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Article

A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning

1
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
State Key Laboratory of Air Traffic Management System and Technology, Nanjing 210007, China
3
Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaolin Meng, Jian Wang, Craig M. Hancock, Weiping Jiang and Zhansheng Liu
Remote Sens. 2021, 13(11), 2117; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112117
Received: 19 April 2021 / Revised: 14 May 2021 / Accepted: 21 May 2021 / Published: 28 May 2021
The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method. View Full-Text
Keywords: GNSS; vehicle; urban positioning; fault detection and exclusion GNSS; vehicle; urban positioning; fault detection and exclusion
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MDPI and ACS Style

Cheng, Q.; Chen, P.; Sun, R.; Wang, J.; Mao, Y.; Ochieng, W.Y. A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning. Remote Sens. 2021, 13, 2117. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112117

AMA Style

Cheng Q, Chen P, Sun R, Wang J, Mao Y, Ochieng WY. A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning. Remote Sensing. 2021; 13(11):2117. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112117

Chicago/Turabian Style

Cheng, Qi, Ping Chen, Rui Sun, Junhui Wang, Yi Mao, and Washington Y. Ochieng 2021. "A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning" Remote Sensing 13, no. 11: 2117. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112117

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