Target detection with HFSW radar : use of curvelets and continuous wavelets

La surveillance du trafic maritime nécessite de disposer de systèmes efficaces permettant de détecter et de suivre des cibles diverses en continu jusqu'aux limites de la Zone Economique Exclusive (ZEE - 200 Milles Marins). Les radars Hautes-Fréquences à ondes de surface (HFSW) sont de bons candidats pour répondre à ces exigences. Cependant les images obtenues en sortie de ces systèmes sont fortements perturbuées pour des applications de détection. Dans cette contribution nous proposons d'utiliser d'une part la transformée en curvelets pour supprimer certains aspects limitants dans l'image. D'autre part, de baser notre méthode de détection sur les ondelettes continues (appliquées à l'image pré-traitée par les curvelets)

Analysis of simulated reflected L-band signals from a sea surface using time-frequency representations.

International audienceThe study proposed in this paper deals with the analysis, in the time-frequency domain, of a L-band signal reflected by an time-evolving sea surface. The final goal of this project is to evaluate the technical potential of passive GNSS based systems to estimate oceanographic parameters. With this purpose in mind, this paper presents the experimental setup and describes the physical modeling applied to generate numerical simulations. We put more particular stress on the time-frequency representation of the signal received by an observer above the time-evolving sea surface. Finally, physical interpretations of the features obtained in the time-frequency domain from these simulated signals are proposed

Ship detection based on morphological component analysis of high-frequency surface wave radar images

WOSInternational audienceIn this study, high-frequency surface wave radar (HFSWR) is considered for target detection. These systems, commonly used for oceanographic purposes, are of interest in maritime surveillance because of their long range detection capabilities compared with conventional microwave radar. Unfortunately, the received signals are strongly polluted by different noises. In this contribution a target detection method based on morphological component analysis (MCA) is investigated. Basically, MCA is a source separation technique based on multiscale transforms and the sparsity representation. The authors goal is to extract the target signatures from the range-Doppler image and then to take the final decision through a simple rule. This study introduces the issue of ship detection from HFSWR images and gives an overview of the MCA approach. Then, the algorithm used for target detection is depicted. Comparisons with a classical constant false-alarm rate (CFAR) detection method, the so-called greatest of cell averaging-CFAR, are given through receiver operating characteristic curves computed from simulated data

Target detection based on morphological component analysis of HFSWR images for maritime surveillance

International audienceTo supplement actual systems (like the Automatic Identification System - AIS) to monitor the maritime traffic in given areas within the Exclusive Economic Zone (EEZ - 200nm), High Fre- quency Surface Wave (HFSW) radar seem to be good candidates. Indeed recent works [1, 2] show that they can provide useful informations even if the spatial and temporal resolutions are weaks. A global surveillance system of the EEZ and harbor could also combine HFSW radar for the long range detection and X-band radar for short ranges (<30km). These last systems are more suitable for near area, which are more vulnerable, since they provide good spatial and temporal resolutions. HFSW radars have been efficiently used these last three decades to remotely measure oceano- graphic parameters. They can provide surface currents, wave spectra, wind intensities and di- rections. In this contribution, as already introduced, these systems are considered for traffic surveillance. The used system is a WEllen RAdar (WERA), with a central frequency between 12 and 13MHz, which provides Range-Doppler (RD) images to be processed for the detection part. These RD images, for the detection purpose, are strongly polluted by sea clutter and other interference leading to a challenging problem. In the paper, a short review of previous works about simulating HFSW images [3] and the validation against real data will be given. Then the image processing approach will be detailed. The proposed method is based on the Morphological Component Analysis (MCA) [5, 6]. Due to HF image features, the method had required some adaptations and some first results have been already given in a previous work [4]. In this contribution, some new modifications of the MCA algorithm are introduced in order to enhance the extraction of the target signatures. The new algorithm includes some processing during the iterative MCA process against the multiscale coefficients (this will be detailed in the full paper). Some results from simulated and real data will be display to illustrate the efficiency of the proposed method

