Deregulation, globalization, and commercialization are drastically changing the space industry. But commercial space activities entail considerable risks. This thesis is primarily an analysis of the risks that private entities in the space industry need to manage in order to be commercially successful. Due to the trend towards a buyer's market, satellite manufacturers increasingly have been forced to accept risks that do not fall within their traditional core business. Consequently, manufacturing companies become risk managers for a variety of legal space risks. Therefore, the legal framework for the commercial management of legal risks is analyzed and solutions to identified problems are offered. This thesis studies trends in contemporary risk management practices in the private sector, which is dominated by market forces. It is argued that risk management of legal issues should form an integral part of overall space project management, the rationale being that losses in any phase of space activities, while certain to occur, are uncertain in time and scale. This thesis, therefore, scrutinizes legal risk management throughout the life cycle of space projects. Few space applications have become commercially viable. Today, satellite navigation provided by the U.S. GPS is widely used, especially because it is free of direct charges. In Europe, a competing system, Galileo, is being developed. It will provide users with different service levels, ranging from free services to more reliable and accurate navigation services. As this system has both, public and commercial benefits, the industry is expected to participate in a Public Private Partnership for the Galileo satellite constellation. This thesis makes specific proposals to manage the legal risks of the Galileo project. At the same time, the allocation of legal space risks between the various parties is studied. The thesis of the author is that the management process, which is used to control technical space risks, can provide satellite manufacturers with a supportive analogy for dealing with legal space risks. Risks will be studied for all project phases of Galileo, i.e., the feasibility study, the establishment of specifications, development, manufacturing, the launch, operations, replenishment, and the final disposition of satellites.

This thesis describes the theoretical and practical stages through development to testing of an integrated navigation system, specifically composed of an Inertial Navigation System (INS), and Global Positioning System (GPS). Integrated navigation systems combine the best features of independent systems to bring out increased performance, improved reliability and system integrity. In an integrated INS/GPS system, INS output is used to calculate current navigation states / GPS output is used to supply external measurements, and a Kalman filter is used to provide the most probable corrections to the state estimate using both data. Among various INS/GPS integration strategies, our aim is to construct a tightly coupled integrated INS/GPS system. For this purpose, mathematical models of INS and GPS systems are derived and they are linearized to form system dynamics and system measurement models respectively. A Kalman filter is designed and implemented depending upon these models. Besides these, based on the given aided navigation system representation a quantitative measure for observability is defined using Gramians. Finally, the performance of the developed system is evaluated with real data recorded by the sensors. A comparison with a reference system and also with a loosely coupled system is done to show the superiority of the tightly coupled structure. Scenarios simulating various GPS data outages proved that the tightly coupled system outperformed the loosely coupled system from the aspects of accuracy, reliability and level of observability.

GPS has become very popular in recent years. It is used in wide range of applications including aircraft navigation, search and rescue, space borne attitude and position determination and cellular network synchronization. Each application places demands on GPS for various levels of accuracy, integrity, system availability and continuity of service. Radio frequency interference (RFI) which results from many sources such as TV/FM harmonics, radar or mobile satellite systems, presents a challenge to the use of GPS. It can affect all the service performance indices mentioned above. To improve the accuracy of GPS positioning, a continuously operating reference station (CORS) network can be used. A CORS network provides all the enabled GPS users in an area with corrections to the fundamental measurements, producing more precise positioning. A threat to these networks is a threat to all high-accuracy GPS users. It is therefore necessary to monitor the quality of the received signal with the objective of promptly detecting the presence of RFI and providing a timely warning of the degradation of system accuracy, thereby boosting the integrity of GPS. This research was focused on four main tasks: a) Detection. The focus here is on a power spectral density fluctuation detection technique, in which statistical inference is used to detect narrowband continuous-wave (CW) interference in the GPS signal band after being captured by the RF front-end. An optimal detector algorithm is proposed. At this optimal point, for a fixed Detection Threshold (DT), probability of false alarm becomes minimal and for a fixed probability of false alarm, we can achieve the minimum value for the detection threshold. Experiments show that at this point we have the minimum computational load. This theoretical result is supported by real experiments. Finally this algorithm is employed to detect a real GPS interference signal generated by a TV transmitter in Sydney. b) Characterization. In the characterization section, using the GNSS signal structure and the baseband signal processing inside the GNSS receiver, a closed formula is derived for the received signal quality in terms of effective carrier to noise ratio ( ). This formula is tested and proved by calculating the C/No using the I and Q data from a software GPS receiver. For pulsed CW, a similar analysis is done to characterize the effect of parameters such as pulse repetition period (PRP) and also duty cycle on the received signal quality. Considering this characterization and the commonality between the GPS C/A code and Galileo signal as a basis to build up a common term for satellite availability, the probability of satellite availability in the presence of CW interference is defined and for the two currently available satellite navigation systems (GPS L1 signal and Galileo signal (GIOVE-A BOC(1, 1) in the E1/L1 band)) it is shown that they can be considered as alternatives to each other in the presence of different RFI frequencies as their availability in the presence of CW RFI is different in terms of RFI frequency. c) Mitigation. The last section of the research presents a new concept of ?Satellite Exclusion Zone?. In this technique, using our previously developed characterization techniques, and considering the fact that RFI has different effects on different satellite signals at different times depending on satellite Doppler frequency, the idea of excluding the most vulnerable satellite signal from positioning calculations is proposed. Using real data and real interference, the effectiveness of this technique is proven and its performance analyzed. d) Hardware implementation. The above detection technique is implemented using the UNSW FPGA receiver board called NAMURU.

