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Bandiera F.,University of Salento | Besson O.,ISAE University | Ricci G.,University of Salento
IEEE Transactions on Signal Processing | Year: 2011

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available. The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios. © 2011 IEEE. Source


Bandiera F.,University of Salento | Besson O.,ISAE University | Ricci G.,University of Salento
IEEE Transactions on Signal Processing | Year: 2010

We address the problem of adaptive detection of a signal of interest embedded in colored noise modeled in terms of a compound-Gaussian process. The covariance matrices of the primary and the secondary data share a common structure while having different power levels. A Bayesian approach is proposed here, where both the power levels and the structure are assumed to be random, with some appropriate distributions. Within this framework we propose MMSE and MAP estimators of the covariance structure and their application to adaptive detection using the NMF test statistic and an optimized GLRT herein derived. Some results, also in comparison with existing algorithms, are presented to illustrate the performances of the proposed detectors. The relevant result is that the solutions presented herein allows to improve the performance over conventional ones, especially in presence of a small number of training data. © 2010 IEEE. Source


Deu J.-F.,French National Conservatory of Arts and Crafts | Matignon D.,ISAE University
Computers and Mathematics with Applications | Year: 2010

A Newmark-diffusive scheme is presented for the time-domain solution of dynamic systems containing fractional derivatives. This scheme combines a classical Newmark time-integration method used to solve second-order mechanical systems (obtained for example after finite element discretization), with a diffusive representation based on the transformation of the fractional operator into a diagonal system of linear differential equations, which can be seen as internal memory variables. The focus is given on the algorithm implementation into a finite element framework, the strategies for choosing diffusive parameters, and applications to beam structures with a fractional Zener model. © 2009 Elsevier Ltd. All rights reserved. Source


Besson O.,ISAE University | Dobigeon N.,Toulouse 1 University Capitole | Tourneret J.-Y.,Toulouse 1 University Capitole
IEEE Transactions on Signal Processing | Year: 2011

We consider the problem of subspace estimation in a Bayesian setting. Since we are operating in the Grassmann manifold, the usual approach which consists of minimizing the mean square error (MSE) between the true subspace ${\mmb U}$ and its estimate $\mathhat{\mmb U}$ may not be adequate as the MSE is not the natural metric in the Grassmann manifold $GN,p , i.e., the set of $p$-dimensional subspaces in $\BBRN. As an alternative, we propose to carry out subspace estimation by minimizing the mean square distance between ${\mmb U}$ and its estimate, where the considered distance is a natural metric in the Grassmann manifold, viz. the distance between the projection matrices. We show that the resulting estimator is no longer the posterior mean of ${\mmb U}$ but entails computing the principal eigenvectors of the posterior mean of ${\mmb{UU}}T. Derivation of the minimum mean square distance (MMSD) estimator is carried out in a few illustrative examples including a linear Gaussian model for the data and Bingham or von Mises Fisher prior distributions for ${\mmb U}$. In all scenarios, posterior distributions are derived and the MMSD estimator is obtained either analytically or implemented via a Markov chain Monte Carlo simulation method. The method is shown to provide accurate estimates even when the number of samples is lower than the dimension of ${\mmb U}$. An application to hyperspectral imagery is finally investigated. © 2011 IEEE. Source


Jardin T.,ISAE University | Bury Y.,ISAE University
Journal of Fluid Mechanics | Year: 2012

We numerically investigate the influence of pulsed tangential jets on the flow past a circular cylinder. To this end a spectral-Lagrangian dual approach is used on the basis of time-series data. The analysis reveals that the flow response to unsteady forcing is driven by strong interactions between shear layers and pulsed jets. The latter preferentially lead to either the lock-on regime or the quasi-steady vortex feeding regime whether the excitation frequency is of the order of, or significantly greater than, the frequency of the natural instability. The intensity of the wake vortices is mainly influenced by the momentum coefficient through the introduction of opposite-sign vorticity in the shear layers. This feature is emphasized using a modal-based time reconstruction, i.e. by reconstructing the flow field upon a specific harmonic spectrum associated with a characteristic time scale. The quasi-steady regime exhibits small-scale counter-rotating vortices that circumscribe the separated region. In the lock-on regime, atypical wake patterns such as 2P or P + S can be observed, depending on the forcing frequency and the momentum coefficient, highlighting remarkable analogies with oscillating cylinders. © 2012 Cambridge University Press. Source

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