Abstract:In this contribution, we present a GPS+GLONASS+BeiDou+Galileo four-system model to 10 fully exploit the observations of all these four navigation satellite systems for real-time precise orbit 11 determination, clock estimation and positioning. A rigorous multi-GNSS analysis is performed to achieve
The world of satellite navigation is undergoing dramatic changes with the rapid development of multi-constellation Global Navigation Satellite Systems (GNSSs). At the moment more than 70 satellites are already in view, and about 120 satellites will be available once all four systems (BeiDou + Galileo + GLONASS + GPS) are fully deployed in the next few years. This will bring great opportunities and challenges for both scientific and engineering applications. In this paper we develop a four-system positioning model to make full use of all available observations from different GNSSs. The significant improvement of satellite visibility, spatial geometry, dilution of precision, convergence, accuracy, continuity and reliability that a combining utilization of multi-GNSS brings to precise positioning are carefully analyzed and evaluated, especially in constrained environments.
The latest generation of GNSS satellites such as GPS BLOCK-IIF, Galileo and BDS are transmitting signals on three or more frequencies, thus having more choices in practice. At the same time, new challenges arise for integrating the new signals. This paper contributes to the modeling and assessment of triple-frequency PPP with BDS data. First, three triple-frequency PPP models are developed. The observation model and stochastic model are designed and extended to accommodate the third frequency. In particular, new biases such as differential code biases and inter-frequency biases as well as the parameterizations are addressed. Then, the relationships between different PPP models are discussed. To verify the triple-frequency PPP models, PPP tests with real triple-frequency data were performed in both static and kinematic scenarios. Results show that the three triple-frequency PPP models agree well with each other. Additional frequency has a marginal effect on the positioning accuracy in static PPP tests. However, the benefits of third frequency are significant in situations of where there is poor tracking and contaminated observations on frequencies B1 and B2 in kinematic PPP tests. B Xiaohong Zhang
The emergence of China’s Beidou, Europe’s Galileo and Russia’s GLONASS satellites has multiplied the number of ionospheric piercing points (IPP) offered by GPS alone. This provides great opportunities for deriving precise global ionospheric maps (GIMs) with high resolution to improve positioning accuracy and ionospheric monitoring capabilities. In this paper, the GIM is developed based on multi-GNSS (GPS, GLONASS, BeiDou and Galileo) observations in the current multi-constellation condition. The performance and contribution of multi-GNSS for ionospheric modelling are carefully analysed and evaluated. Multi-GNSS observations of over 300 stations from the Multi-GNSS Experiment (MGEX) and International GNSS Service (IGS) networks for two months are processed. The results show that the multi-GNSS GIM products are better than those of GIM products based on GPS-only. Differential code biases (DCB) are by-products of the multi-GNSS ionosphere modelling, the corresponding standard deviations (STDs) are 0.06 ns, 0.10 ns, 0.18 ns and 0.15 ns for GPS, GLONASS, BeiDou and Galileo, respectively in satellite, and the STDs for the receiver are approximately 0.2~0.4 ns. The single-frequency precise point positioning (SF-PPP) results indicate that the ionospheric modelling accuracy of the proposed method based on multi-GNSS observations is better than that of the current dual-system GIM in specific areas.
With the development of precise point positioning (PPP), the School of Geodesy and Geomatics (SGG) at Wuhan University is now routinely producing GPS satellite fractional cycle bias (FCB) products with open access for worldwide PPP users to conduct ambiguity-fixed PPP solution. We provide a brief theoretical background of PPP and present the strategies and models to compute the FCB products. The practical realization of the two-step (wide-lane and narrow-lane) FCB estimation scheme is described in detail. With GPS measurements taken in various situations, i.e., static, dynamic, and on low earth orbit (LEO) satellites, the quality of FCB estimation and the effectiveness of PPP ambiguity resolution (AR) are evaluated. The comparison with CNES FCBs indicated that our FCBs had a good consistency with the CNES ones. For wide-lane FCB, almost all the differences of the two products were within ±0.05 cycles. For narrow-lane FCB, 87.8 % of the differences were located between ±0.05 cycles, and 97.4 % of them were located between ±0.075 cycles. The experimental results showed that, compared with conventional ambiguity-float PPP, the averaged position RMS of static PPP can be improved from (3.6, 1.4, 3.6) to (2.0, 1.0, 2.7) centimeters for ambiguity-fixed PPP. The average accuracy improvement in the east, north, and up components reached 44.4, 28.6, and 25.0 %, respectively. A kinematic, ambiguity-fixed PPP test with observation of 80 min achieved a position accuracy of better than 5 cm at the one-sigma level in all three coordinate components. Compared with the results of ambiguity-float, kinematic PPP, the positioning biases of ambiguity-fixed PPP were improved by about 78.2, 20.8, and 65.1 % in east, north, and up. The RMS of LEO PPP test was improved by about 23.0, 37.0, and 43.0 % for GRACE-A and GRACE-B in radial, tangential, and normal directions when AR was applied to the same data set. These results demonstrated that the SGG FCB products can be produced with high quality for users anywhere around the world to carry out ambiguity-fixed PPP solutions.
The application of deep learning technology to ionospheric prediction has become a new research hotspot. However, there are still some gaps, such as the prediction effect with different input solar and geomagnetic activity parameters, and the forecast accuracy with different prediction methods as well as the validation of long period data results, to be filled. We developed an ionospheric long short‐term memory network (Ion‐LSTM) with multiple input parameters to predict the global ionospheric total electron content (TEC). Two solutions with different ionospheric data based on Ion‐LSTM were assessed, namely spherical harmonic coefficients (SHC) and vertical TEC (VTEC) prediction solution. The results show two solutions, both perform well in accuracy and stability. The input of the geomagnetic activity index improves the prediction effect of the model in the storm period. For the 1‐ and 2‐day‐predicted global ionospheric maps (GIMs) from 2015 to 2020, the root mean square error (RMSE) of SHC prediction solution is 1.69 TECU and 1.84 TECU while that of the VTEC prediction solution is 1.70 TECU and 1.84 TECU, respectively. Over 70% of the absolute residuals are within 3 TECU in high solar activity and over 96% in low solar activity. Further, by comparing the predicted results between Ion‐LSTM and conventional methods (e.g., Center for Orbit Determination in Europe (CODE) predicted GIMs), the evaluation results show that the RMSE of Ion‐LSTM is 0.7 TECU lower than that of CODE predicted GIMs under different solar and geomagnetic activities. Additionally, the accuracy of the Ion‐LSTM prediction results decreases slightly as the input time span increases.
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