A Dynamic Continuous Constrained Phase Factor Graph Optimization Method for GNSS Kinematic Precise Point Positioning

Shuai, Qiangqiang; Guo, Fei; Li, Guangchen; Zhao, Xinyang; Zhu, Bocheng; Sun, Junren

doi:10.1109/jsen.2023.3264782

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…By adding a sliding window for factor graph optimisation, the robustness of the integrated navigation system can be further enhanced when the GNSS multipath effect is severe [24,25]. Another approach decomposes the GNSS measurement factors into more accurate measurements to reduce the impact of unreliable GNSS factors on the overall optimisation process, for example, by adding carrier phase factors based on integral Doppler estimation to the factor graph model [26,27].…”

Section: Factor Graph Optimisationmentioning

confidence: 99%

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Hu,

Li,

Liu

2023

IET Radar Sonar & Navi

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Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter.

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Section: Factor Graph Optimisationmentioning

confidence: 99%

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Hu,

Li,

Liu

2023

IET Radar Sonar & Navi

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“…The GNSS and 5G joint-positioning methods are mainly divided into three types. Firstly, GNSS positioning results are dominant, and 5G base stations are utilized as communication means to transmit GNSS-enhanced information [13,14]; secondly GNSS positioning signals are blocked or suffer from significant errors, and the positioning result of 5G PRS signal is dominant [11]; thirdly the positioning terminal can receive the GNSS signal and the 5G positioning signal at the same time and can obtain the positioning result through the fusion of the two signals [8][9][10]12]. Therefore, the fusion positioning algorithm has become a research hotspot [2,12].…”

Section: Introductionmentioning

confidence: 99%

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

Liu,

Deng,

et al. 2023

Remote Sensing

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The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.

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A Method for PPP Reliability Improvement Based on Integrated Multichannel Observations

Lei,

Wenqiang,

Shangyang

et al. 2024

IEEE Sensors J.

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A Dynamic Continuous Constrained Phase Factor Graph Optimization Method for GNSS Kinematic Precise Point Positioning

Cited by 3 publications

References 30 publications

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

A Method for PPP Reliability Improvement Based on Integrated Multichannel Observations

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