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Unmanned Aerial Vehicle Positioning Using a Phased Array Radio and GNSS Independent SensorsRapp, Carl January 2019 (has links)
This thesis studies the possibility to replace the global navigation satellite system (GNSS) with a phased array radio system (PARS) for positioning and navigation of an unmanned aerial vehicle (UAV). With the increase of UAVs in both civilian and military applications, the need for a robust and accurate navigation solution has increased. The GNSS is the main solution of today for UAV navigation and positioning. However, the GNSS can be disturbed by malicious sources, the signal can either be blocked by jamming or modified to give the wrong position by spoofing. Studies have been conducted to replace or support the GNSS measurements with other drift free measurements, e.g. camera or radar systems. The position measurements from PARS alone is shown not to provide sufficient quality for the application in mind. The PARS measurements are affected by noise and outliers. Reflections from the ground makes the PARS elevation measurements unusable for this application. A root mean square error (RMSE) accuracy of 10 m for a shorter flight and 198 m for a longer flight are achieved in the horizontal plane. The decrease in accuracy for the longer flight is assumed to come from a range bias that increases with distance due to the flat earth approximation used as the navigation frame. Positioning based on PARS aided with a filter and other GNSS independent sensors is shown to reduce the noise and remove the outliers. Five filters are derived and evaluated: a constant velocity extended Kalman filter (EKF), an inertial measurement unit (IMU) aided EKF, an IMU and barometer aided EKF, a converted measurements Kalman filter (CMKF) and a stationary Kalman filter (KF). The IMU and barometer aided EKF performed the best results with a RMSE of 8 m for a shorter flight and 106 m for a longer flight. The noise is significantly reduced compared to the standalone PARS measurements. The conclusion is that PARS can be used as a redundancy system with the IMU and barometer aided EKF. If the EKF algorithm is too computational demanding, the simpler stationary KF can be motivated since the accuracy is similar to the EKF. The GNSS solution should still be used as the primary navigation solution as it is more accurate.
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Traitement spatial des interférences cyclostationnaires pour les radiotélescopes à réseau d'antennes phasé / Spatial processing of cyclostationary interferers for phased array radio telescopesFeliachi, Rym 12 April 2010 (has links)
Cette thèse est une contribution à l’amélioration des observations pour les radiotélescopes à réseaux phasés en présence d’interférences. L’originalité de cette thèse repose sur l’utilisation de la séparation spatiale entre les sources cosmiques et les brouilleurs issus des télécommunications en se basant sur la cyclostationnarité de ces derniers. Cette thèse s’inscrit dans le cadre du projet européen SKADS pour l’amélioration des techniques de suppression d’interférences en radioastronomie pour les futurs instruments d’observations.Nous avons proposé trois techniques de traitement d’interférences : la détection,l’estimation et la soustraction, et le filtrage spatial. Les performances des techniques proposées ont été évaluées à travers des simulations sur des données synthétiqueset/ou réelles, et comparées aux techniques existantes. / This thesis is a contribution to observation improvements for phased array radiotelescopes, in the presence of radio frequency interferers (RFIs). The originality ofthe study is the use of the cyclostationarity property, in order to improve the spatial separation between cosmic sources and telecommunication signals. This thesis is part of the European SKADS project, which aims to improve RFI mitigation techniques for future instruments in radio astronomy.We have proposed three spatial processing techniques: detection, estimation and subtraction and spatial filtering. The performance of the techniques presented have been evaluated through simulations on synthetic and/or real data, and compared to existing approaches.
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