Atmospheric mountain waves form in the lee of mountainous terrain under appropriate conditions of the vertical structure of wind speed and atmospheric stability. Trapped lee waves can extend hundreds of kilometers downwind from the mountain range, and they can extend tens of kilometers vertically into the stratosphere. Mountain waves are of importance in meteorology as they affect the general circulation of the atmosphere, can influence the vertical structure of wind speed and temperature fields, produce turbulence and downdrafts that can be an aviation hazard, and affect the vertical transport of aerosols and trace gasses, and ozone concentration. Sailplane pilots make extensive use of mountain lee waves as a source of energy with which to climb. There are many sailplane wave flights conducted every year throughout the world and they frequently cover large distances and reach high altitudes. Modern sailplanes frequently carry flight recorders that record their position at regular intervals during the flight. There is therefore potential to use this recorded data to determine the 3D wind velocity at positions on the sailplane flight path. This would provide an additional source of information on mountain waves to supplement other measurement techniques that might be useful for studies on mountain waves. The recorded data are limited however, and determination of wind velocities is not straightforward. This thesis is concerned with the development and application of techniques to determine the vector wind field in atmospheric mountain waves using the limited flight data collected during sailplane flights. A detailed study is made of the characteristics, uniqueness, and sensitivity to errors in the data, of the problem of estimating the wind velocities from limited flight data consisting of ground velocities, possibly supplemented by air speed or heading data. A heuristic algorithm is developed for estimating 3D wind velocities in mountain waves from ground velocity and air speed data, and the algorithm is applied to flight data collected during “Perlan Project” flights. The problem is then posed as a statistical estimation problem and maximum likelihood and maximum a posteriori estimators are developed for a variety of different kinds of flight data. These estimators are tested on simulated flight data and data from Perlan Project flights.

Cette thèse s’inscrit dans le cadre du projet "Petits drones dans le vent" porté par le centre ONERA de Lille. Ce projet vise à utiliser le drone comme "capteur du vent" pour gérer un quadcopter UAV dans des conditions aérologiques perturbées en utilisant une prédiction du champ de vent. Dans ce contexte, le but de la thèse est de faire du quadcopter un capteur de vent pour fournir des informations locales afin de mettre à jour le système de navigation. Grâce à l’estimation du vent à bord en temps réel, le quadcopter peut calculer une planification de trajectoire évitant les zones dangereuses et le contrôle de trajectoire correspondant basé sur une cartographie existante et doté des informations relatives au concernant le comportement aérodynamique de l’écoulement d’air à proximité des obstacles. Ainsi, les résultats de cette thèse, dont les objectifs principaux portent sur l’estimation du vent instantanée et le contrôle de position, seront fusionnés avec une autre étude traitant de la planification de trajectoire. Un problème important est que les capteurs de pression, tels que l’aéroclinomètre et le tube de Pitot, ne sont pas facilement utilisables à bord des véhicules à voilure tournante car l’entrée des rotors interfère avec le flux atmosphérique et les capteurs LIDAR légers généralement ne sont pas disponibles. Une autre approche pour estimer le vent consiste à mettre en œuvre un logiciel d’estimation (ou un capteur intelligent). Dans cette thèse, trois estimateurs de ce type sont développés en utilisant l’approche du mode glissant, basée sur un modèle de drone adéquat et des mesures disponibles sur le quadcopter et sur des systèmes de position de suivi inertiel. Nous nous intéressons ensuite au contrôle de la trajectoire également par mode glissant en considérant le modèle non linéaire du quadcopter. Nous étudions par ailleurs de façon encore assez préliminaire une solution alternative fondée sur la commande H, en considérant le modèle linéarisé pour différents points d’équilibre en fonction de la vitesse du vent. Les algorithmes de contrôle et d’estimation sont strictement basés sur le modèle détaillé du quadcopter, qui met en évidence l’influence du vent / This thesis is part of the project "Small drones in the wind" carried by the ONERA center of Lille. This project aims to use the drone as a "wind sensor" to manage a UAV quadrotor in disturbed wind conditions using wind field prediction. In this context, the goal of the thesis is to make the quadrotor a wind sensor to provide local information to update the navigation system. With real-time on-board wind estimation, the quadrotor can compute a trajectory planning avoiding dangerous areas and the corresponding trajectory control, based on anexisting cartography and information on the aerodynamic behavior of airflow close to obstacles. Thus, the results of this thesis, whose main objectives are to estimate instant wind and position control, will be merged with another study dealing with trajectory planning. An important problem is that pressure sensors, such as the aeroclinometer and the Pitot tube, are not usable in rotary-wing vehicles because rotors air inflow interferes with the atmospheric flow and lightweight LIDAR sensors generally are not available. Another approach to estimate the wind is to implement an estimation software (or an intelligent sensor). In this thesis, three estimators are developed using the sliding mode approach, based on an adequate drone model, available measurements on the quadrotor and inertial tracking position systems. We are then interested in the control of the trajectory also by sliding mode considering the nonlinear model of the quadrotor. In addition, we are still studying quite an early alternative solution based on the H control, considering the linearized model for different equilibrium points as a function of the wind speed. The control and estimation algorithms are strictly based on the detailed model of the quadrotor, which highlights the influence of the wind

