Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820187
Title: GPS precise positioning using multisensor data fusion for vehicular communication
Author: Yin, Jiachen
ISNI:       0000 0004 9354 5754
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2019
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Abstract:
The Global Positioning System (GPS) is a widely used timing, navigation and positioning system. However, its performance can be signifi- cantly degraded by the effects of the various sources of noises due to a highly dynamic multipath environment, signal blockage or attenuation due to ionospheric perturbation. Therefore, the aim of this research is to enhance the GPS signal acquisition and tracking ability to mitigate these effects. Furthermore, it is desirable to provide continuous and consistent positioning information under GPS-denied environments with assistance from multi-sensor data fusion techniques. In this thesis, a novel GPS signal acquisition approach for very dense multipath environments, using a low cost innovative dual polarization patch antenna attached to GPS receiver, is implemented. This reduces the acquisition processing time significantly compared to the conventional serial searching approach. Furthermore, it successfully acquires extra satellites in a dense multipath environment. Furthermore, the GPS signal carrier tracking loop has been considered as one of the most important links in order to demodulate the navigation data frame. An innovative carrier tracking loop is also proposed that comprises two approaches, namely, the adaptive Kalman filter and the adaptive unscented Kalman filter, dynamically integrated with a third order phased locked loop respectively. The proposed two-carrier tracking loops are compared against the conventional carrier tracking loop and the results prove that the proposed approach is more robust and accurate. The carrier tracking performance employing this novel approach is improved, especially in highly dynamic and low CNR environments. Finally, in order to integrate GPS and the sensors, GPS, IMU (inertial measurement unit) and LiDAR are combined for data fusion. A novel line feature extraction and mapping algorithm was designed for LiDAR navigation with a low complexity that resulted in faster feature extraction. This was followed by an innovative integration scheme that combined GPS, IMU and LiDAR, which resulted in continuous, precise positioning data for vehicular communication, even when GPS signals are not available, in a harsh multipath environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.820187  DOI: Not available
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