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Open AccessArticle
Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques
by
Kaiting Xie
Kaiting Xie 1
,
Zhaoguo Zhang
Zhaoguo Zhang 1,2,* and
Shiliang Zhu
Shiliang Zhu 2
1
The Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2
The Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(8), 1396; https://doi.org/10.3390/agriculture14081396 (registering DOI)
Submission received: 17 July 2024
/
Revised: 12 August 2024
/
Accepted: 17 August 2024
/
Published: 18 August 2024
Abstract
This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions.
Share and Cite
MDPI and ACS Style
Xie, K.; Zhang, Z.; Zhu, S.
Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques. Agriculture 2024, 14, 1396.
https://doi.org/10.3390/agriculture14081396
AMA Style
Xie K, Zhang Z, Zhu S.
Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques. Agriculture. 2024; 14(8):1396.
https://doi.org/10.3390/agriculture14081396
Chicago/Turabian Style
Xie, Kaiting, Zhaoguo Zhang, and Shiliang Zhu.
2024. "Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques" Agriculture 14, no. 8: 1396.
https://doi.org/10.3390/agriculture14081396
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