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11 results on '"Pascal, Fr?d?ric"'

3. Detection and Estimation in non Gaussian Noise

Author

Pascal, Frédéric, Pascal, Frédéric, ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), Groupe d'Electromagnétisme Appliqué (GEA), Université Paris Nanterre (UPN), Université de Nanterre - Paris X, and Philippe Forster(pforster@u-paris10.fr)

Subjects
Maximum Likelihood, GLRT-LQ, Estimation de paramètres, Détection Radar, Maximum de Vraisemblance, Fouillis non Gaussien, Covariance Matrix, Parameters Estimation, Détecteur GLRT-LQ, Matrice de covariance, SIRV, Non Gaussian Noise, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Radar Detection, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

This thesis deals with radar detection in impulsive noise contexts. Indeed, under Gaussian assumptions, classical detectors, like Optimum Gaussian Detector, present several failures when the background scatterers are heterogeneous and non Gaussian, which is the case for ground or sea clutter. Clutter physical models based on compound noise modeling (SIRP, Compound Gaussian Processes) allow to correctly describe the reality (range power variations or clutter transitions areas). However, these models depend on several unknown parameters (covariance matrix, statistical distribution of the texture, disturbance parameters) which have to be estimated. When this estimation scheme is carried out, it is possible to build optimum radar detectors (Generalized Likelihood Ratio Test - Linear Quadratic) associated to this non Gaussian background. Based on these noise models, this thesis presents a complete analysis of several estimation schemes of the noise covariance matrix, associated to the detection problem. A statistical study of the main covariance matrix estimates which are used in the literature, is performed. Moreover, an improved estimate is proposed: the Fixed Point estimate, very attractive thanks to its good statistical and detection properties.This thesis also describes detection performance and theoretical properties (texture-CFAR and matrix-CFAR) of the GLRT-LQ detector built with the studied covariance matrix estimates. In particular, the detector invariance to the texture distribution and to the covariance matrix structure is shown. Finally, these detectors are analyzed with simulated data and then, experimented on real ground clutter data., Dans le contexte très général de la détection radar, les détecteurs classiques, basés sur l'hypothèse d'un bruit Gaussien, sont souvent mis en défaut dès lors que l'environnement (fouillis de sol, de mer) devient inhomogène, voire impulsionnel, s'écartant très vite du modèle Gaussien. Des modèles physiques de fouillis basés sur les modèles de bruit composé (SIRP, Compound Gaussian Processes) permettent de mieux représenter la réalité (variations spatiales de puissance et nature de fouillis, transitions, ...). Ces modèles dépendent cependant de paramètres (matrice de covariance, loi de texture, paramètres de "disturbance") qu'il devient nécessaire d'estimer. Une fois ces paramètres estimés, il est possible de construire des détecteurs radar optimaux (Generalized Likelihood Ratio Test - Linear Quadratic) pour ces environnements. Cette thèse, qui s'appuie sur ces modèles, propose une analyse complète de diverses procédures d'estimation de matrices de covariance, associées à ce problème de détection. Une étude statistique des principaux estimateurs de matrice de covariance, utilisés actuellement, est réalisée. De plus, un nouvel estimateur est proposé: l'estimateur du point fixe, très attractif grâce à ses bonnes propriétés statistiques et "radaristiques".Elle décrit également les performances et les propriétés théoriques (SIRV-CFAR) du détecteur GLRT-LQ construits avec ces nouveaux estimateurs. En particulier, on montre l'invariance du détecteur à la loi de la texture mais également à la matrice de covariance régissant les propriétés spectrales du fouillis. Ces nouveaux détecteurs sont ensuite analysés sur des données simulées mais également testés sur des données réelles de fouillis de sol.

Published
2006

4. Normalized Coherency Matrix Estimation Under the SIRV Model. Alpine Glacier Polsar Data Analysis

Author

Vasile, Gabriel, Ovarlez, Jean-Philippe, Pascal, Frédéric, Tison, Céline, Bombrun, Lionel, Gay, Michel, Trouvé, Emmanuel, SIGMAPHY (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, ONERA - The French Aerospace Lab [Toulouse], ONERA, Sondra, CentraleSupélec, Université Paris-Saclay (SONDRA), ONERA-CentraleSupélec-Université Paris-Saclay, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), GIPSA-Services (GIPSA-Services), Erosion torrentielle neige et avalanches (UR ETGR (ETNA)), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Sondra, CentraleSupélec, Université Paris-Saclay (COmUE) (SONDRA), ONERA-CentraleSupélec-Université Paris Saclay (COmUE), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Érosion torrentielle, neige et avalanches (UR ETGR (ETNA)), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Normalization (statistics), Synthetic aperture radar, Computer science, Gaussian, Maximum likelihood, 0211 other engineering and technologies, Probability density function, 02 engineering and technology, symbols.namesake, Speckle pattern, Matrix (mathematics), Image texture, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Invariant (mathematics), 021101 geological & geomatics engineering, business.industry, Covariance matrix, Estimator, 020206 networking & telecommunications, symbols, Clutter, Artificial intelligence, business, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Coherence (physics)
Abstract

