Atmospheric sensing tasks, including measuring the thermodynamic state (pressure, temperature, and humidity) and kinematic state (wind velocity) of the atmospheric boundary layer (ABL) can aid in numerical weather prediction, help scientists assess climatological and topological features over a region, and can be incorporated into flight path planning and control of small aircraft. Small uncrewed aerial vehicles (UAVs) are becoming an attractive platform for atmospheric sensing tasks as they offer increased maneuverability and are low-cost instruments when compared to traditional atmospheric sensing methods such as ground-based weather stations and weather balloons. In situ measurements using a UAV can be obtained for the thermodynamic state of the ABL using dedicated sensors that directly measure pressure, temperature, and humidity whereas the kinematic state (wind velocity) can be measured directly, using, for example, a five-hole Pitot probe or a sonic anemometer mounted on an aircraft, or indirectly. Indirect measurement methods consider the dynamics of the aircraft and use measurements from its operational sensor suite to infer wind velocity. This work is concerned with the design of the invariant extended Kalman filter (invariant EKF) for indirect wind estimation using a small, fixed-wing uncrewed aerial vehicle. Indirect wind estimation methods are classified as model-based or model-free, where the model refers to the aerodynamic force and moment model of the considered aircraft. The invariant EKF is designed for aerodynamic model-free wind estimation using a fixed-wing UAV in horizontal-plane flight and the full six degree of freedom UAV. The design of the invariant EKF relies on leveraging the symmetries of the dynamic system in the estimation scheme to obtain more accurate estimates where convergence of the filter is guaranteed on a larger set of trajectories when compared to conventional estimation techniques, such as the conventional extended Kalman filter (EKF). The invariant EKF is applied on both simulated and experimental flight data to obtain wind velocity estimates where it is successful in providing accurate wind velocity estimates and outperforms the conventional EKF. Overall, this work demonstrates the feasibility and effectiveness of implementing an invariant EKF for aerodynamic model-free indirect wind estimation using only the available measurements from the operational sensor suite of a UAV. / Doctor of Philosophy / Atmospheric sensing tasks, such as obtaining measurements of the pressure, temperature, humidity, and wind velocity of the atmospheric boundary layer (ABL), the lowest part of the atmosphere, have historically been dominated by the use of ground-based weather stations and deployment of weather balloons. Uncrewed aerial vehicles (UAVs) are emerging as an attractive, cost-effective platform for measuring desired quantities in the ABL. A UAV provides increased maneuverability when compared to fixed ground-based sensors and weather balloons as it can fly in different patterns and over any specified region within physical limits. Measurements of the ABL can help atmospheric scientists improve numerical weather prediction by providing more temporally and spatially dense data, in addition to helping assess climatological or topological features such as how the flow of wind varies over different types of terrain. A UAV can measure wind velocity directly or indirectly. Direct wind velocity measurements require mounting a dedicated wind sensor on a UAV and indirect measurement methods require only knowledge of the UAV's motion model with measurements from sensors already onboard to support automated flight. This work is concerned with designing an estimator for indirect wind velocity estimation using a small, fixed-wing UAV and only measurements from its operational sensor suite. The estimator, the invariant extended Kalman filter, leverages the symmetries of the system to provide estimates of the state or extended state of the system which can include position, velocity, and wind velocity. A system with symmetry is one that is unchanged by actions or transformations such as translation and rotation. The knowledge that the system remains unchanged under some transformations is used in the design of the invariant EKF. This estimator is then implemented for indirect wind estimation on both simulated and experimental flight data where it, in general, outperforms a conventional estimation method–the extended Kalman filter. The work presented in this dissertation demonstrates the effectiveness of implementing an invariant EKF for indirect wind estimation using a small, fixed-wing UAV and measurements from its operational sensor suite.

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.