Evaluating GNSS Signals for Passive Local Sea State Monitoring

International audiencePassive remote sensing based upon electromagneticsources of opportunity provides the promise of a usefultool for monitoring natural environments and especiallymarine environments [1], [2], [3]. In this context, the GlobalNavigation Satellite Systems GNSS (GPS,...) appear as oneof the most relevant solution since the emitted signals (in LBand)are reliable, available all over the world, deterministicand perfectly known.In this study, we propose to analyze and to assess thepossibility of sensing the sea wave movements using thereflected L-Band signals (see figure 1). Thus, based uponnumerical simulations of the electromagnetic scattered field(Method of Moments), we investigate the connections betweena time evolving surface and the features in the timefrequency(TF) domain of the signal scattered by this surface(using Wigner-Ville transform). The idea is to take advantageof these representations to extract from Doppler and micro-Doppler signatures the oceanographic parameters of interes

Résolution de problèmes inverses non linéaires (applications en imagerie à ondes électromagnétiques)

CACHAN-ENS (940162301) / SudocSudocFranceF

Identification of the electromagnetic scattering by dynamic sea surfaces with a stochastic differential equation model

International audienc

Bayesian parameter estimation for asymmetric power distributions

International audienceThis paper proposes a hierarchical Bayesian model for estimating the parameters of asymmetric power distributions (APDs). These distributions are defined by shape, scale and asymmetry parameters which make them very flexible for approximating empirical distributions. A hybrid Markov chain Monte Carlo method is then studied to sample the unknown parameters of APDs. The generated samples can be used to compute the Bayesian estimators of the unknown APD parameters. Numerical experiments show the good performance of the proposed estimation method. An application to an image segmentation problem is finally investigated

Détection de cibles en milieu maritime par radar HF à ondes de surface

Les portées atteintes par les radars HF (3-30MHz) à ondes de surface sont largement supérieures à celles des radars classiques de surveillance maritime qui sont limités par la ligne de l horizon. Les systèmes radar HF permettent ainsi la surveillance d une très large zone en continu et à moindre coût. Mais différentes raisons rendent le signal reçu difficile à traiter : une faible résolution temporelle et spatiale, et surtout une prédisposition à subir différents types d interférences et de bruits. C est dans ce contexte de traitement et de détection à partir des signaux fortement bruités que s inscrivent les motivations initiales des travaux réalisés dans le cadre de ces travaux de thèse. Le premier travail réalisé porte sur le développement et la mise en place d une méthodologie permettant de générer des images radar avec un système à ondes de surface (image distance-Doppler). Cet outil est développé en tenant compte de l ensemble des paramètres de la chaîne, il permet notamment de maîtriser diverses entrées comme les paramètres maritimes (hauteurs significatives, direction des vagues dominantes...), les paramètres radar (fréquence d émission, largeur de bande...), mais aussi différentes caractéristiques des cibles considérées (surface efficace radar, distance radar, vitesse, ...). Sur le volet détection de cibles à partir des images générées ou réelles, une des solutions développée et proposée dans ce manuscrit consiste dans l application de l Analyse en Composante Morphologique (MCA), une des techniques de séparation de sources proposée initialement par Starck et al. 2005. Cette approche nous permet d extraire une image contenant les signatures des cibles. La prise de décision finale est alors basée sur une technique classique dite a taux de fausse alarme constant. La méthodologie suivie dans ce travail de thèse pour mettre en place et évaluer cette application (surveillance maritime) combine à la fois des aspects de modélisation, de simulation, et d utilisation de données réelles.High Frequency Surface Wave (HFSW) radars (3-30MHz) provide largely better ranges than classical costal systems used for sea monitoring. HFSW radars make possible to get range further than line of sight and to monitor continuously and cheaply a large area of sea. But for different reason it is hard to process the received signal: a low spatial and temporal resolution and a strong tendency to be polluted by different kind of interferences and noises. The general topic of this PhD thesis is to deal with a very noisy signal for detection purpose. The first work deals with the development of a methodology to generate images obtained by HSWR radars (Range-Doppler images). This tool was developed taking into account most of the system parameters and leads to control entries parameters for the sea (wave height, wave directions...), the radar (central frequency, bandwidth..J, and target characteristics (radar cross section, range, velocity...). For the detection task, from generated and real data, a source separation techniques called Morphological Component Analysis (MCA), initially proposed by Starck et al. 2005, is considered. The goal is to extract an image made of the target signature, the decision part being solved by using a common constant false alarm rate method. The selected methodology in this thesis in order to solve the detection problem by HFSW radars combines modeling and simulation aspects, and real data use.BREST-BU Droit-Sciences-Sports (290192103) / SudocBREST-ENSIETA Médiathèque (290195201) / SudocSudocFranceF