International audience; This paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors modelling. We propose the use of the Fixed Point (FP) estimator for deriving normalized polarimetric coherency matrices in compound Gaussian clutter. The main advantages of the FP estimator are that it does not require any "a priori" information about the probability density function of the texture and it can be directly applied on adaptive neighborhoods. Interesting results are obtained when coupling this FP estimator with an adaptive spatial support driven on the scalar span information. The proposed method is tested with both simulated POLSAR data and high resolution POLSAR data acquired over the French Alps.

Published
2008

5. Derivation of the Bias of the Normalized Sample Covariance Matrix in a Heterogeneous Noise With Application to Low Rank STAP Filter.

Author

Ginolhac, Guillaume, Forster, Philippe, Pascal, Frédéric, and Ovarlez, Jean-Philippe

Subjects
SIGNAL processing, ADAPTIVE filters, RADAR interference, STATISTICAL bias, VECTOR spaces, ROBUST control, EIGENVALUES
Abstract

In a previous work, we have developed a low-rank (LR) spatio-temporal adaptive processing (STAP) filter when the disturbance is modeled as the sum of a low-rank spherically invariant random vector (SIRV) clutter and a zero-mean white Gaussian noise. This LR-STAP filter is built from the normalized sample covariance matrix (NSCM) and exhibits good robustness properties to secondary data contamination by target components. In this correspondence, we derive the bias of the NSCM with this noise model. We show that the eigenvectors estimated from the NSCM are unbiased. The new expressions of the expectation of NSCM eigenvalues are also given. From these results, we also show that the estimate of the clutter subspace projector based on the NSCM used in our LR-STAP is a consistent estimate of the true one. Results on numerical data validates the theoretical approach. [ABSTRACT FROM PUBLISHER]

Published
2012
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6. Optimal Parameter Estimation in Heterogeneous Clutter for High-Resolution Polarimetric SAR Data.

Author

Vasile, Gabriel, Pascal, Frédéric, Ovarlez, Jean-Philippe, Formont, Pierre, and Gay, Michel

Abstract

This letter presents a new estimation scheme for optimally deriving clutter parameters with high-resolution polarimetric synthetic aperture radar (POLSAR) data. The heterogeneous clutter in POLSAR data is described by the spherically invariant random vector model. Three parameters are introduced for the high-resolution POLSAR data clutter: the span, the normalized texture, and the speckle normalized covariance matrix. The asymptotic distribution of the novel span estimator is investigated. A novel heterogeneity test for the POLSAR clutter is also discussed. The proposed method is tested with airborne POLSAR images provided by the Office National d'Études et de Recherches Aerospatiales Radar Aéroporté Multi-spectral d'Etude des Signatures system. [ABSTRACT FROM PUBLISHER]

Published
2011
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7. Statistical Classification for Heterogeneous Polarimetric SAR Images.

Author

Formont, Pierre, Pascal, Frédéric, Vasile, Gabriel, Ovarlez, Jean-Philippe, and Ferro-Famil, Laurent

Abstract

This paper presents a general approach for high- resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently “close” from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical H-\alpha decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts. [ABSTRACT FROM PUBLISHER]

Published
2011
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8. Clutter rank for slow-time MIMO STAP.

Author

Chong, Chin Yuan, Brigui, Frederic, Pascal, Frederic, and Quek, Yee Kian

Abstract

Multiple-Input Multiple-Output (MIMO) radar has been recently proposed to enhance performance compared to classical phased array. For STAP applications, coherent MIMO allows better angular resolution by using orthogonal waveforms. Several methods have been studied to generate orthogonal waveforms. We focus here on the slow-time waveforms which achieve orthogonality over the entire coherent integration time. We study in this paper the clutter rank using slow-time waveforms. We show that for certain cases the clutter rank cannot exceed a certain value and does not follow the Brennan rule. Some simulations are presented to illustrate this result. [ABSTRACT FROM PUBLISHER]

Published
2012

9. MIMO filters based on robust rank-constrained Kronecker covariance matrix estimation.

Author

Breloy, Arnaud, Ginolhac, Guillaume, Gao, Yongchan, and Pascal, Frédéric

Subjects
*COVARIANCE matrices, *KRONECKER products, *LOW-rank matrices, *RANDOM noise theory, *ADAPTIVE signal processing, *MIMO radar
Abstract

