Robust Positioning for Road Information Services in Challenging Environments
المؤلف | El-Wakeel, Amr S. |
المؤلف | Osman, Abdalla |
المؤلف | Zorba, Nizar |
المؤلف | Hassanein, Hossam S. |
المؤلف | Noureldin, Aboelmagd |
تاريخ الإتاحة | 2024-07-14T07:57:23Z |
تاريخ النشر | 2020 |
اسم المنشور | IEEE Sensors Journal |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/JSEN.2019.2958791 |
الرقم المعياري الدولي للكتاب | 1530437X |
الملخص | Next-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. |
راعي المشروع | Manuscript 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). |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | connected vehicles intelligent transportation systems Kalman filter positioning Road information services spectral de-noising |
النوع | Article |
الصفحات | 3182-3195 |
رقم العدد | 6 |
رقم المجلد | 20 |
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