Real-time Kinematic Positioning: Background, Assessment and Forecasting
2018-07
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Real-time Kinematic Positioning: Background, Assessment and Forecasting
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2018-07
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This thesis presents an introduction and work performed related to real-time kinematic (RTK) positioning. RTK positioning is a differential positioning method that uses signals from global navigation satellite systems (GNSS). Position solutions using RTK methods have a nominal accuracy on the order of centimeters and are available in real-time, making them useful for such applications as autonomous vehicles and mobile robotics, driver-assist technologies, and precise geospatial data collection. First, a background on positioning using GNSS and RTK methods is presented. Next, an assessment of low-cost RTK receivers for the Minnesota Department of Transportation is described. Several low-cost RTK-capable receivers were assessed using metrics related to accuracy, availability, continuity and ini- tialization in different environments during static and dynamic tests. The low-cost mul- tifrequency receiver tested performed more consistently than the single-frequency low-cost receivers, especially for the dynamic tests. Of the low-cost single frequency receivers, there is a wide range in performance metrics. In addition, a multi-thousand dollar receiver was tested and outperformed all of the low-cost receivers in all environments. Finally, a fore- casting method using recurrent neural networks is explored to increase the robustness of RTK positioning. The methodology presented here was unable to create a reliable RTK solution, but suggestions are offered for future work. The goal of this thesis is to familiarize the reader with the basic premise of RTK positioning and educate them on the capabilities of low-cost receivers.
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University of Minnesota M.S. thesis. July 2018. Major: Aerospace Engineering and Mechanics. Advisor: Demoz Gebre-Egziabher. 1 computer file (PDF); ii, 82 pages.
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Jackson, John. (2018). Real-time Kinematic Positioning: Background, Assessment and Forecasting. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/200136.
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