Unmanned aerial vehicles (UAVs) are emerging as enablers for supporting many applications and services, such as precision agriculture, search and rescue, temporary network deployment, coverage extension, and security. UAVs are being considered for integration into emerging wireless networks as aerial users, aerial relays (ARs), or aerial base stations (ABSs). This dissertation proposes employing UAVs to contribute to physical layer techniques that enhance the security performance of advanced wireless networks and services in terms of availability, resilience, and confidentiality. The focus is on securing terrestrial cellular communications against eavesdropping with a cellular-connected UAV that is dispatched as an AR or ABS. The research develops mathematical tools and applies machine learning algorithms to jointly optimize UAV trajectory and advanced communication parameters for improving the secrecy rate of wireless links, covering various communication scenarios: static and mobile users, single and multiple users, and single and multiple eavesdroppers with and without knowledge of the location of attackers and their channel state information. The analysis is based on established air-to-ground and air-to-air channel models for single and multiple antenna systems while taking into consideration the limited on-board energy resources of cellular-connected UAVs. Simulation results show fast algorithm convergence and significant improvements in terms of channel secrecy capacity that can be achieved when UAVs assist terrestrial cellular networks as proposed here over state-of-the-art solutions. In addition, numerical results demonstrate that the proposed methods scale well with the number of users to be served and with different eavesdropping distributions. The presented solutions are wireless protocol agnostic, can complement traditional security principles, and can be extended to address other communication security and performance needs.

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Pour résoudre les problèmes environnementaux et énergétiques liés au nombre croissant de véhicules en circulation, deux approches sont envisageables : l'une est technologique et vise à améliorer les composants du véhicule ou son architecture, l'autre est comportementale et cherche à changer la manière d'utiliser les véhicules. Dans ce contexte, l'éco-conduite représente une méthode, applicable immédiatement, permettant à chaque conducteur de réduire sa consommation. L'objectif de cette thèse est donc l'analyse des gains potentiels de l'éco-conduite pour les différents types de véhicules existant : thermique, électrique et hybride. Ainsi, la première partie de ce travail se focalise sur une étude théorique visant à calculer les gains potentiels et à déterminer les règles d'éco-conduite, avant d'aborder dans un second temps une mise en situation plus réaliste et une intégration des algorithmes dans un système d'assistance pour le conducteur. En s'appuyant sur une modélisation énergétique des différents types de véhicules, la détermination et la comparaison du fonctionnement optimal se base sur l'optimisation du profil de vitesse pour des trajets connus. La programmation dynamique a été mise enoeuvre pour calculer la trajectoire optimale énergétique en tenant compte de la contrainte temporelle afin de ne pas pénaliser l'intérêt d'une conduite économe. Evidemment, l'intégration de l'éco-conduite doit, d'une part, tenir compte du trafic à proximité du véhicule et d'autre part, ne pas aboutir à une augmentation des émissions de polluants. Ainsi, en nous appuyant sur des modèles de suivi de véhicules (trafic), nous avons montré que les principes d'éco-conduite restent valables et conduisent de toute façon à des gains énergétiques. Concernant les contraintes d'émissions, des résultats expérimentaux nous ont conduit à adapter nos algorithmes pour répondre simultanément aux aspects écologiques et économiques. Enfin, les connaissances acquises ont été appliquées à la conception d'un système d'assistance testé sur un simulateur de conduite. / The transportation sector has been identified as one of many sources of today's energetic and environmental problems. With constantly increasing numbers of vehicles on the road, non-renewable fossil fuels are becoming scarce and expensive. In addition, due to the pollutant emissions of internal combustion engines, the transportation sector is a major producer of greenhouse gas emissions. To resolve these problems researcher are looking for technological solutions, such as more efficient components and alternative drive train technologies, on one hand. On the other hand, work is being done to ensure the most efficient utilization of available technological resources. Eco driving is one way to immediately reduce a driver's energy consumption. In this thesis the potential gains of eco driving for passenger vehicles will be discussed. The main objective of this work is to, first, identify and compare drive train specific, optimal vehicle operation. Secondly, the effect of real-life constraints on potential gains of eco driving is evaluated. In addition, an approach to integrate mathematical optimization algorithms in an advanced driver assist system for eco driving is proposed. Physical vehicle models are developed for three representative vehicles: the conventional, electric and power-split hybrid vehicle. Using real-life and standard drive cycles a baseline mission is defined by specifying trip and road constraint. Applying the dynamic programming algorithms the trajectory optimization problem is solved, minimizing energy consumption for the trip. The effect of traffic on potential gains of eco driving is discussed, considering a vehicle following situation. Integrating emission constraints in the optimization algorithm the environmental advantages of eco driving are discussed. Finally, the developed algorithms were integrated in a driver assist system. Experimental tests on a driving simulator were used to verify the effectiveness of the system, as well as driver acceptance.

