No description available.

L'optique adaptative (OA), qui permet de corriger en temps-réel les déformations du front d'onde induites par la turbulence atmosphérique, connaît une limitation fondamentale : l'anisoplanétisme. Pour y remédier, le concept d'OA grand champ (OAGC) a été proposé. La turbulence est mesurée dans plusieurs directions du champ de vue à l'aide d'étoiles guide naturelles et laser, et son impact corrigé sur les images par une commande basée sur une reconstruction tomographique. L'approche linéaire quadratique gaussienne (LQG) est bien adaptée à la conception de lois de commande en OAGC comme en OA classique. Elle permet d'estimer et de prédire la phase à l'aide d'un filtre de Kalman basé sur des a priori spatiaux et temporels. Les modèles d'état et commandes associées sont détaillés. On présente la première mise en oeuvre sur le ciel d'une commande LQG sur tous les modes, en OA classique et multi-objet, à l'aide du démonstrateur CANARY. Ces résultats sont obtenus avec identification du modèle de tip-tilt et filtrage des vibrations, ce qui constitue la première mise en oeuvre ciel de cette stratégie. Les a priori spatiaux de la phase en volume sont identifiés par la méthode LEARN. Des données issues du profilomètre stereoSCIDAR ont aussi été utilisées. Des comparaisons sont proposées avec une commande intégrateur en OA classique, avec un gain significatif en performances pour le LQG. Les comparaisons avec le reconstructeur statique APPLY (moindres carrés régularisés) en OA multi-objet mettent en évidence un gain du LQG dans certains cas (fort bruit en particulier). L'ensemble des résultats confirme la faisabilité et l'intérêt d'une commande LQG pour un instrument d'OA ou d'OAGC. / Adaptive Optics (AO), which enables to correct in real time wavefront deformation induced by atmospheric turbulence, faces a fundamental limitation: anisoplanatism. To counter it, the concept of Wide-Field AO (WFAO) has been proposed. Turbulence is measured in several directions of the field of view, using natural and laser guide stars, and its impact on images is mitigated by a control based on tomographic reconstruction. The Linear Quadratic Gaussian (LQG) approach is well-suited to AO control design in both WFAO and classical AO. LQG enables to estimate and predict the phase with a Kalman filter based on spatial and temporal priors. State-space models and associated controls are laid out. The first on-sky implementation of LQG control on all modes, in classical and multi-object AO, is presented on the CANARY pathfinder. These results have been obtained with identification of tip-tilt models and vibration filtering, which constitutes the first on-sky implementation of this strategy. Spatial priors on the phase in the volume are identified using the LEARN algorithm. Data from the stereoSCIDAR profilometer were also used. Comparisons are provided with integral AO control in standard AO, showing significant gain in performance with LQG. Comparisons with the static reconstructor APPLY (regularized least-squares) in multi-object AO show a gain in performance with LQG in some cases (especially in high-noise conditions). Results confirm feasibility and relevance of LQG control for AO or WFAO instruments.

Localization is an essential part of any autonomous vehicle. In a simple setting, the localization problem is almost trivial, and can be solved sufficiently using simple dead reckoning or an off-the-shelf GPS with differential corrections. However, as the surroundings become more complex, so does the localization problem. The urban environment is a prime example of a situation in which a vehicle's surroundings complicate the problem of position estimation. The urban setting is marked by tall structures, overpasses, and tunnels. Each of these can corrupt GPS satellite signals, or completely obscure them, making it impossible to rely on GPS alone. Dead reckoning is still a useful tool in this environment, but as is always the case, measurement and modeling errors inherent in dead reckoning systems will cause the position solution to drift as the vehicle travels eventually leading to a solution that is completely diverged from the true position of the vehicle. The most widely implemented method of combining the absolute and relative position measurements provided by GPS and dead reckoning sensors is the Extended Kalman Filter (EKF). The implementation discussed in this paper uses two Kalman Filters to track two completely separate position solutions. It uses GPS/INS and odometry to track the Absolute Position of the vehicle in the Global frame, and simultaneously uses odometry alone to compute the vehicle's position in an arbitrary Local frame. The vehicle is then able to use the Absolute position estimate to navigate on the global scale, i.e. navigate toward globally referenced checkpoints, and use the Relative position estimate to make local navigation decisions, i.e. navigating around obstacles and following lanes. This localization solution was used on team VictorTango's 2007 DARPA Urban Challenge entry, Odin. Odin successfully completed the Urban Challenge and placed third overall. / Master of Science

The multi-scale mixed finite element method (MsMFEM) discussed in this work uses a two-scale approach, where the solutions to independent local flow problems on the fine grid capture the fine-scale variations of the reservoir model, while the coarse grid equations appropriately assimilate this information in the global solution. Temporal changes in porous media flow are relatively moderate when compared to the spatial variations in the reservoir. Hence, approximate global solutions by adaptively solving these local flow problems can be obtained with significant savings in computational time. The ensemble Kalman filter, used for real-time updating of reservoir models, can thus be coupled with the MsMFEM-streamline simulator to speed up the historymatching process considerably.

Abstract A feasible technique, using carrier-phase data from GPS and CCD camera, is presented to identify ground target location as well as azimuth angle of a low altitude aircraft/helicopter without using any gyroscope measurements; the baseline vector can also be identified using GPS compass. The ground target¡¦s image is extracted from background and recorded by image processing technique. By integrating ground target¡¦s location and the recorded GPS data, the designated states can be estimated by using extended Kalman filter technique. Basically, the extended Kalman filter does the state estimation job, and it¡¦s a nonlinear measurement process. By processing these time update and measurement update, the integer ambiguity as well as azimuth angle can be determined. The proposed GPS compass system consists of three componets : pointer, sensor, and controller. By using carrier-phase data from two GPS receivers, we can compute the baseline vector, whose length is equal to one meter, and achieve the direction accuracy within one degree. The integer ambiguity number is resolved by rotating the baseline vector; the conventional antenna swapping technique is a special case of the proposed method. Therefore, the GPS compass may replace these magnetic compass or gyroscope used in navigation system. By continuously snapping ground target image using CCD camera and utilizing the GPS receivers, the coordinate of the ground target can be identified. Simulation justifies the feasibility of the proposed scenario. Simulation has shown that the estimation errors for stationary and traveling with constant velocity ground targets are within 1.8 m and 6 m, respectively.

Stuttgart, Univ., Diplomarb., 2004.

Stuttgart, Univ., Diplomarb., 2004.

Chemnitz, Techn. Univ., Diplomarb., 2007.

The Kalman filter algorithm can be applied as a recursive estimator of the state of a dynamic system described by a linear difference equation. Given discrete measurements linearly related to the state of the system, but corrupted by white Gaussian noise, the Kalman filter estimate of the system is statistically optimal with respect to a quadratic function of the estimate error. The first objective of this paper is to give deep enough insight into the mathematics of the Kalman filter algorithm to be able to choose the correct type of algorithm and to set all the parameters correctly in a basic application. This description also includes several examples of different approaches to derive and to explain the Kalman filter algorithm. In addition to the mathematical description of the Kalman filter algorithm this paper also provides an implementation written in MATLAB. The objective of this part is to correctly replicate the target tracker used in the surveillance radar PS-90. The result of the implementation is evaluated using a simulated target programmed to have an aircraft-like behaviour and done without access to the actual source code of the tracker in the PS-90 radar

No description available.