Indirect wind estimation onboard unmanned aerial systems (UASs) can be accomplished using existing air vehicle sensors along with a dynamic model of the UAS augmented with additional wind-related states. It is often desired to extract a mean component of the wind the from frequency fluctuations (i.e. turbulence). Commonly, a variation of the KALMAN filter is used, with explicit or implicit assumptions about the nature of the random wind velocity. This dissertation presents an H-infinity (H∞) filtering approach to wind estimation which requires no assumptions about the statistics of the process or measurement noise. To specify the wind frequency content of interest a low-pass filter is incorporated. We develop the augmented UAS model in continuous-time, derive the H∞ filter, and introduce a KALMAN-BUCY filter for comparison. The filters are applied to data gathered during UAS flight tests and validated using a vaned air data unit onboard the aircraft. The H∞ filter provides quantitatively better estimates of the wind than the KALMAN-BUCY filter, with approximately 10-40% less root-mean-square (RMS) error in the majority of cases. It is also shown that incorporating DRYDEN turbulence does not improve the KALMAN-BUCY results. Additionally, this dissertation describes the theory and process for using generalized polynomial chaos (gPC) to re-cast the dynamics of a system with non-deterministic parameters as a deterministic system. The concepts are applied to the problem of wind estimation and characterizing the precision of wind estimates over time due to known parametric uncertainties. A novel truncation method, known as Sensitivity-Informed Variable Reduction (SIVR) was developed. In the multivariate case presented here, gPC and the SIVR-derived reduced gPC (gPCr) exhibit a computational advantage over Monte Carlo sampling-based methods for uncertainty quantification (UQ) and sensitivity analysis (SA), with time reductions of 38% and 98%, respectively. Lastly, while many estimation approaches achieve desirable accuracy under the assumption of known system parameters, reducing the effect of parametric uncertainty on wind estimate precision is desirable and has not been thoroughly investigated. This dissertation describes the theory and process for combining gPC and H-infinity (H∞) filtering. In the multivariate case presented, the gPC H∞ filter shows superiority over a nominal H∞ filter in terms of variance in estimates due to model parametric uncertainty. The error due to parametric uncertainty, as characterized by the variance in estimates from the mean, is reduced by as much as 63%. / Doctor of Philosophy / On unmanned aerial systems (UASs), determining wind conditions indirectly, without direct measurements, is possible by utilizing onboard sensors and computational models. Often, the goal is to isolate the average wind speed while ignoring turbulent fluctuations. Conventionally, this is achieved using a mathematical tool called the KALMAN filter, which relies on assumptions about the wind. This dissertation introduces a novel approach called H-infinity (H∞) filtering, which does not rely on such assumptions and includes an additional mechanism to focus on specific wind frequencies of interest. The effectiveness of this method is evaluated using real-world data from UAS flights, comparing it with the traditional KALMAN-BUCY filter. Results show that the H∞ filter provides significantly improved wind estimates, with approximately 10-40% less error in most cases. Furthermore, the dissertation addresses the challenge of dealing with uncertainty in wind estimation. It introduces another mathematical technique called generalized polynomial chaos (gPC), which is used to quantify and manage uncertainties within the UAS system and their impact on the indirect wind estimates. By applying gPC, the dissertation shows that the amount and sources of uncertainty can be determined more efficiently than by traditional methods (up to 98% faster). Lastly, this dissertation shows the use of gPC to provide more precise wind estimates. In experimental scenarios, employing gPC in conjunction with H∞ filtering demonstrates superior performance compared to using a standard H∞ filter alone, reducing errors caused by uncertainty by as much as 63%.

Numerical weather modeling can benefit from improved wind sensing in the Earth's atmospheric boundary layer (ABL). Small, low-cost, uncrewed aircraft (drones) can be used to measure wind and a distribution of these vehicles could potentially provide measurements with much greater density and resolution, in both space and time, than current methods allow. To measure wind, a drone could be equipped with dedicated wind-measuring sensors, although these can be costly and obtrusive and must be carefully calibrated to account for interference effects. State estimation algorithms that combine a drone's operational measurements with a flight dynamic model can be used to infer wind without a dedicated wind sensor, although the sensor quality affects measurement accuracy. Previous studies have explored the effects of various sensors on wind estimate accuracy, but the effect of flight dynamic model fidelity has received less attention. This dissertation presents analysis of different aerodynamic model-free and model-based wind estimation methods, comparing six wind estimation formulations using experimental flight data from a small, fixed-wing aircraft. Each formulation is implemented using a Kalman filter, an extended Kalman filter, and an unscented Kalman filter. These filters are designed based on different assumptions related to the flight dynamic model, available sensors, and available measurements. Having identified a promising estimation approach, the dissertation next explores the value of incorporating unsteady effects into a flight dynamic model for model-based wind estimation. An unsteady aerodynamic model for a small, fixed-wing aircraft is developed, identified, and validated using experimental flight data. An extended Kalman filter is then designed and implemented for two motion models -- one that includes unsteady effects and another that does not. Analysis of the wind estimates and the estimation differences show that, while the unsteady flight dynamic model better predicts the aircraft motion, the value of incorporating this model for wind estimation is questionable. / Doctor of Philosophy / Wind velocity sensing is crucial to understanding the meteorological processes at low altitudes. The integration of low-cost drones has allowed them to be used as wind-sensing platforms. This is achieved by equipping small drones with dedicated wind-measuring sensors, often costly and infeasible, or inferring wind velocity from the drone's motion. Algorithms designed to infer wind can be used by combining onboard flight sensor measurements with a drone's flight dynamic model to infer wind. However, low-cost drones are usually equipped with low-cost flight sensors, which frequently lead to higher measurement uncertainty and degrade the accuracy of wind estimates. Previous studies have explored the effects of various sensors on wind estimates, but errors due to low-fidelity dynamic models have received less attention. This dissertation first presents a detailed analysis of different flight dynamic model-free and model-based wind estimation methods. It compares six wind estimation formulations. Each formulation is implemented in wind inferring algorithms called a Kalman filter, an extended Kalman filter, and an unscented Kalman filter. These algorithms are designed based on different assumptions related to the flight dynamic model, available flight sensors, and available measurements. Secondly, the value of incorporating a fixed-wing, unsteady flight dynamic model in a wind estimation scheme is analyzed. To this end, an unsteady flight dynamic model for a fixed-wing drone is developed, identified, and validated from data acquired from the drone's flight history. Furthermore, an extended Kalman filter is designed and implemented for two motion models -- one that includes unsteady effects and another that does not. The analysis of the time histories of the wind estimates and the wind estimate differences show that both model-based estimators perform equally well.

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.