Preliminary interplanetary trajectory designs frequently use simplified two-body orbital mechanics and linked conics methodology to model the complex trajectories in multi-body systems. Incorporating gravity assists provides highly efficient interplanetary trajectories, enabling otherwise infeasible spacecraft missions. Future missions may employ powered gravity assists, using a propulsive maneuver during the flyby, improving the overall trajectory performance. This dissertation provides a complete description and analysis of a new interplanetary trajectory design tool known as TRACT (TRAjectory Configuration Tool). TRACT is capable of modeling complex interplanetary trajectories, including multiple ballistic and/or powered gravity assists, deep space maneuvers, parking orbits, and other common maneuvers. TRACT utilizes an adaptable architecture of modular boundary value problem (BVP) algorithms for all trajectory segments. A bi-level optimization scheme is employed to reduce the number of optimization variables, simplifying the user provided trajectory information. The standardized optimization parameter set allows for easy use of TRACT with a variety of optimization algorithms and mission constraints. The dissertation also details new research in powered gravity assists. A review of literature on optimal powered gravity assists is presented, where many optimal solutions found are infeasible for realistic spacecraft missions. The need was identified for a mission feasible optimal powered gravity assist algorithm using only a single impulsive maneuver. The solution space was analyzed and a complete characterization was developed for solution types of the optimal single-impulse powered gravity assist. Using newfound solution space characteristics, an efficient and reliable optimal single-impulse powered gravity assist BVP algorithm was formulated. The mission constraints were strictly enforced, such as maintaining the closest approach above a minimum radius and below a maximum radius. An extension of the optimal powered gravity assist research is the development of a gravity assist BVP algorithm that utilizes an asymptote ΔV correction maneuver to produce ballistic gravity assist trajectory solutions. The efficient algorithm is tested with real interplanetary mission trajectory parameters and successfully converges upon ballistic gravity assists with improved performance compared to traditional methods. A hybrid approach is also presented, using the asymptote maneuver algorithm together with traditional gravity assist constraints to reach ballistic trajectory solutions more reliably, while improving computational performance.

The objective of this study was to design a launch vehicle capable of deploying a nanosatellite into a Sun-synchronous orbit at 500 km orbital altitude from the JAS 39E/F Gripen fighter aircraft. This was achieved by first performing theoretical calculations for the required nozzles and solid propellant grain configurations for the first two solid stages, followed by the necessary liquid propellant configuration for the third stage. Lastly, two methods were investigated in solving the trajectory ascent problem for the launch vehicle design. First, by stating the trajectory problem as an initial value problem while guessing a Sigmoidal steering law. Secondly, by stating the trajectory problem as a boundary value problem. The latter was solved by transcribing the trajectory problem into a nonlinear program where a parametric steering law was derived using a Sequential quadratic programming algorithm.Ultimately, resulting in a launch vehicle design with a gross lift-off mass of 1,289 kg, capable of launching an 8.4 kg payload into the targeted orbit, with suggested modifications to increase the possible payload mass to 12.9 kg. / Målet med den här studien var att designa en luftlanserad trestegsraket kapabel till att transportera en nanosatellit upp till en solsynkron omloppsbana på 500 km altitud från ett JAS 39E/F Gripen jaktflygplan. Det gjordes genom att först beräkna de nödvändiga dysorna och krutladdningsformerna för de två första stegen tillsammans med en flytande bränsledesign för det tredje steget. Två metoder undersöktes för bananalysen. Först genom att anta en Sigmoidal styrningsfunktion för pitchen, sedan genom att transkribera problemet till ett icke-linjärt program där en parametrisk styrlag togs fram genom att använda en Sequential quadratic programming algoritm. Slutligen presenterades en raketdesign med en total vikt på 1 289 kg, kapabel till att skjuta upp en nyttolast på 8,4 kg till den önskade omloppsbanan tillsammans med förslag som kan öka den möjliga nyttolasten till 12,9 kg.