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AutoNet2030 – Co-operative Systems in Support of Networked Automated Driving by 2030 – is a European project connecting two domains of intensive research: cooperative systems for Intelligent Transportation Systems and Automated Driving. Given the latest developments in the standardization of vehicular communications, vehicles will soon be wirelessly connected, enabling cooperation among them and with the infrastructure. At the same time, some vehicles will offer very advanced driving assistance systems, ranging from Cooperative Adaptive Cruise Control (C-ACC) to full automation. The research issues addressed in AutoNet2030 are as follows: how can all these vehicles with different capabilities most efficiently cooperate to increase safety and fluidity of the traffic system? What kind of information should be exchanged? Which organization (e.g. centralized or distributed) is the best? The purpose of this paper is to introduce the vision and concepts underlying the AutoNet2030 project and the direction of this ongoing research work.

L'avènement des Système de Positionnement par Satellites, également désigné sous le sigle GNSS, a révolutionné le monde d'aujourd'hui avec des applications diverses, de la localisation à la télédétection. Le principe du GNSS est basé sur la trilatération, qui dépend de l'estimation du retard de propagation et de l'effet Doppler. En effet, la synchronisation retard-Doppler est un sujet de recherche important pour de nombreux domaines, avec des applications pratiques telles que le radar, le sonar, l'ultrason, la télécommunication et la navigation. Or, pour la conception et l'évaluation des techniques d'estimation, il est important de connaître la meilleure performance accessible au sens de l'erreur quadratique moyenne (EQM), ce qui est fourni par le calcul des bornes inférieures (BI) sur l'EQM. Par rapport aux autres BI, les bornes de Cramér-Rao (BCR) sont plus simples à calculer et fournissent une estimation précise de l'EQM de l'estimateur au sens du Maximum de Vraisemblance (EMV) dans la région d'opération asymptotique, sous certaines conditions. Aussi, les architectures des récepteurs GNSS reposent sur des approches d'acquisition et de suivi, effectuée de manière scalaire, qui peuvent être considérées comme des instances particulières de l'EMV. Cependant, malgré une littérature fournie sur les BCR relatives à l'estimation retard-Doppler, la plupart de ces expressions de la BCR sont trop restrictives et ne concernent que le modèle de signal à bande étroite, sans tenir compte de l'impact de l'effet du Doppler sur le signal en bande de base. En effet, une expression analytique de BCR, suffisamment générale et facile à utiliser, pour n'importe quel signal à bande limitée, n'était pas disponible au début de cette thèse. L'objectif principal de la présente thèse porte sur la caractérisation des performances asymptotiques de l'estimation du retard et du Doppler par un EMV cohérent. La première contribution est l'obtention d'une nouvelle expression analytique de la BCR pour l'estimation du retard, en considérant un signal générique à bande limitée et un retard de propagation constant, cette expression offrant de nouvelles possibilités pour la conception d'un signal optimal. Cette expression de la BCR est ensuite utilisée pour caractériser l'estimation conjointe du retard et de la phase du signal. Cette approche est pertinente pour la localisation précise, exploitant la phase de la porteuse, telles que PPP et RTK. Par ailleurs, ces travaux de recherche ont permis de mettre en évidence l'absence d'une analyse complète des performances des signaux GNSS dans la littérature scientifique, d'un point de vue de l'estimation optimale. L'analyse présentée ici vise à combler cette lacune et fournit également les limites de performance du positionnement standard. Pour franchir cette limite, il devient alors nécessaire de recourir aux techniques de positionnement basées sur la phase de la porteuse. Enfin, nous étendons la formulation analytique de la BCR générique pour inclure l'estimation conjointe du retard et du Doppler, d'abord pour les signaux à bande étroite, puis pour leurs homologues à large bande, incluant également l'amplitude et la phase. The advent of Global Navigation Satellite Systems (GNSS) has revolutionized today's world, with applications in positioning, timing and remote sensing. The principle of GNSS is based on multilateration, which first involves the estimation of both signal propagation time-delay and Doppler effect for each visible satellite. In fact, delay-Doppler