The rise of autonomous drive imposes new challenges in terms of robustness and precision of Global Navigation Satellite Systems (GNSS) technology, especially in the urban environment. The conventional GNSS signal processing usually takes Kalman filter (KF) as the signal parameter estimator to enhance the tracking performance, however, this is not a promising design for the urban navigation application where the signal blockages and severe multipath interferences are frequently occurred. To address this issue, a moving horizon estimator (MHE) will be used to replace KF for the accuracy and robustness improvement. Unlike the KF that highly depends on the accurate modelling of the system and measurement characteristics and cannot afford random outliers and distortions, MHE can incorporate the constraints, e.g., a priori defined variances, to limit threats from the faults, interferences or invalid measurements. Therefore, MHE is less sensitive to the random environmental variations as compared to EKF, which makes it more robust and more applicable to urban environment. The proposed tracking algorithm is verified with a realistic road test near Lujiazui CBD area in Shanghai in post-processing manner. The results confirm the improved accuracy, reliability, and robustness by using MHE method.
Performance Evaluation of Robust GPS Signal Tracking with Moving Horizon Estimation in Urban Environment
Lect. Notes Electrical Eng.
International Conference on Aerospace System Science and Engineering ; 2020 ; Shanghai, China July 14, 2020 - July 16, 2020
Proceedings of the International Conference on Aerospace System Science and Engineering 2020 ; Kapitel : 28 ; 403-418
2021-06-02
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Multipath , Urban environment , Kalman filter , Moving horizon estimator , GNSS signal tracking Engineering , Aerospace Technology and Astronautics , Communications Engineering, Networks , Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) , Control and Systems Theory
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