Browsing by Author "Isik, Oguz Kagan"
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Item Open Access Integrity analysis for GPS-based navigation of UAVs in urban environment(MDPI, 2020-08-25) Isik, Oguz Kagan; Hong, Juhyeon; Petrunin, Ivan; Tsourdos, AntoniosThe increasing use of Unmanned Aerial Vehicles (UAVs) in safety-critical missions in both civilian and military areas demands accurate and reliable navigation, where one of the key sources of navigation information is presented by Global Navigation Satellite Systems (GNSS). In challenging conditions, for example, in urban areas, the accuracy of GNSS-based navigation may degrade significantly due to user-satellite geometry and obscuration issues without being noticed by the user. Therefore, considering the essentially dynamic rate of change in this type of environment, integrity monitoring is of critical importance for understanding the level of trust we have in positioning and timing data. In this paper, the dilution of precision (DOP) coefficients under nominal and challenging conditions were investigated for the purpose of integrity monitoring in urban environments. By analyzing positioning information in a simulated urban environment using a software-based GNSS receiver, the integrity monitoring approach based on joint consideration of GNSS observables and environmental parameters has been proposed. It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches. This has allowed generating indirect integrity measures based on cut-off elevation and satellite visibility that can be used for UAV path planning and guidance in urban environments.Item Open Access A machine learning based GNSS performance prediction for urban air mobility using environment recognition(IEEE, 2021-11-15) Isik, Oguz Kagan; Petrunin, Ivan; Inalhan, Gokhan; Tsourdos, Antonios; Moreno, Ricardo Verdeguer; Grech, RaphaelAs the primary navigation source, GNSS performance monitoring and prediction have critical importance for the success of mission-critical urban air mobility and cargo applications. In this paper, a novel machine learning based performance prediction algorithm is suggested considering environment recognition. Valid environmental parameters that support recognition and prediction stages are introduced, and K-Nearest Neighbour, Support Vector Regression and Random Forest algorithms are tested based on their prediction performance with using these environmental parameters. Performance prediction results and parameter importances are analyzed based on three types of urban environments (suburban, urban and urban-canyon) with the synthetic data generated by a high quality GNSS simulator.Item Open Access Performance enhancement of low-cost INS/GNSS navigation system operating in urban environments(AIAA, 2023-01-19) Ozdemir, Yunus Emre; Isik, Oguz Kagan; Geragersian, Patrick; Petrunin, Ivan; Grech, Raphael; Wong, RonaldAs a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.