Th. 3e cycle--Histoire--Paris 1, 1985. / Bibliogr. f. 224-264.

University, Diss., 2003--Tübingen.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

L'environnement \textit{indoor} permet un grand nombre de services issus des technologies de l'information et de la communication. La localisation de l'utilisateur, via celle de son smartphone, est donc un élément-clé de cette réussite. Cette thèse s'intéresse au suivi des déplacements de l'utilisateur grâce aux capteurs de mouvement embarqués dans son smartphone. Elle repose sur la détermination du type de locomotion. Nous proposons une solution de navigation \textit{indoor} complète, permettant de proposer à l'utilisateur un chemin jusqu'à sa destination dans n'importe quel bâtiment tout en connaissant sa position à chaque instant, avec une précision de l'ordre du mètre. De façon analogue, nous avons également montré que nous pouvons déterminer le mode de transport d'un utilisateur pour une application de détection de places de parking libres. / Indoor environments present opportunities for a rich set of location-aware services in the information and communications technology (ICT) area. Therefore, accurately localizing a user indoors has become a key enabling technology. This thesis addresses the issue of tracking a user equipped with an off-the-shelf smartphone by exploiting its embedded motion sensors. Leveraging key characteristics of human locomotion, we propose a complete, infrastructure-free indoor navigation solution, allowing a user to navigate any unknown building with meter-level accuracy. Finally, extending our understanding of locomotion to outdoors areas where users are inside vehicles, we design and implement a smartphone application for smart on-street parking.

Pour assister la navigation piétonne dans les espaces urbains et intérieurs, une estimation précise de la pose (i.e. la position 3D et l'orientation3D) d'un équipement tenu en main constitue un point essentiel dans le développement d'outils d'aide à la mobilité (e.g. applications de réalité augmentée). Dans l'hypothèse où le piéton n'est équipé que d'appareils grand public, l'estimation de la pose est limitée à l'utilisation de capteurs à faible coût intégrés dans ces derniers (i.e. un récepteur GNSS, une unité de mesure inertielle et magnétique et une caméra monoculaire). De plus, les espaces urbains et intérieurs, comprenant des bâtiments proches et des éléments ferromagnétiques, constituent des zones difficiles pour la localisation et l'estimation de la pose lors de grands déplacements piétons.Cependant, le développement récent et la mise à disposition d'informations contenues dans des Systèmes d'Information Géographiques 3D constituent une nouvelle source de données exploitable pour la localisation et l'estimation de la pose. Pour relever ces défis, cette thèse propose différentes solutions pour améliorer la localisation et l'estimation de la pose des équipements tenus en main par le piéton lors de ses déplacements en espaces urbains et intérieurs. Les solutions proposées intègrent l'estimation de l'attitude basée inertielle et magnétique, l'odométrie visuelle monoculaire mise à l'échelle grâce à l'estimation des déplacements du piéton, l'estimation absolue de la pose basée sur la reconnaissance d'objets SIG 3D parfaitement connus et la mise à jour en position de la navigation à l'estime du piéton.Toutes ces solutions s'intègrent dans un processus de fusion permettant d'améliorer la précision de la localisation et d'estimer en continu une pose qualifiée de l'appareil tenu en main.Cette qualification est nécessaire à la mise en place d'un affichage en réalité augmentée sur site. Pour évaluer les solutions proposées, des données expérimentales ont été recueillies au cours de déplacements piétons dans un espace urbain avec des objets de référence et des passages intérieurs. / To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3Dposition and 3D orientation) of an equipment held inhand constitutes an essential point in the development of mobility assistance tools (e.g.Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e. an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements,constitute challenging areas for localization and sensor pose estimation during large pedestrian displacements.However, the recent development and provision of data contained in 3D Geographical Information System constitutes a new wealth of data usable for localization and pose estimation.To address these challenges, this thesis proposes solutions to improve pedestrian localization and hand-held device pose estimation in urban and indoor spaces. The proposed solutions integrate inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation, 3D GIS known object recognition-based absolute pose estimation and Pedestrian Dead-Reckoning updates. All these solutions are fused to improve accuracy and to continuously estimate a qualified pose of the handheld device. This qualification is required tovalidate an on-site augmented reality display. To assess the proposed solutions, experimental data has been collected during pedestrian walks in an urban space with sparse known objects and indoors passages.

