Estimation d'un mélange de distributions alpha-stables à partir de l'algorithme EM

International audienceLe modèle Gaussien est souvent utilisé dans de nombreuses applications. Cependant, cette hypothèse est réductrice. Par exemple, il est possible que les données fournies par des capteurs ne soient pas symétriques et/ou présentent une décroissance rapide au niveau de la queue de la distribution. De plus, il est rare que la densité de probabilité représentant les données soit unimodale. Il existe des algorithmes permettant l'estimation d'un mélange de distributions. L'algorithme Espérance-Maximisation (EM) permet entre autre d'estimer un mélange de distributions Gaussiennes. Nous proposons dans ce papier d'étendre l'algorithme EM pour estimer un mélange de distributions α-stables. Un des objectifs futurs de ce papier est d'appliquer la notion de fonctions de croyance continues sachant que les informations fournies par les sources peuvent être modélisées par un mélange de densité de probabilité α-stables

Segmentation d'images sonar par matrice de co-occurrence

National audienceL'automatisation de la détection et de l'identification des objets reposant sur les fonds marin est réalisée grâce à une analyse de la forme de l'ombre acoustique que produise ces objets sur les images sonar. Par conséquent, une première étape de segmentation s'avère indispensable car elle conditionne la qualité de l'interprétation qui va suivre. A cette fin, nous proposons dans ce papier d'effectuer une segmentation de l'image sonar en 2 classes : le fond et l'ombre de l'objet. La méthode adoptée pour segmenter ce type d'image repose sur l'analyse statistique de la texture couplée à une classification par la méthode de k-means. Ceci consiste, dans un premier temps, à faire parcourir l'image sonar par une fenêtre glissante, et à calculer les paramètres de texture en chaque pixel, en utilisant une méthode d'extraction des caractéristiques comme les matrices de co-occurrence, afin d'obtenir les vecteurs caractéristiques de l'image. La répartition des ces vecteurs en utilisant l'algorithme de k-means en 2 classes permet de classer les pixels de l'image comme faisant partie ou non de l'ombre produit par l'objet

ISAR Image formation with a combined Empirical Mode Decomposition and Time-Frequency Representation

International audienceIn this paper, a method for Inverse Synthetic Aperture Radar (ISAR) image formation based on the use of the Complex Empirical Mode Decomposition (CEMD) is proposed. The CEMD [1] which based on the Empirical Mode Decomposition (EMD) is used in conjunction with a Time-Frequency Representation (TFR) to estimate a 3-D time-range-Doppler Cubic image, which we can use to effectively extract a sequence of ISAR 2-D range-Doppler images. The potential of the proposed method to construct ISAR image is illustrated by simulations results performed on synthetic data and compared to 2-D Fourier Transform and TFR methods. The simulation results indicate that this method can provide ISAR images with a good resolution. These results demonstrate the potential application of the proposed method for ISAR image formation

Continuous belief functions and α-stable distributions

International audienceThe theory of belief functions has been formalized in continuous domain for pattern recognition. Some applications use assumption of Gaussian models. However, this assumption is reductive. Indeed, some data are not symmetric and present property of heavy tails. It is possible to solve these problems by using a class of distributions called α-stable distributions. Consequently, we present in this paper a way to calculate pignistic probabilities with plausibility functions where the knowledge of the sources of information is represented by symmetric α-stable distributions. To validate our approach, we compare our results in special case of Gaussian distributions with existing methods. To illustrate our work, we generate arbitrary distributions which represents speed of planes and take decisions. A comparison with a Bayesian approach is made to show the interest of the theory of belief functions

Experimental Measurement of Time Difference Of Arrival

International audienceIn this paper is described an experimental passive localization system based on SDR (Software Defined Radio) components. This system is designed to measure Time Differences of Arrival (TDOA) of radar pulses between two platforms. For a TDOA system, time error between the two receivers must be kept very low, which requires a very accurate way to synchronize the time bases. In this purpose, a custom offline synchronization method is proposed. The overall performances of the system are analyzed. In a small scale outdoor experiment, it has been shown to perform TDOA measurements accurately. The performances measured during this experiment are then extrapolated to a more realistic electronic warfare scenario

A Column Generation Based Label Correcting Approach for the Sensor Management in an Information Collection Process

International audienceThis paper deals with problems of sensor management in a human driven information collection process. This applicative context results in complex sensor-to-task assignment problems, which encompass several difficulties. First of all, the tasks take the form of several information requirements, which are linked together by logical connections and priority rankings. Second, the assignment problem is correlated by many constraint paradigms. Our problem is a variant of Vehicle Routing Problem with Time Windows (VRPTW), and it also implements resource constraints including refuelling issues. For solving this problem, we propose a column generation approach, where the label correcting method is used to treat the sub-problem. The efficiency of our approach is evaluated by comparing with solution given by CPLEX on different scenarios

Une méthode de génération de colonnes pour la planification des capteurs dans un processus de collecte d'informations

National audienceDans ce papier, nous considérerons le problème de la planification de capteurs sous le contrôle d'équipes humaines. Le processus de planification est divisé en deux étapes : la première donne lieu à une définition du problème d'affectation par une déclinaison de demandes informelles en requêtes formalisées et conditionnées par des contraintes logiques, des contraintes de trajectoire des moyens d'acquisition et des contraintes temporelles ; la seconde correspond à la réalisation effective du processus d'optimisation. Dans ce type de planification, l'interaction humaine avec le processus d'optimisation est fondamentale

ISAR imaging Based on the Empirical Mode Decomposition Time-Frequency Representation

International audienceThis work proposes an adaptation of the Empirical Mode Decomposition Time-Frequency Distribution (EMD-TFD) to non-analytic complex-valued signals. Then, the modified version of EMD-TFD is used in the formation of Inverse Synthetic Aperture Radar (ISAR) image. This new method, referred to as NSBEMD-TFD, is obtained by extending the Non uniformly Sampled Bivariate Empirical Mode Decomposition (NSBEMD) to design a filter in the ambiguity domain and to clean the Time-Frequency Distribution (TFD) of signal. The effectiveness of the proposed scheme of ISAR formation is illustrated on synthetic and real signals. The results of our proposed methods are compared to other Time-Frequency Representation (TFR) such as Spectrogram, Wigner-Ville Distribution (WVD), Smoothed Pseudo Wigner-Ville Distribution (SPWVD) or others methods based on EMD

Influence de l'estimation des paramètres de texture pour la classification de données complexes

National audienceThis paper shows a classification of data based on the theory of belief functions. The complexity of this problem can be seen as two ways. Firstly, data can be imprecise and/or uncertain. Then, it is difficult to choose the right model to represent data. Gaussian model is often used but is limited when data are complex. This model is a particular case of α-stable distributions. Classification is divided into two steps. Learning step allows to modelize data by a mixture of α-stable distributions and Gaussian distributions. Test step allows to classify data with the theory of belief functions and compare the two models. The classification is realized firstly on generated data and then on real data type sonar images