Particle optimization with metamodel for crack characterization

International audienceA new inverse method for real time eddy current testing is introduced. It is based on particle swarm optimization coupled with a metamodel. This metamodel is generated by interpolating data from an adaptive database. Linear and radial basis function interpolations are compared as means to generate the metamodel. The inverse method gives a result combined with an approximation of the likelihood function to help for decision analysis. It is finally compared to a state-of-art inverse method using support vector machine on a 3D case on simulated data with very promising results

Bases de données séquentielles et méthodes inverses appliquées au CND par courants de Foucault.

1pLe but de cette étude est de développer une méthode d'optimisation capable, en temps réel, de donner un résultat fiable de Contrôle Non Destructif par Courants de Foucault (CND-CF). On utilise des bases de données séquentielles à partir desquelles il est possible de générer des données par interpolation tout en maîtrisant l'erreur maximale d'interpolation. Ces bases de données sont ensuite utilisées avec des méthodes d'optimisation afin de résoudre le problème de CND. D'une part, elles servent à générer rapidement des signaux utilisés par des méthodes d'optimisation particulaires, ici le PSO (Particle Swarm Optimizer). Cela permet d'accélérer considérablement la méthode PSO avec en contrepartie une perte en précision due à l'interpolation. D'autre part, elles sont utilisées comme bases d'apprentissage pour une méthode d'inversion par SVM (Support Vectors Machines). On obtient ainsi deux méthodes rapides, l'une particulaire et l'autre par apprentissage, permettant de mesurer les dimensions des défauts que l'on désire retrouver. Des premiers résultats sont exposés et les deux méthodes d'inversion sont comparées

Comparisons of discrete and continuous propagators for the modelling of low tropospheric propagation

International audienceFor modelling the propagation of electromagnetic waves in the low troposphere, the discrete mixed Fourier method is classically used. It is based on a finite-difference approximation of the ground boundary condition. However, the propagator of the split-step Fourier method is derived from continuous equations. In this paper, we apply the finite-difference approximation to the propagation equation. A discrete operator is derived from these discrete equations. The continuous and discrete propagators are compared. The accuracy of these propagators is numerically analysed in free space and over an impedance ground. We show that the discrete propagator avoids some numerical instabilities

Nanosatellite activities at ENAC: the TETX receiving ground station and the EYESAT mission control center

International audienceThis paper describes the activities of ENAC (École Nationale de l’Aviation Civile), Toulouse, around the nanosatellites. Firstly, the TETX (Telemetry Earth Terminal X‐band) receiving antenna and itsaccompanying satellite signal processing unit are presented. Although educationally oriented, they constitute a fully operational ground station. Secondly, the mission control center dedicated to theEYESAT nanosatellite is described. Its development is also an important source of interesting educational project

Modeling the long-range wave propagation by a split-step wavelet method

International audienceA split-step wavelet method for simulating the long-range wave propagation is introduced. It is based on the fast wavelet transform. Compared to the split-step Fourier method, this method improves the computation efficiency while keeping a good accuracy. The propagation is performed iteratively by means of a pre-computed matrix containing the individual propagations of the wavelets. A fast computation method of this matrix is also presented. For the radiowave propagation in the low troposphere, a local image method is proposed to account for an impedance ground. Inhomogeneous atmospheres and irregular grounds are also considered. Finally, numerical tests of long-range propagations are performed to show the accuracy and time efficiency of this method

Probabilistic VOR error due to several scatterers — Application to wind farms

International audienceThis paper introduces a method to calculate the VOR error due to multipaths from several known scatterers within known quantiles. In such a configuration, the amplitudes of the multipaths can be numerically or analytically calculated, whereas their phases are modelled as uniformly distributed. A probabilistic formulation of the VOR error that overestimates its variance is introduced to obtain the quantiles. The method is useful to obtain probabilities of occurrence of large VOR errors for multiple configurations and in a short computation time. Examples with wind farms are presented

Methodology for Assessing the Impact of Wind Turbines on Civil Aviation Primary and Secondary Radars

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A Stastistical Model for Assessing the Impact of Wind Turbines on Conventionnal VOR

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A Dynamic VOR Receiver Model for Estimating the Bearing Error in the Presence of Wind Turbines

International audienceThis work introduces a dynamic VOR receiver model for estimating the bearing error in the presence of wind turbines. The receiver processes time series generated by an electromagnetic simulation tool that takes into account the multipaths. This global model can reproduce the response of a VOR receiver on a realistic aircraft trajectory. The receiver is tested in a dynamic scenario where the multipaths change rapidly with time

A duct mapping method using least squares support vector machines

International audienceThis paper introduces a “refractivity from clutter” (RFC) approach with an inversion method based on a pregenerated database. The RFC method exploits the information contained in the radar sea clutter return to estimate the refractive index profile. Whereas initial efforts are based on algorithms giving a good accuracy involving high computational needs, the present method is based on a learning machine algorithm in order to obtain a real-time system. This paper shows the feasibility of a RFC technique based on the least squares support vector machine inversion method by comparing it to a genetic algorithm on simulated and noise-free data, at 1 and 5 GHz. These data are simulated in the presence of ideal trilinear surface-based ducts. The learning machine is based on a pregenerated database computed using Latin hypercube sampling to improve the efficiency of the learning. The results show that little accuracy is lost compared to a genetic algorithm approach. The computational time of a genetic algorithm is very high, whereas the learning machine approach is real time. The advantage of a real-time RFC system is that it could work on several azimuths in near real time