synchronization is an active research topic of significant practical importance in many fields, with applications in radar, sonar, ultrasonics, communications and navigation. Now, when designing and assessing estimation techniques, it is of fundamental importance to know the ultimate achievable performance in the mean square error (MSE) sense, information which is brought by the computation of performance lower bounds (LBs) on the MSE. Compared to other LBs, Cramér-Rao Bounds (CRB) are simple to calculate and give an accurate estimation of the MSE of the maximum likelihood estimator (MLE) in the asymptotic region of operation under certain conditions. Indeed, GNSS receiver architectures rely on a scalar acquisition and tracking approach, which can be seen as particular instances of a MLE solution. However, even if the delay-Doppler CRB literature is abundant, most of these CRB expressions are unnecessarily restrictive and only address the standard narrowband signal model, without considering the impact of the Doppler effect on the baseband signal. Most importantly, a sufficiently generalized and easy-to-use compact CRB for generic band-limited signals is not available, being an important missing point of practical interest. The primary objective of the present thesis is the characterization of the asymptotic performance of GNSS time-delay and Doppler estimation, i.e., synchronization, within a coherent integration time. The first contribution is the derivation of a new compact closed-form CRB expression for time-delay estimation, considering a generic band-limited transmitted signal and constant transmitter-to-receiver propagation delay, completed with insights on an optimal signal design as well as a performance loss metric. This CRB derivation is then extended to include the joint time-delay and phase estimation, which is of interest for standard precise positioning approaches exploiting the carrier phase, such as PPP and RTK. A complimentary problem is then addressed: the lack of a comprehensive performance analysis of GNSS signals in literature, from an optimal estimation point of view. The work further provides the performance thresholds that require a need for carrier phase-based positioning techniques. Finally, we extend the derivation of the generic compact closed-form CRB expression to include the joint time-delay and Doppler stretch estimation, first for narrowband signals and then for their wideband counterpart, followed by the introduction of a general compact closed-form CRB expression for the amplitude and phase. Such results set the theoretical basis for the full GNSS receiver chain characterization.

Orientadores: João Bosco Ribeiro do Val, Yusef Rafael Caceres Zuniga Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação Resumo: Radares são sistemas que utilizam ondas eletromagnéticas para detectar alvos e obter informações sobre eles, as quais geralmente incluem distância, azimute, elevação e velocidade radial. Quase todos os radares modernos podem ainda rastrear as trajetórias dos alvos, baseando-se nas medidas. O problema de rastreamento de trajetórias é bem conhecido na literatura. Muitas soluções incluem a premissa de que a variável aleatória representando o estado do sistema, em um dado instante de tempo, é modelada por uma distribuição Gaussiana no espaço Euclidiano, ou, ao menos, pode ser aproximada por uma. Trabalhos recentes mostram que alguns sistemas dinâmicos, tais como robôs de tração diferencial, apresentam restrições geométricas que produzem uma distribuição curva para o estado e enfraquecem a aproximação Gaussiana, o que leva a perdas no desempenho da filtragem. Entretanto, os trabalhos mostram que se o estado do sistema é representado em uma estrutura de grupo de Lie matricial, as coordenadas exponenciais da álgebra de Lie acabam por apresentar um formato que pode ser ajustado a uma distribuição Gaussiana. Mais que isso, muitas estratégias de filtragem negligenciam as restrições geométricas associadas ao sistema, enquanto filtros baseados em grupo de Lie podem considerar tais restrições de uma forma muito natural. Devido às incertezas na velocidade e na velocidade angular de um alvo aéreo, durante a filtragem da trajetória, é razoável esperar que a distribuição do estado também pode apresentar um formato curvo. Além disso, muitos modelos de alvos aéreos de radar consideram o sistema como sendo não holonômico. Por conta disso, é coerente esperar que o rastreamento de alvos de radar também possa se beneficiar ao usar uma estrutura de grupo de Lie para representar o estado do sistema. Este trabalho desenvolve um estudo