Numerical performances of low rank stap based on different heterogeneous clutter subspace estimators

International audienceSpace time Adaptive Processing (STAP) for airborne RADAR fits the context of a disturbance composed of a Low Rank (LR) clutter, here modeled by a Compound Gaussian (CG) process, plus a white Gaussian noise (WGN). In such context, the corresponding LR adaptive filters used to detect a target require less training vectors than classical methods to reach equivalent performance. Unlike the classical filter which is based on the Covariance Matrix (CM) of the noise, the LR filter is based on the clutter subspace projector, which is usually derived from a Singular Value Decomposition (SVD) of a noise CM estimate. Regarding to the considered model of LR-CG plus WGN, recent results are providing both direct estimators of the clutter subspace [1][2] and an exact MLE of the noise CM [3]. To promote the use of these new estimation methods, this paper proposes to apply them to realistic STAP simulations

Signal subspace change detection in structured covariance matrices

International audienceTesting common properties between covariance matricesis a relevant approach in a plethora of applications. In thispaper, we derive a new statistical test in the context of structuredcovariance matrices. Specifically, we consider low rank signalcomponent plus white Gaussian noise structure. Our aim is totest the equality of the principal subspace, i.e., subspace spannedby the principal eigenvectors of a group of covariance matrices. Adecision statistic is derived using the generalized likelihood ratiotest. As the formulation of the proposed test implies a non-trivialoptimization problem, we derive an appropriate majorizationminimizationalgorithm. Finally, numerical simulations illustratethe properties of the newly proposed detector compared to thestate of the art

Bayesian Signal Subspace Estimation with Compound Gaussian Sources

International audienceIn this paper, we consider the problem of low dimensional signal subspace estimation in a Bayesian con- text. We focus on compound Gaussian signals embedded in white Gaussian noise, which is a realistic modeling for various array processing applications. Following the Bayesian framework, we derive two algorithms to compute the maximum a posteriori (MAP) estimator and the so-called minimum mean square distance (MMSD) estimator, which minimizes the average natural distance between the true range space of interest and its estimate. Such approaches have shown their interests for signal subspace esti- mation in the small sample support and/or low signal to noise ratio contexts. As a byproduct, we also introduce a generalized version of the complex Bingham Langevin distribution in order to model the prior on the subspace orthonormal basis. Finally, numerical simulations illustrate the performance of the proposed algorithms

Détection de changement de sous-espace signal de matrices de covariance structurées

International audienceTesting common properties between covariance matrices is a relevant problem in a plethora of signal processing applications. In this paper, we derive a new statistical test in the context of structured covariance matrices. Specifically, we consider low rank signal component plus white Gaussian noise structure. Our aim is to test the equality of the principal subspace, i.e., subspace spanned by the principal eigenvectors of a group of covariance matrices. A decision statistic is derived using the generalized likelihood ratio test. As the formulation of the proposed test implies a non-trivial optimization problem, we derive an appropriate majorization-minimization algorithm. Finally, numerical simulations illustrate the properties of the newly proposed detector compared to the state of the art.Le test statistique de propriété communes entre les matrices de covariance tient une place très importante en traitement du signal. Dans cet article, nous proposons un nouveau test statistique dans le contexte de matrices de covariance structurées. Plus précisément, nous considérons un signal de rang faible corrompu par un bruit blanc gaussien additif. Notre objectif est de tester l’égalité du sous-espace signal, c’est à dire les composantes principales communes à un ensemble de matrices de covariance. Dans un premier temps, une statistique de décision est dérivée en utilisant le rapport de vraisemblance généralisée. Le maximum de vraisemblance n’ayant pas d’expression analytique dans ce cas, nous proposons un algorithme d’estimation itératif de type majoration-minimisation. Enfin, nous étudions les propriétés du détecteur proposé à l’aide de simulations numériques

The Glucuronyltransferase GlcAT-P Is Required for Stretch Growth of Peripheral Nerves in Drosophila

During development, the growth of the animal body is accompanied by a concomitant elongation of the peripheral nerves, which requires the elongation of integrated nerve fibers and the axons projecting therein. Although this process is of fundamental importance to almost all organisms of the animal kingdom, very little is known about the mechanisms regulating this process. Here, we describe the identification and characterization of novel mutant alleles of GlcAT-P, the Drosophila ortholog of the mammalian glucuronyltransferase b3gat1. GlcAT-P mutants reveal shorter larval peripheral nerves and an elongated ventral nerve cord (VNC). We show that GlcAT-P is expressed in a subset of neurons in the central brain hemispheres, in some motoneurons of the ventral nerve cord as well as in central and peripheral nerve glia. We demonstrate that in GlcAT-P mutants the VNC is under tension of shorter peripheral nerves suggesting that the VNC elongates as a consequence of tension imparted by retarded peripheral nerve growth during larval development. We also provide evidence that for growth of peripheral nerve fibers GlcAT-P is critically required in hemocytes; however, glial cells are also important in this process. The glial specific repo gene acts as a modifier of GlcAT-P and loss or reduction of repo function in a GlcAT-P mutant background enhances VNC elongation. We propose a model in which hemocytes are required for aspects of glial cell biology which in turn affects the elongation of peripheral nerves during larval development. Our data also identifies GlcAT-P as a first candidate gene involved in growth of integrated peripheral nerves and therefore establishes Drosophila as an amenable in-vivo model system to study this process at the cellular and molecular level in more detail

Robust mean and covariance matrix estimation under heterogeneous mixed-effects model with missing values

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Robust low-rank covariance matrix estimation with a general pattern of missing values

International audienceThis paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussian assumption, whereas existing robust estimation ones assume unstructured signal models. The former can be inaccurate in real-world data sets in which heterogeneity causes heavy-tail distributions, while the latter does not profit from the usual low-rank structure of the signal. Taking advantage of both worlds, a covariance matrix estimation procedure is designed on a robust (mixture of scaled Gaussian) low-rank model by leveraging the observed-data likelihood function within an expectation-maximization algorithm. It is also designed to handle general pattern of missing values. The proposed procedure is first validated on simulated data sets. Then, its interest for classification and clustering applications is assessed on two real data sets with missing values, which include multispectral and hyperspectral time series