GNSS RTK/UWB/DBA Fusion Positioning Method and Its Performance Evaluation

Wang, Shengliang; Dong, Xianshu; Liu, Genyou; Gao, Ming; Xiao, Gongwei; Wenhao, Zhao; Lv, Dong

doi:10.3390/rs14235928

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Appendix A.1. Proof of Equation (5) The relationship between the Earth-fixed North-East-Down coordinate system n and the typical body-fixed coordinate system b is shown in Figure A1. The following unit vectors are defined:…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

“…As a result, there are numerous challenges in receiving Global Navigation Satellite System (GNSS) signals in urban or forest environments due to the weak anti-interference ability of GNSS [3]. Despite some related studies [4][5][6] having reduced the error in GNSS-based positioning systems through multi-information fusion technology, intentional interference may render GNSS unusable in certain specialized fields, such as military applications. Therefore, achieving relative positioning of MAVs in GNSS-denied scenarios has become a potentially fruitful area of research.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Sensors

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Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

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Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Sensors

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“…For the short and medium distance (e.g., 0~30km) baseline, inaccuracies in the satellite and receiver clocks are eliminated, and the ionospheric and tropospheric delays can be ignored. Then the between-receiver and between-satellite double differenced observation equation can be expressed as [31]- [32]:…”

Section: A Positioning and Navigation With Gnss Sensormentioning

confidence: 99%

Improved Multi-Sensor Fusion Positioning System Based on GNSS/LiDAR/Vision/IMU With Semi-Tight Coupling and Graph Optimization in GNSS Challenging Environments

Zhu,

Zhou,

Wang

et al. 2023

IEEE Access

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With the development of autonomous driving, precise positioning capabilities are becoming increasingly important. GNSS (Global Navigation Satellite System) is normally utilized for vehicle positioning, but is susceptible to factors such as urban canyons, especially in increasingly urbanized scenario nowadays. The interpretation of relative positioning information by means of multi-source sensors such as LiDAR (Light Detection And Ranging) or camera, has also been widely investigated, but there are deficiencies in the precision and reliability of the sensors due to their operating principles. For GNSS challenging environments, we give a semi-tightly coupled sensor fusion system based on factor graph optimization. The system is tightly coupled with the raw observations from the LiDAR, camera, and inertial measurement unit. Based on this, the back end is optimized by means of the factor graphs, which ultimately give optimized positional information of the vehicle, and the GNSS sensor is decoded to reduce the cumulative error, which is regarded as the loosely coupled component of graph optimization. We selected three representative datasets from publicly available datasets in different categories and conducted experiments respectively. The results indicate that this semi-tightly coupled system outperforms both the GNSS strategy and the typical SLAM (Simultaneous Localization And Mapping) strategies in terms of precision and possesses very high reliability, exhibiting the best performance.

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“…The evaluation was carried out using GPS location values as the reference [61], and the results are shown in Figure 10. The GPS position values were obtained through the GNSS (the Global Navigation Satellite System) Real-Time Kinematic (RTK)/UWB/DBA Fusion Positioning Method, which can achieve a positioning error of less than 5 cm under a 50-degree elevation mask angle [62].…”

Section: Position Matching Accuracy Evaluationmentioning

confidence: 99%

Evaluation of Simulated CO2 Point Source Plumes from High-Resolution Atmospheric Transport Model

Li,

Wang,

et al. 2023

Remote Sensing

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Coal-fired power plants, as major anthropogenic CO2 emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO2 emissions from these point sources is crucial, as it will aid in quantifying CO2 emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO2 diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO2 simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO2 diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO2 diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R2 (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO2 point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO2 point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO2 concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO2 emissions using top-down approaches.

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GNSS RTK/UWB/DBA Fusion Positioning Method and Its Performance Evaluation

Cited by 5 publications

References 38 publications

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Improved Multi-Sensor Fusion Positioning System Based on GNSS/LiDAR/Vision/IMU With Semi-Tight Coupling and Graph Optimization in GNSS Challenging Environments

Evaluation of Simulated CO2 Point Source Plumes from High-Resolution Atmospheric Transport Model

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