sobre a aplicação de grupos de Lie ao problema de rastreamento de alvos de radar, investigando melhorias em desempenho de filtros de trajetórias quando o estado do alvo é modelado como um processo estocástico que evolui em um grupo de Lie. Nós apresentamos a teoria necessária para o entendimento dos algoritmos de filtragem em grupos de Lie utilizados de uma forma auto-contida. Ademais, desenvolvemos os modelos de sistema e de medida para alvos aéreos de radar, bem como verificamos a premissa da distribuição curva para o estado do sistema. Ainda, comparamos o desempenho dos filtros em grupos de Lie desenvolvidos contra suas alternativas que evoluem no espaço Euclidiano. O desempenho é avaliado em termos do erro RMS da posição e é analisado para valores variáveis da precisão em azimute e probabilidade de detecção, utilizando dados simulados Abstract: Radar is a system that uses electromagnetic waves to detect targets and obtain information about them. Usually, they include radial distance, azimuth, elevation, and radial speed relative to the radar. Additionally, almost every modern radar system can track the trajectories of the targets based on the measurements. The problem of trajectory tracking is well-known in the literature. Many solutions include a premise that the system state, at a given time instant, is modeled by a Gaussian distribution in the Euclidean space, or at least can be approximated to one. Recent works have shown that some systems, such as differential-drive robots, present geometrical constraints that produce a curved-shape state distribution, such that the Gaussian approximation is poor and can compromise the filtering performance. However, they show that if one represents the system state in a matrix Lie group structure, instead of in the Euclidean space, the exponential coordinates of the Lie algebra end up presenting a shape that can be fitted to a Gaussian distribution. More than that, many filtering strategies neglect the geometrical constraints associated with the system while Lie group-based filters can naturally consider such restrictions. Due to the uncertainties in speed and angular velocity of aerial targets, it is reasonable to expect that the state distribution of aerial targets may also present a curved shape during the tracking filtering. Moreover, many aerial target models consider the system to be non-holonomic. Due to this, it can be expected that the radar target tracking could also benefit from the Lie group structure to represent the state. This work studies the application of the Lie group theory to the radar target tracking problem, investigating improvements in the tracking filter performance when the target state is modeled as a stochastic process evolving on a Lie group. We present the theory for understanding filtering on Lie groups in a self-contained manner. We derive system and measurement models for aerial radar targets. We verify the premise of a curved distribution for the system state distribution. Moreover, we compare the performance of the developed filters on Lie groups against their alternatives in the Euclidean space. The performance is evaluated in terms of the position RMSE and it is analyzed for varying the precision range in azimuth and the probability of detection of the radar system using simulated data Mestrado Automação Mestre em Engenharia Elétrica FINEP

In the context of space surveillance, we are interested in a cluster of debris evolving in orbit around the Earth and observed by a radar sensor.It is then observed that the debris spreads out taking a bananoid shape due to their movement constrained by Kepler's laws.This distribution is representative of concentrated Gaussian samples on the Lie group SE (3) and can be completely characterized by anunknown covariance matrix.We propose in this thesis an original reformulation of the cluster observation model on Lie groups. The latter is then modeled as an extended targetcharacterized by its shape and its centroid. In this way, we reconsiderits estimation as a manifold inference problem.The geometry of the cluster is thus intrinsically taken into account. Two algorithms on Lie groups are then proposed in order to estimate respectively statically and dynamically the parameters of the cluster.In the first part of the manuscript, the issue of space surveillance is underlined and the main methods for tracking debris are recalled.In a second part, the foundations of Lie groups arepresented. The third part focuses on the contributions of the thesis andproposes a model and two algorithms for estimating the shape and centroid of a cluster which are