Autonomous miniature air vehicles (MAVs) are becoming increasingly popular platforms for the collection of data about an area of interest for military and commercial applications. Two challenges that often present themselves in the process of collecting this data. First, winds can be a significant percentage of the MAV's airspeed and can affect the analysis of collected data if ignored. Second, the majority of MAV's video is transmitted using RF analog transmitters instead of the more desirable digital video due to the computational intensive compression requirements of digital video. This two-part thesis addresses these two challenges. First, this thesis presents an innovative method for estimating the wind velocity using an optical flow sensor mounted on a MAV. Using the flow of features measured by the optical flow sensor in the longitudinal and lateral directions, the MAV's crab-angle is estimated. By combining the crab-angle with measurements of ground track from GPS and the MAV's airspeed, the wind velocity is computed. Unlike other methods, this approach does not require the use of a “varying” path (flying at multiple headings) or the use of magnetometers. Second, this thesis presents an efficient and effective method for video compression by drastically reducing the computational cost of motion estimation. When attempting to compress video, motion estimation is usually more than 80% of the computation required to compress the video. Therefore, we propose to estimate the motion and reduce computation by using (1) knowledge of camera locations (from available MAV IMU sensor data) and (2) the projective geometry of the camera. Both of these methods are run onboard a MAV in real time and their effectiveness is demonstrated through simulated and experimental results.

For optical propagation through the turbulent atmosphere, the angle of arrival (AOA) cross-correlation function obtained from two spatially separated light sources carries information regarding the transverse wind velocity averaged along the propagation path. Two methods for the retrieval of the beam transverse horizontal wind velocity, v_t, based on the estimation of the time delay to the peak and the slope at zero lag of the AOA cross-correlation function, are presented. Data collected over a two week long experimental campaign conducted at the Boulder Atmospheric Observatory (BAO) site near Erie, CO was analyzed. The RMS difference between 10 s estimates of v_t retrieved optically, and 10 s averages of the transverse horizontal wind velocity measured by an ultrasonic anemometer, was found to be 14 cm/s for the time-delay-to- peak method and 20 cm/s for the slope-at-zero-lag method, for a 2 h period beginning 0345 MDT on 16 June, 2010, during which the transverse horizontal wind velocity varied between -1 m/s and 2 m/s.

Cette thèse s'inscrit dans l'amélioration de la gestion du trafic aérien. Le vent et la température sont deux paramètres omniprésents, subis, et à l'origine de nombreux biais de prédiction qui altèrent le suivi des trajectoires. Nous proposons une méthode pour limiter ces biais. Le concept "Wind and Température Networking" améliore la prédiction de trajectoire en utilisant le vent et la température mesurés par les avions voisins. Nous détaillons les effets de la température sur l'avion, permettant sa prise en compte. L'évaluation du concept est faite sur 8000 vols. Nous traitons du calcul de trajectoires optimales en présence de vent prédit, pour remplacer les actuelles routes de l'Atlantique Nord, et aboutir à des groupes de trajectoires optimisées et robustes. Dans la conclusion, nous présentons d'autres champs d'applications du partage de vents, et abordons les besoins en nouvelles infrastructures et protocoles de communication, nécessaires à la prise en compte de ce nouveau concept. / This thesis is related to air traffic management systems current changes. On the ground and in flight, trajectory calculation methods and available data differ. Wind and temperature are two ubiquitous parameters that are subject to and cause prediction bias. We propose a concept to limit this bias. Our "Wind and Temperature Networking" concept improves trajectory prediction, using wind and temperature information from neighboring aircraft. We detail the effects of temperature on the aircraft performances, allowing for temperature to be taken into account. The concept evaluation is done on 8000 flights. We discuss the calculation of optimal trajectories in the presence of predicted winds, to replace the current North Atlantic Tracks, and to provide optimized and robust groups of trajectories. The conclusion of this thesis presents other fields of wind sharing applications, and addresses the need for new telecommunications infrastructures and protocols.