Cette thèse s’inscrit dans le domaine de la navigation robotique bio-inspirée en environnement réel et implique la capacité pour un robot mobile à se déplacer de manière autonome dans un monde a priori dynamique et inconnu. Les travaux décrits au cours de ce manuscrit s’attacheront à montrer comment, en partant des travaux académiques réalisés par l’équipe Neurocybernétique du laboratoire ETIS, il a été possible de concevoir le robot mobile Diya One capable de naviguer de manière autonome dans de grands environnements intérieurs. Depuis une vingtaine d’année, l’équipe Neurocybernétique élabore des modèles de navigation bio-inspirée. De précédents travaux ont montré qu’un modèle de cellules de lieu, enregistrées chez le rat, permet à un robot mobile d’apprendre des comportements de navigation robustes, tels qu’une ronde ou un retour au nid, à partir d’associations entre lieu et action. L’apprentissage et la reconnaissance d’un lieu ne reposent alors que sur des informations visuelles. Cependant, trois problèmes critiques ne permettent pas de naviguer dans de grands environnements : 1- l’ambiguïté de certaines situations visuelles (ou alias perceptif), 2- l’apprentissage sur le long terme et 3- la sensibilité aux conditions environnementales. L’ajout d’autres modalités constitue une solution efficace pour augmenter la robustesse de la localisation. L’équipe a développé plusieurs modèles basés sur la proprioception du robot afin desuppléer, dans les cas limites, les modèles purement visuels. La principale limitation des approches proprioceptives est qu’elles sont soumisesà l’accumulation d’erreurs. Il est donc nécessaire de recalibrer périodiquement les modèles. Fusionner des modalités allothétiques et idiothétiques semblent être une bonne stratégie pour obtenir une estimation fiable de la localisation du robot. Les champs de neurones dynamiques (DNF) ou continous attractor neural network (CANN) constituent un puissant candidat pour mettre en œuvre le type de mémoire requis pour la construction de cellules de lieu. Nous présenterons un premier modèle de fusion utilisant les champs de neurones dynamiques pour maintenir l’orientation du robot puis un second généralisant le principe du modèle de fusion pour la construction de cellules de lieux multimodales.Être capable de produire et commercialiser rapidement un produit est un enjeu majeur pour la survie de Partnering. En plus des capacités de navigation et de localisation, un robot commercialisable requiert un ensemble de comportements indispensables à la mobilité, à la sécurité (loi de contrôle, évitement des obstacles et des trous) et à son autonomie (gestion d’énergie et retour à la station de recharge). Pour aboutir à cette première solution, nous avons suivit une démarche ascendante (bottom-up) défendue par la robotique comportementale. Nous avons développé progressivement la complexité du robot au travers de comportements élémentaires intégrés dans une architecture de contrôle régissant à chaque instant l’expression de ces comportements et la sélection des actions à exécuter.Ce mémoire est découpée en deux parties. Une première partie industrielle relevant d’objectifs à court terme, consistant à mettre en place, à partir des modèles existants développés par l’équipe Neurocybernétique, l’architecture comportementale de la première version du robot Diya One. Puis, une seconde partie plus fondamentale dans laquelle nous traiterons de la réalisation demodèles de fusion multimodale. Ces modèles seront ajoutés incrémentalement au robot afin d’améliorer progressivement ses capacités de navigation. / This thesis falls into the field of navigation in bio-inspired robotics in real environment and implies the ability for a mobile robot to move autonomously in a world a priori dynamic and unknown. The work described in this manuscript will show how, starting from the academic work carried out by the Neurocybernetics team of the ETIS laboratory, it was possible to design the mobile robot Diya One able to navigate autonomously in large indoor environments. For the past 20 years, the Neurocybernetics team has been developing bio-inspired navigation models. Previous work has shown that a model of place cells, recorded in the rat, allows a mobile robot to learn robust navigation behaviors, such as a round or a homing, from associations between place and action. Learning and the recognition of a place are based only on visual information. However, three critical problems do not allow to navigate in large environments: 1- the ambiguity of certain visual situations (or perceptual alia), 2- long-term learning, and 3-sensitivity to environmental conditions. The addition of other modalities is an effective solution for increasing the robustness of the location. The team has developed several models based on the proprioception of the robot in order to compensate, in limiting cases, for purely visual models. The main limitation of proprioceptive approaches is that, based on the proprioception of the robot, they are subject to the accumulation of errors. It is therefore necessary to periodically recalibrate the models. Merging allothetic and idiothetic modalities seems to be a good strategy for obtaining a reliable estimate of the robot’s location. Dynamic neural network (DNF) or continous attractor neural network (CANN) constitute a powerful candidate to implement the type of memory required for the construction of place cells. We present a first fusion model using dynamic neuron fields to maintain the orientation of the robot and then a second one generalizing the principle of fusion model for the construction of cells of multimodal places. Being able to produce and market quickly a product is a major challenge for Partnering’s survival. In addition to navigation and location capabilities, a marketable robot requires a set of behaviors that are essential to mobility, security (control law, avoidance of obstacles and holes) and its autonomy (energy management and return to the charging station). To arrive at this first solution, we followed a bottom-up approach defended by behavioral robotics. We have progressively developed the complexity of the robot through elementary behaviors integratedin a control architecture governing at each moment the expression of these behaviors and the selection of the actions to be executed. This manuscript is divided into two parts. A first industrial part with short-term objectives, consisting of implementing the behavioral architecture of the first version of the Diya One robot, based on the existing models developed by the Neurocybernetics team. Then, a second more theoretical part in which we will deal with the realization of multimodal fusion models. These models will be added incrementally to the robot in order to gradually improve its navigationcapabilities.

