthogonal joint-diagonalization procedure recently proposed in Yeredor (2002). We also show how such an a...

In this paper, we consider the blind signal separation problem in the convolutive case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms and give examples of these new criteria. We also realize a statistical study of the proposed source separation approach, including both the consistency...

We consider the problem of blind multivariate signal equalization. Assuming that the input signals are i.i.d. and statistically mutually independent, we propose both a generalization of some available equalization criteria and a generalization of some source separation criteria to the convolutive case. Hence, we obtain a new generalized class of ob...

In this paper, the problem of blind source separation is considered. Many solutions have been brought to that problem, among which is the method recently introduced in Belouchrani (2001, 1998), that consists of joint-diagonalizing a combined set of spatial t-f distribution (stfd) matrices. In Giulieri et al. (2001) we have introduced new criteria o...

We consider here the problem of blind sources separation. During the last decade, many solutions have been proposed among which contrasts functions, maximum likelihood functions, information-theoretic criteria, etc... More recently, a new method based on some time-frequency (t-f ) representations has been introduced by Belouchrani et al. It consist...

The problem of multichannel blind signal deconvolution is considered. The mixing system is supposed to be stable and invertible and the input signals, also called sources, are assumed zero-mean independent and identically distributed (IID) random signals. Using the hypothesis that sources are statistically independent, we propose a generalization t...

In this paper, the problem of the blind separation of independent sources is considered. Our approach relies on high-order inverse criteria. After generalizing the definition of classical contrast functions, we exhibit a wide class of generally nonsymmetrical functions that will be called “generalized contrasts” and whose maximization is proved to...

Nous considérons le problème de la séparation de mélanges de signaux statistiquement indépendants en contexte convolutif. Notre approche est fondée sur la maximisation de fonctions de contraste. Après un rappel préalable de la définition des fonctions de contraste, nous montrons que dans le cadre des mélanges convolutifs, on peut considérer des con...

Problems of separation of convolutive mixtures of wideband signals impinging on an antenna array of sensors often arise in signal processing. In seismic signal processing, techniques have been developed to perform separation of seismic waves (f-k or median filters, spectral matrix filtering, Radon or Karhunen-Loe`ve transforms, maximum likeli...

The problem of multichannel blind signal deconvolution is considered. We show that input signals can be restored (or separated) using only the condition that they are statistically independent. Two main necessary and sufficient conditions involving high order cumulants are given and proved. Hence, a class of criteria for multichannel signal deconvo...

The wavelet transform can be used to develop the process which allows group and phase velocity measurement of dispersive waves. The method has been applied to acoustic data to measure formation velocities. The behaviour and the accuracy of the method have been checked on synthetic full waveform acoustic data. The use of a wavelet transform yields a...

The dispersive properties of surface waves can be used in a case of exploration technology for weathering calculations. In full waveform acoustic Jogging, the dispersive waves are the Pseudo- Rayleigh waves in fast formations only and the Stoneley modes. The phase velocity of these dispersive waves can be used to evaluate the shear velocity of a fo...

We consider the problem of separation of convolutive mixtures of wideband signals impinging on an antenna of sensors focusing on the case of interfering seismic waves. We are looking at the spec-tral matrix filtering method. The analytical study of its resolving power, makes it possible for us to theoretically justify its use but even to explain it...

This communication is concerned with blind separation of convolutive mixtures of white correlated sources using second order statistics only. It is shown that non- orthogonal joint zero-diagonalization of some correlation ma- trices of the observed data allows to identify the mixing system up to the classical scale and permutation indetermination....

Le problème de la séparation de mélanges convolutifs de signaux à large bande de fréquences apparaît très souvent en traitement du signal. Les signaux sismiques, qui sont des signaux déterministes, colorés et corrélés entre eux du fait du problème des multi-trajets, s'inscrivent dans cette problématique. L'objet de cet article sera d'exploiter cert...

On considère le problème de la séparation de sources basée sur l'optimisation de critères. Nous proposons une définition des fonctions de contraste plus générale afin de pouvoir considérer des fonctions non symétriques et nous donnons deux nouveaux contrastes. Dans le cas de deux sources, nous déterminons le coefficient de dissymétrie optimal en mi...

·Nous considérons le problème de la séparation aveugle de sources déterministes par zéro-diagonalisation conjointe d'un ensemble particulier de matrices issues des représentations temps-fréquence spatiales quadratiques des observations. Nous proposons un nouvel algorithme de zéro-diagonalisation conjointe sous transformation non unitaire, obtenu pa...

Nous considérons le problème de la séparation aveugle de mélanges convolutifs de sources par bloc-diagonalisation conjointe non unitaire d'un ensemble de matrices issues de transformées temps-fréquence spatiales quadratiques. Nous proposons un nouveau critère de sélection automatique de points temps-fréquence permettant la construction de l'ensembl...

Nous considérons le problème de la séparation aveugle de mélanges convolutifs de sources. Nous proposons un nouvel algorithme de bloc-diagonalisation conjointe d'un ensemble de matrices sous transformation non-orthogonale. Il repose sur l'optimisation algébrique d'un critère de type moindres carrés. L'intérêt majeur d'une telle approche, outre le f...