Zidan, Jasmine (2022) Enhancing the robustness of global navigation satellite systems for connected and autonomous vehicles. PhD thesis, University of Warwick.

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Abstract

This thesis presents an overview of GNSS vulnerabilities from a CAV perspect ive and surveys the immediate GNSS challenges and current (un)-intentional interference mitigating techniques. In this thesis, the effect of antenna place ment on the localisation accuracy is evaluated. The evaluation results show that it is recommended that the GNSS antenna is placed along the longitudinal mid-way section and approximately one-third from the front or back of a car’s roof. Additionally, this research evaluates supervised learning techniques for GNSS pseudorange discrimination. A prediction accuracy of approximately 98 % can be obtained from a fine decision tree using the GNSS carrier-to-noise ratio and the rate of change of the epochs of the satellite vehicles in view. Moreover, this thesis proposes the design of a possible method with two layers of GNSS observation checks to exclude non-line-of-sight and multipath effects. Results will show that the exclusion of the inconsistent measurements can yield a 96 % increase in mean positioning accuracy of the estimated position in a high and medium multipath environment and a mean positioning error of 85 cm can be attained. Furthermore, this thesis evaluates the impact of GNSS pseudor anges and Doppler measurements effects as decision tree prediction features for multi-constellation GNSS signal classification. The analysis shows that both features could enhance the classification, with the pseudorange having higher predicting importance. A 13 % enhancement in the prediction accuracy is obtained compared to the signal strength and the elevation angle as standalone features. Also evaluated is the use of a GPS L1 signal prediction regression model on other GNSS constellations. Analysis of the investigated scenario shows that the current model is not adequate for non-GPS constellations. Moreover, open research areas and techniques are identified, which can be further investigated to enhance the robustness of GNSS for CAV applications.

Item Type:	Thesis (PhD)
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) Q Science > Q Science (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics
Library of Congress Subject Headings (LCSH):	Global Positioning System, Artificial satellites in navigation, Automated vehicles, Antennas (Electronics) -- Industrial applications, Supervised learning (Machine learning)
Official Date:	July 2022
Dates:	Date Event July 2022 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Warwick Business School
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Higgins, Matthew D. ; Kampert, Erik
Sponsors:	Spirent Communications
Format of File:	pdf
Extent:	xiii, 99 pages : illustrations (colour), maps (colour), charts (colour)
Language:	eng

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