LOG-POLAR AND POLAR IMAGE FOR RECOGNITION TARGETS

ABSTRACT We describe in this abstract, data processing algorithms applied on radar image in order to extract feature descriptors and then to perform recognition task. Several kinds of descriptors can be used to acquire information about target characteristics from radar images such as ISAR (Inverse Synthetic Aperture Radar) images. We present in this abstract two types of vector descriptors extracted from two minds of transformed images so-called polar and log-polar images obtained respectively via the polar and log-polar mapping. In order to guarantee the invariance of some geometrical transformation, additional processing are proposed. In this paper, we present our approach to extract feature vectors obtained on the both transformed images. In the classification step, the Support Vector machine will be used in the field of radar experimentation

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

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

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

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

Robust Control of Series Active Power Filters for Power Quality Enhancement in Distribution Grids: Simulation and Experimental Validation

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.This paper presents a simulation study and an experimental implementation of a single-phase Series Active Power Filter (SAPF) for the mitigation of harmonics in the load voltage. The aim is to regulate the injection voltage of the SAPF to compensate the grid voltage via the injection transformer in addition to maintaining the load voltage stable. The control strategies investigated in this work include Backstepping Sliding Mode Control (BSMC) and a neuro-fuzzy controller based on ANFIS (Adaptive Neuro-Fuzzy Inference System) l. The proposed control strategies for the single-phase SAPF are initially evaluated in simulations under MATLAB/Simulink and then validated on a laboratory-scale hardware experimental set up consisting of a source and a single-phase SAPF. A comparative study of these controllers with respect to their performance and robustness in mitigating power quality against voltage disturbances and harmonics is presented. Both simulation and experimental results have demonstrated that ANFIS-based controller was able to achieve superior performance and a lower total harmonic distortion (THD) as compared to the other control methods.Peer reviewedFinal Accepted Versio

A Column Generation Approach for Coordination and Control of Multiple UAVs

International audienceIn this work, we consider the problem of autonomous task allocation and trajectory planning for a set of UAVs. This is a bi-level problem: the upper-level is a task assignment problem, subjected to UAV capability constraints; the lower-level constructs the detailed trajectory of UAVs, subjected to dynamics, avoidance and dependency constraints. Although the entire problem can be formulated as a mixed-integer linear program (MILP), and thus it can be solved by available software, the computational time increases intensively. For solving more efficiently this problem we propose a new approach based on the column generation method. Then, the two branching techniques will be investigated in order to obtain a branch-and-price scheme for this problem. The efficiency of our approach is evaluated by comparing with solution given by CPLEX on different scenarios

Intégration des bases de connaissances dans les systèmes d'aide à la décision (application à l'aide à la reconnaissance de cibles radar non-coopératives)

Le travail réalisé et présenté dans ce mémoire se situe à un carrefour d approche aussi variées que spécifiques dans le contexte du processus d extraction de connaissance à partir de données (ECD). Au cours du travail de recherche réalisé, les étapes du processus de reconnaissance ont été étudiées dans le contextes de la reconnaissance de cibles aériennes, depuis l acquisition des signaux radar jusqu à l aide à la prise de décision (évaluation de la reconnaissance) en passant par les techniques de l imagerie radar ISAR (Inverse Synthetic Aperture Radar). L architecture modulaire ainsi réalisée et l étude d un système expérimental d acquisition de données radar (chambre anéchoïque de l ENSIETA) ont permis une meilleure maîtrise de la qualité des données produites et de faciliter la validation de la fonction de reconnaissance semi-automatique des cibles radar utilisées et traitées. Afin d accroître le pouvoir discriminant des signaux radar, la reconstruction des images présente un outil efficace pour élaborer la tâche d aide à la décision. Cette dernière est basée sur la classification des images ISAR, notamment la recherche d images parle contenu. Dès lors, l extraction des primitives en particulier, celles de forme (contours fermés) et de structure est réalisée. Au final, la tâche de reconnaissance et d aide à la décision consisté à fournir les moyens adéquats, non seulement d interprétabilité et de validation mais aussi de contrôle et d optimisation.This Framework deals with varied and specific approaches. The methodology used to design the complete processing chain from the acquisition step and image ISAR (Opposite Synthetic Aperture Radar) reconstruction to the classification and decision making, is issued from artificial intelligence approach. This is in flowing the process of Knowledge Discovery from Data (KDD) which has been adapted to radar target recognition system. For a better control and to achieve a validation function of the assisted target recognition task, system architecture is modular-designed and the acquisition system is studied from an anechoic chamber of ENSIETA (Brest, France) in a specific environment of experimentation. Thus, in order to increase the discriminating capacity of the target signal, the ISAR imaging technics are used to give more detailed information on the target geometry and particularly to produce the helpful information for human interpretation. The decision making is based on the image classification, in particular on multimedia retrieval field. Hence, the some feature vectors are examined from shape and global target structure. Finally, The target recognition and decision-making task consists in providing the adequate means, not only of interpretability and validation but also of control and optimization.BREST-BU Droit-Sciences-Sports (290192103) / SudocSudocFranceF

Target Recognition using IFFT and MUSIC ISAR images

International audienceWe present in this paper an approach to achieve the aircraft target recognition task using Inverse Synthetic Aperture Radar (ISAR) images reconstructed using IFFT (Inverse Fast Furrier Transform) and MUSIC2D (multiple signal characterization) methods. The first goal of this work is to propose efficient features for target recognition using a polar mapping procedure, to make a polar signature, with well-designed classifier. The second goal of this work is to compare the recognition rate using the both reconstruction methods. Finally, the obtained results are given and compared for the two types ISAR images in order to validate our proposed feature vectors and retrieval scheme

Corrélation optique pour l’identification de cibles radar

International audienceCe présent travail s’intègre dans la problématique générale du traitementet de l’exploitation de l’information dans le domaine de la reconnaissanceet l’identification de cibles aériennes à partir des images radar. Nous nous intéressonsdans ce travail aux méthodes de corrélation optique, particulièrement àla corrélation monovoie et la corrélation multivoie utilisées pour l’identificationd’objets. Ces différentes techniques de corrélation optique se distinguent généralementpar la nature des filtres qu’elles proposent. Ces derniers sont utiliséspour réaliser la corrélation optique et ainsi la phase de reconnaissance. L’objectifde ce travail est de proposer une méthode de reconstruction de filtre optiquecaractérisant l’information contenue dans une ou plusieurs images radar en assurantune taille réduite de la base d’images utilisée comme base de références(base d’apprentissage). Pour le critère de corrélation et ainsi la prise de décision(classification), nous proposons une mesure de similarité basée sur un critèreénergétique défini par la somme d’énergie maximale normalisée dans le plan decorrélation utilisé