then tested on different simulation scenarios.The last part is devoted to a theoretical contribution inwhich is proposed a bound for Bayesian estimation error on Lie groups., Dans le contexte de la surveillance spatiale, nous nous intéressons à un amas de débris évoluant en orbite autour de la Terre et observé par un capteur radar.Il est alors constaté que l'ensemble des débris se disperse selon une forme bananoïdale due à leur mouvement contraint par les lois de Kepler.Cette répartition est représentative d'échantillons gaussiens concentréssur le groupe de Lie SE(3) et peut être complètement caractérisée par unematrice de covariance inconnue.Nous proposons dans cette thèse une reformulation originale sur groupe de Liedu modèle d'observation de l'amas. Ce dernier est alors modélisé comme une cibleétendue caractérisée par sa forme et et son centroïde. De cette manière, nous reconsidéronsl'estimation de ces derniers comme un problème d'inférence sur variété.La géométrie de l’amas est ainsi intrinsèquement prise en compte. Deux algorithmes sur groupes de Liesont alors proposés afin d'estimer respectivement de manière statique et dynamique les paramètres de l'amas.Dans une première partie du manuscrit, l'enjeu de la surveillance spatiale est souligné et les principales méthodes de pistage de débris sont rappelées.Dans une seconde partie, les fondements des groupes de Lie sontprésentés. La troisième partie est axée sur les contributions de la thèse etpropose un modèle et deux algorithmes d'estimation de la forme et du centroïde d’un amas qui sont ensuite testés sur différents scénarios de simulation.La dernière partie est consacrée à une contribution théorique danslaquelle est mise en place une borne d'erreur d'estimation bayésienne sur groupe de Lie.

We are concerned with the comparison of the shapes of open smooth curves that take their values in a Riemannian manifold M. To this end, we introduce a reparameterization invariant Riemannian metric on the infinite-dimensional manifold of these curves, modeled by smooth immersions in M. We derive the geodesic equation and solve the boundary value problem using geodesic shooting. The quotient structure induced by the action of the reparametrization group on the space of curves is studied. Using a canonical decomposition of a path in a principal bundle, we propose an algorithm that computes the horizontal geodesic between two curves and yields an optimal matching. In a second step, restricting to base manifolds of constant sectional curvature, we introduce a detailed discretization of the Riemannian structure on the space of smooth curves, which is itself a Riemannian metric on the finite-dimensional manifold Mn+1 of "discrete curves" given by n + 1 points. We show the convergence of the discrete model to the continuous model, and study the induced geometry. We show results of simulations in the sphere, the plane, and the hyperbolic halfplane. Finally, we give the necessary framework to apply shape analysis of manifold-valued curves to radar signal processing, where locally stationary radar signals are represented by curves in the Poincaré polydisk using information geometry.; Nous nous intéressons à la comparaison de formes de courbes lisses prenant leurs valeurs dans une variété riemannienne M. Dans ce but, nous introduisons une métrique riemannienne invariante par reparamétrisations sur la variété de dimension infinie des immersions lisses dans M. L’équation géodésique est donnée et les géodésiques entre deux courbes sont construites par tir géodésique. La structure quotient induite par l’action du groupe des reparamétrisations sur l’espace des courbes est étudiée. À l’aide d’une décomposition canonique d’un chemin dans un fibré principal, nous proposons un algorithme qui construit la géodésique horizontale entre deux courbes et qui fournit un matching optimal. Dans un deuxième temps, nous introduisons une discrétisation de notre modèle qui est elle-même une structure riemannienne sur la variété de dimension finie Mn+1 des "courbes discrètes" définies par n + 1 points, où M est de courbure sectionnelle constante. Nous montrons la convergence du modèle discret vers le modèle continu, et nous étudions la géométrie induite. Des résultats de simulations dans la sphère, le plan et le demi-plan hyperbolique sont donnés. Enfin, nous donnons le contexte mathématique nécessaire à l’application de l’étude de formes dans une variété au traitement statistique du signal radar, où des signaux radars localement stationnaires sont représentés par des courbes dans le polydisque de Poincaré via la géométrie de l’information.