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AuthorEl-Wakeel, Amr S.
AuthorOsman, Abdalla
AuthorZorba, Nizar
AuthorHassanein, Hossam S.
AuthorNoureldin, Aboelmagd
Available date2024-07-14T07:57:23Z
Publication Date2020
Publication NameIEEE Sensors Journal
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/JSEN.2019.2958791
ISSN1530437X
URIhttp://hdl.handle.net/10576/56620
AbstractNext-generation Intelligent Transportation Systems (ITS) of future road traffic monitoring will be required to provide reports on traffic status, road conditions, and driver behaviour. Road surface anomalies contribute to increasing the risk of traffic accidents, reduced driver comfort and increased vehicles' damage. The conventional integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) positioning solutions can suffer from errors because of inertial sensor noises and biases, especially when low-cost and commercial grade inertial sensors are used. In this work, we use a reduced inertial sensor system utilizing Micro-Electro-Mechanical-System (MEMS) based inertial sensors, to integrate with the GNSS receiver and provide robust positioning in urban canyons. To provide acceptable performance in challenging urban environments, our method de-noises the MEMS-based inertial sensor measurements using a technique based on a Bi-orthonormal search, which separates the monitored motion dynamics from both the inertial sensor bias errors and high-frequency noises. As a result, the performance of the positioning system is improved, providing reliable positioning accuracy during extended GNSS outages that occur in various areas. To show the significant enhancement achieved by the proposed approach, we examined the system performance over three road test trajectories involving MEMS-based inertial sensors and GNSS receivers mounted on our test vehicle. The superior performance of our proposed INS/GNSS integrated positioning system is demonstrated in this paper during various GNSS outages, in different areas, and under multiple driving scenarios.
SponsorManuscript received October 25, 2019; revised December 4, 2019; accepted December 6, 2019. Date of publication December 10, 2019; date of current version February 14, 2020. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant STPGP 521432 and in part by the National Priorities Research Program (NPRP) through the Qatar National Research Fund (a member of The Qatar Foundation) under Grant NPRP 9-185-2-096. The associate editor coordinating the review of this article and approving it for publication was Dr. Prosanta Gope. (Corresponding author: Amr S. El-Wakeel.) A. S. El-Wakeel is with the Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada (e-mail: amr.elwakeel@queensu.ca).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectconnected vehicles
intelligent transportation systems
Kalman filter
positioning
Road information services
spectral de-noising
TitleRobust Positioning for Road Information Services in Challenging Environments
TypeArticle
Pagination3182-3195
Issue Number6
Volume Number20
dc.accessType Abstract Only


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