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A novel image representation of GNSS correlation for deep learning multipath detection
No full textInternational audienceThis paper proposes a novel framework for multipath prediction in Global Navigation Satellite System (GNSS) signals. The method extends from dataset generation to deep learning inference through Convolutional Neural Network (CNN). The process starts at the output of the correlation stage of the GNSS receiver. Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into grey scale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a CNN for automatic feature construction and multipath pattern detection. The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed. The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for C/N0 ratio as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution. Its performance is also measured and compared with an alternative Support Vector Machines (SVM) technique. The results show the undeniable superiority of the proposed CNN algorithm over the SVM benchmark
Is it time for passive Brain Computer Interfaces in UAV Operations ?
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Antennes à Résonateur Diélectrique Multibande fabriquées en Céramique Inhomogène et Anisotropique par Impression 3D
No full textAdditive manufacturing, or simply three-dimensional (3D)-printing, has been playing an important role in different fields due to its rapid manufacturing, energy savings, customization, and material waste reduction, to name a few. When it comes to antenna applications, it turns out that most of the dielectric-based 3D-printed solutions proposed in the literature deal with non-resonant and large structures in comparison to the wavelength. More recently, the possibility of using ceramics as printing material opened new possibilities for the design of small and resonant structures due to the higher dielectric constant. In this context, the main goal of this work is to show the possibility of locally controlling the inhomogeneity and anisotropy of a dielectric. To demonstrate it, multi-band dielectric resonator antennas (DRAs) are proposed to achieve circular polarization and/or specific radiation patterns by manipulating the electric permittivity of the dielectric. At first, an original approach to design a dual-band and circularly polarized DRA is presented. The circular polarization is achieved only due to the manipulation of the permittivity of the dielectric instead of using complex feeding techniques and/or using complex dielectric shapes as most of the works found in the literature. The results demonstrate the efficiency of the proposed model and a design guideline for this type of antenna is presented. Besides, an extension to a triple-band antenna is proposed with the two lower bands presenting circular polarization and broadside radiation pattern, while the upper bandhas linear polarization and omnidirectional pattern. Both antennas require an assembly of anisotropic and isotropic dielectrics with specific values of permittivity to achieve the desired results. However, not necessarily, these materials with these characteristics would be available. To overcome this issue, periodically arranged sub-wavelength cells allow to create artificial and heterogeneous media by controlling their effective permittivity. For this purpose, only zirconia is used as printing material in a single manufacturing process using 3D printing technology.La fabrication additive, ou simplement l’impression tridimensionnelle (3D), joue un rôle important dans différents domaines en raison de sa rapidité de fabrication, des économies d’énergie, de la personnalisation et de la réduction des déchets de matériaux, par exemple. En ce qui concerne lesapplications d’antennes, il s’avère que la plupart des solutions imprimées en 3D à base de diélectrique proposées dans la littérature portent sur des structures non résonantes et de grande taille par rapport à la longueur d’onde. Plus récemment, la possibilité d’utiliser la céramique comme matériau d’impression a ouvert de nouvelles possibilités pour la conception de structures petites et résonantes en raison de la constante diélectrique plus élevée. Dans ce contexte, l’objectif principal de ce travail est de montrer la possibilité de contrôler localement l’inhomogénéité et l’anisotropie d’un diélectrique. Pour le démontrer, des antennes à résonateur diélectrique (DRAs) multibandes sont proposées pour obtenirpolarisation circulaire et/ou des diagrammes de rayonnement spécifiques en manipulant la permittivité électrique du diélectrique. Dans un premier temps, une approche originale pour concevoir un DRA bi-bande à polarisation circulaire est présentée. La polarisation circulaire est obtenue uniquement grâce à la manipulation de la permittivité du diélectrique au lieu d’utiliser des techniques d’alimentation complexes et/ou d’utiliser des formes diélectriques complexes comme la plupart des travaux trouvés dans la littérature. Les résultats démontrent l’efficacité du modèle proposé et un guide de conception pour ce type d’antenne est présenté. En outre, une extension à une antenne à trois bandes est proposée, les deux bandes inférieures présentant polarisation circulaire et un diagramme de rayonnement large, tandis que la bande supérieure a une polarisation linéaire et un diagramme omnidirectionnel. Les deux antennes nécessitent un assemblage de diélectriques anisotropes et isotropes avec des valeursspécifiques de permittivité pour obtenir les résultats souhaités. Cependant, ces matériaux présentant ces caractéristiques ne sont pas nécessairement disponibles. Pour surmonter ce problème, des motifs sub-longueur d’onde agencés périodiquement permettent de créer des milieux artificiels et hétérogènes en contrôlant leur permittivité effective. Pour cela uniquement la zircone est utilisée comme matériaud’impression en un seul procédé de fabrication en utilisant la technologie d’impression 3D
Impact of Terrestrial Emitters on Civil Aviation GNSS Receivers