In this paper, we propose a new estimator of the covariance matrix parameters when observations follow a mixture of a deterministic Compound-Gaussian (CG) and a white Gaussian noise. In particular, the covariance matrix of the CG contribution is assumed to be expressed as the Kronecker product of two low-rank matrices, which is a structure often involved in MIMO array processing. The proposed estimator is then obtained by maximizing the likelihood of the data with the use of a specifically tailored block Majorization-Minimization (MM) algorithm. Finally, the method is evaluated in terms of adaptive filtering on a MIMO-STAP radar setting, showing important improvements over standard processing. [ABSTRACT FROM AUTHOR]

Published
2021
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10. Classification robuste sur l'espace des matrices de covariance : application à la texture et aux images de télédétection polarimétriques radar à ouverture synthétique

Author

ILEA, Ioana, STAR, ABES, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux, Universitatea tehnică (Cluj-Napoca, Roumanie), Christian Germain, Romulus Mircea Terebes, Germain, Christian, Terebes, Romulus Mircea, Champion, Isabelle, Fauvel, Mathieu, Bombrun, Lionel, Borda, Monica, Trouvé, Emmanuel, and Pascal, Frédéric

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Classification robuste, Riemannian space, [SPI.OTHER] Engineering Sciences [physics]/Other, Covariance matrix, Robust classification, Texture, Matrice de covariance, Espace Riemannien
Abstract

In the recent years, covariance matrices have demonstrated their interestin a wide variety of applications in signal and image processing. The workpresented in this thesis focuses on the use of covariance matrices as signatures forrobust classification. In this context, a robust classification workflow is proposed,resulting in the following contributions.First, robust covariance matrix estimators are used to reduce the impact of outlierobservations, during the estimation process. Second, the Riemannian Gaussianand Laplace distributions as well as their mixture model are considered to representthe observed covariance matrices. The k-means and expectation maximization algorithmsare then extended to the Riemannian case to estimate their parameters, thatare the mixture's weight, the central covariance matrix and the dispersion. Next,a new centroid estimator, called the Huber's centroid, is introduced based on thetheory of M-estimators. Further on, a new local descriptor named the RiemannianFisher vector is introduced to model non-stationary images. Moreover, a statisticalhypothesis test is introduced based on the geodesic distance to regulate the classification false alarm rate. In the end, the proposed methods are evaluated in thecontext of texture image classification, brain decoding, simulated and real PolSARimage classification., Au cours de ces dernières années, les matrices de covariance ont montré leur intérêt dans de nombreuses applications en traitement du signal et de l'image.Les travaux présentés dans cette thèse se concentrent sur l'utilisation de ces matrices comme descripteurs pour la classification. Dans ce contexte, des algorithmes robustes de classification sont proposés en développant les aspects suivants.Tout d'abord, des estimateurs robustes de la matrice de covariance sont utilisés afin de réduire l'impact des observations aberrantes. Puis, les distributions Riemannienne Gaussienne et de Laplace, ainsi que leur extension au cas des modèles de mélange, sont considérés pour la modélisation des matrices de covariance.Les algorithmes de type k-moyennes et d'espérance-maximisation sont étendus au cas Riemannien pour l'estimation de paramètres de ces lois : poids, centroïdes et paramètres de dispersion. De plus, un nouvel estimateur du centroïde est proposé en s'appuyant sur la théorie des M-estimateurs : l'estimateur de Huber. En outre,des descripteurs appelés vecteurs Riemannien de Fisher sont introduits afin de modéliser les images non-stationnaires. Enfin, un test d'hypothèse basé sur la distance géodésique est introduit pour réguler la probabilité de fausse alarme du classifieur.Toutes ces contributions sont validées en classification d'images de texture, de signaux du cerveau, et d'images polarimétriques radar simulées et réelles.

Published
2017

11. A new clustering algorithm for PolSAR images segmentation

Author

Violeta Roizman, Frédéric Pascal, Gordana Draskovic, Pascal, Frédéric, Universidad de Buenos Aires [Buenos Aires] (UBA), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and CentraleSupélec

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], Covariance matrix, business.industry, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Gaussian, Pattern recognition, Image segmentation, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], [STAT.OT] Statistics [stat]/Other Statistics [stat.ML], Data modeling, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.AP] Statistics [stat]/Applications [stat.AP], Robustness (computer science), Expectation–maximization algorithm, symbols, Segmentation, Artificial intelligence, Cluster analysis, business
Abstract

International audience; This paper deals with polarimetric synthetic aperture radar (PolSAR) image segmentation. More precisely, we present a new robust clustering algorithm designed for non-Gaussian data. The algorithm is based on an expectation-maximization approach. Its novelty is that, in addition to the estimation of each cluster center and covariance matrix, it also provides for each observation an estimation of the scale parameter , allowing a better flexibility when assigning each observation in one cluster. The method performances are evaluated on both simulated and real multi-looked PolSAR data. It is demonstrated that the algorithm outperforms the classical clustering algorithms such as k-means and GMM (Gaussian-based EM algorithm) in various scenarios.

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