Ce travail de thèse porte sur la modélisation des phénomènes de propagation affectant les signaux de navigation par satellite en environnement urbain dense avec une focalisation particulière sur les multitrajets et l'aspect large bande du canal de propagation espace/Terre. Le simulateur de canal pseudo temps-réel développé, SCHUN (Simplified CHannel for Urban Navigation), repose sur une approche hybride physico-statistique. La composante statistique de la modélisation permet essentiellement de générer une ville virtuelle à partir de distributions de bâtiments connues. Le reste de la modélisation s'appuie sur une approche physique simplifiée où les interactions ondes électromagnétiques/ville virtuelle reposent d'une part sur un modèle de macro-diffusion à l'échelle des façades, (3CM (Three Component Model)), et d'autre part sur un modèle physique de masquage du trajet direct par les bâtiments. Les principales méthodes numériques sous-jacentes sont l'optique physique et la théorie uniforme de la diffraction. Le simulateur de canal SCHUN ouvre aujourd'hui des perspectives intéressantes pour la modélisation large bande du canal de propagation espace/Terre. Optimisé pour des temps de calcul raisonnables, alliant une composante statistique à une composante physique simplifiée, ce simulateur a été conçu et validé par des mesures expérimentales pour répondre à des besoins de simulation des systèmes à diversité de satellite, diversité de réception, diversité de polarisation ou encore diversité de fréquence pour des applications de navigation par satellite. / This PhD work deals with land mobile satellite channel modelling and addresses the specific issue of satellite navigation systems in urban environments with a particular focus on multipath modelling and wide-band representation of the channel. The developed land mobile satellite channel simulator, SCHUN (Simplified CHannel for Urban Navigation), is based on a hybrid physical-statistical approach satisfying fast computation requirements. The statistical component of the modelling is mainly used during the virtual city synthesis step based on known statistical distributions of building height and street width. The rest of the modelling comes from deterministic methods using simplified electromagnetic interaction models reproducing building macro-scattering (3CM model (Three Component Model)) and building blockage of the direct path. The main underlying electromagnetic methods are the physical optics and the uniform theory of diffraction. The SCHUN simulator now opens interesting perspectives for the modelling of wide-band land mobile satellite propagation channel in dense urban environments. Optimised for pseudo real-time constraints, it uses both physical and statistical approaches. Furthermore, the SCHUN simulator has been designed and validated against measurements to answer specific needs of satellite diversity, receiving diversity, polarisation diversity or frequency diversity for satellite navigation applications.