No full textInternational audienceGNSS radio frequency interferences (RFI) sources are often classified in two categories in the civil aviation community: aeronauticaland non-aeronautical RFI. Aeronautical RFI sources gather systems with an aeronautical radio navigation system (ARNS) frequencyallocation which radiate in or near the GNSS band and consequently affectthe GNSS performance. Non-aeronautical sources includesystems with no ARNS frequency allocation also radiating in the GNSS band, either voluntarily or involuntarily. To preciselyestimate the impact of RFI sources on GNSS receiver has one main stake. Indeed, it allows to assess the GNSS receiver capabilityto meet minimum International Civil Aviation Organization (ICAO) performance objectives in nominal RFI environment or in nonnominal RFI environment (during jamming operations for instance)
Air Traffic Flow Representation and Prediction using Transformer in Flow-centric Airspace
No full textInternational audienceThe air traffic control paradigm is shifting from sector-based operations to cross-border flow-centric approaches to overcome sectors' geographical limits. Under the flow-centric paradigm, prediction of the traffic flow at major flow intersections, defined as flow coordination points in this paper, may assist controllers in coordinating intersecting traffic flows which is the main challenge for implementing flow-centric concepts. This paper proposes to predict the flow at coordination points through a transformer neural network model. Firstly, the flow coordination points, i.e., the major flow intersections, are identified by hierarchical clustering of flight trajectory intersections whose location and connectivity characterize daily traffic flow patterns as a graph. The number of coordination points is optimized through graph analysis of the daily flow pattern evolution. Secondly, air traffic flow features in the airspace during a period are described as a "paragraph" whose "sentences" consist of the time and callsign sequences of flights transiting through the identified coordination points. Finally, a transformer neural network model is adopted to learn the sequential flow features and predict the future number of flights passing the coordination points. The proposed method is applied to French airspace based on one-month ADS-B data (from Dec 1, 2019, to Dec 31, 2019), including 158,856 flights. Results show that the proposed prediction model can approximate the actual flow values with a coefficient of determination (R 2) between 0.909 to 0.99 and a mean absolute percentage error (MAPE) varying from 27.4% to 11.7% with respect to a 15-minute to 2-hour prediction window. The sustainability of the prediction accuracy under an increasing prediction window demonstrates the potential of the proposed model for longer-term flow prediction
Evaluation of drag coefficient for a quadrotor model
No full textInternational audienceThis paper focuses on the quadrotor drag coefficient model and its estimation from flight tests. Precise assessment of such a model permits the use of a quadrotor as a sensor for wind estimation purposes without the need for additional onboard sensors. Firstly, the drag coefficient has been estimated in a controlled environment via wind generator and motion capture system.Later, the evolution of the coefficient is observed for various mass and fuselage shapes. Finally, an estimation method is proposed, based on the leastsquares optimization, that evaluates the drag of the quadrotor directly from outdoor flight data. The latter leads the methodology towards an easier adoption in other researchers' systems without the need for complex and expensive flight testing facilities. The accuracy of the proposed method is presented both in simulation, based on a realistic flight dynamics model, and also for real outdoor flights
Predicting Passenger Flow at Charles De Gaulle Airport using Dense Neural Networks
No full textInternational audienceSecurity checking is a major issue in airport operations. Affecting the correct number of security agents is essential to provide a good quality of service to passengers while providing the best security performances. At Paris Charles de Gaulle airport the affectation of security agents is decided at strategical level, more than a month in advance. The key element to determine the number of agents needed is the passenger flow through the security checkpoints. This flow is correlated to the passenger flow in the different boarding rooms. This paper investigates the interest of small dense neural networks to perform passenger flow prediction at strategical level for Paris Charles de Gaulle airport. A dense neural network has been trained to predict the passenger flow for each boarding room of the airport. The network has been compared to a more complex long short-term memory model in terms of mean absolute error and outperformed a mathematical model based on exponentially modified Gaussian distribution
EOG-Based Human–Computer Interface: 2000–2020 Review
No full textInternational audienceElectro-oculography (EOG)-based brain–computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users’ intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user’s communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user’s intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries
A Queuing Network Model of a Multi-Airport System Based on Point-Wise Stationary Approximation
No full textInternational audienceA multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong–Hong Kong–Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS
Flight Rescheduling to Improve Passenger Journey during Airport Access Mode Disruptions
No full textInternational audienceDisruptions on airport access mode impact the passenger journey. This paper shows that the impact can be mitigated with a modest tactical rescheduling of flights. Operational constraints related to connecting flights, minimum turnaround time, runway throughput limitations, terminal and taxi network capacities are considered. In order to solve this optimization problem, we implement a simulated annealing coupled with a simulation-based evaluation and a sliding time window. We propose a data-driven approach to simulate the passenger arrival process at the airport. The coordination mechanism has been evaluated on several scenarios with different levels of disruption. New flight schedules and runway assignments obtained after optimization succeed in reducing up to 70% the number of stranded passengers at the airport by only assigning on average a 6-minute delay to the flight set