|
|
Journal of Global Positioning Systems Vol. 21, No. 2, 2025 |
Cover |
|
Journal of Global Positioning Systems
|
JGPS Team Structure, Copyright and Table of Contents |
|
JGPS Team Structure, Copyright
|
1. Integrity Assessment and Error Modeling of BDS PPP-B2b Signal-in-Space for ARAIM |
|
Yaping Huang, Yun Wu, Yaxuan Li, Lang Feng
BDS provides PPP (Precise Point Positioning) service via the B2b signal transmitted by BDS-3 GEO satellites for users in China and surrounding areas. For high-precision safety-of-life applications, the integrity of its Signal-in-Space (SIS) is critical. In addition, Advanced Receiver Autonomous Integrity Monitoring (ARAIM), as a key approach of GNSS integrity monitoring, relies on reliable prior integrity parameters of SIS to ensure its results trustworthy. Aiming at integrity assessment and SIS error modeling of PPP-B2b, this study first computes the PPP-B2b Signal-in-Space Errors (SISEs) from January 1, 2021, to December 31, 2024, by applying PPP-B2b correction messages to the BDS-3 CNAV1 broadcast ephemerides and comparing the corrected orbits and clocks with precise ephemerides. Based on the four-year SISEs datasets, the integrity assessments show that PPP-B2b SISEs exhibit improved smoothness compared to CNAV1. The RMS of Orbit-Only User Range Errors (UREs) improves by 5.7%, while clock performance shows only marginal improvement for four satellites. There is no significant spatial correlation of SISEs between satellites. Furthermore, the empirical distribution of Instantaneous UREs (IUREs) indicates that most satellites exhibit non-zero mean, multimodality, and asymmetry characteristics which contradict the ARAIM assumption of zero-mean Gaussian distribution. Additionally, the User Range Accuracy (URA) values broadcast by PPP-B2b are overly tight and fail to adequately bound the actual IUREs, which improperly describe the deviations of UREs. To obtain reliable SISEs stochastic model for PPP-B2b ARAIM users, the integrity support parameters under SIS nominal condition are derived by taking use of the Two-Step Gaussian overbounding method. The derived SIS integrity parameters can be referred by ARAIM users and the integrity assessment provides a key reference for subsequent optimization of system integrity performance.
|
2. Analysis of the influence of strong magnetic storms on precise point positioning in China |
|
Wei Xiang, Xiaomin Luo, Zhuang Chen, Biyan Chen, Ankang Xie, Lingqiao Zeng, Yingrui Wang, Kamarul Hawari Bin Ghazali
It is currently in the active period of the 25th solar cycle, and magnetic storm events occur frequently. To study the impact of magnetic storms on precise point positioning (PPP) in China, PPP experiments were carried out using 17 IGS stations data in China based on a total of 84 strong magnetic storm events from 2000 to 2024. The rate of total electron content index (ROTI) and the detrended total electron content (dTEC) were used to reflect the impact of magnetic storms on the ionosphere over the 17 stations. GNSS PPP positioning accuracy of magnetic static days and magnetic storm days was compared and analyzed. Meanwhile, the cluster analysis method was introduced to investigate the correlation between GNSS positioning accuracy and latitude during magnetic storms. Experimental results show that 50%-60% of strong magnetic storms do not significantly impact the GNSS PPP positioning accuracy and regional ionospheric environment of these IGS stations in China. The ionosphere in the low-latitude region of China is more sensitive to magnetic storms, and in the strong magnetic storm environment, the ROTI index can reach 4 TECU/min, the ambiguity resolved percentage decreases significantly, and the GNSS PPP 3D error can reach several meters. GNSS stations in the mid-latitude region can show relatively stable PPP positioning results in the magnetic storm environment, and more than 90% of the magnetic storms do not cause obvious positioning errors in the mid-latitude stations.
|
3. Enhancing GPS satellite code bias performance for PNT services |
|
Ke Su, Ying Xu, Guoqiang Jiao, Yuze Yang
Global Positioning System (GPS) flex power adjustment significantly impacts on the quality of Positioning, Navigation, and Timing (PNT) services. This is because conventional code biases provided by agencies often fail to fully meet the diverse and evolving user requirements. In response to this challenge, we propose an innovative code bias solution specifically tailored for GPS flex power scenarios. We begin by thoroughly assessing the performance of existing GPS code bias products, using them as benchmarks for comparison. Building on it, we introduce a novel code bias concept and elaborate on the estimation methodology. To precisely characterize code bias behavior, we develop a specialized metric that can effectively capture key performance dimensions. Experimental validation shows that the proposed solution remains compatible with conventional products during standard operations. Moreover, it demonstrates the ability to perform adaptive state transitions in response to complex conditions induced by flex power management. Comparative analysis across normal and flex power operational phases reveals multifaceted advantages for GPS, especially highlighting enhanced environmental adaptability and performance for PNT services, which are crucial for a wide range of applications.
|
4. A Method for Constructing Local Sea Surface Height Model in the South China Sea Using Dual-Source Spaceborne GNSS-R Data |
|
Fanfan Xu, Baogui Ke, Jinxin Hu, Yuxuan Tan
Traditional satellite altimetry technology is well-established, yet it still faces limitations in spatiotemporal resolution. Currently, multiple Global Navigation Satellite System Reflectometry (GNSS-R) missions, with their distinct orbital configurations and revisit cycles, provide opportunities to enhance Sea Surface Height (SSH) retrieval capabilities through multi-source data fusion. To address the limitations of single GNSS-R data sources in terms of spatiotemporal coverage and retrieval accuracy, this study proposes a dual-source GNSS-R data fusion method based on wavelet transform. First, SSH is independently retrieved from CYGNSS (an 8-satellite constellation) and FY-3E (a single satellite) L1-band observations using a Delay-Doppler Map (DDM)-based physical retrieval algorithm, followed by the application of error correction models to mitigate systematic biases. Subsequently, leveraging the superior time-frequency localization characteristics of wavelet transform, the two datasets are fused to integrate valid signals and suppress noise. Based on this, a regional SSH model for the South China Sea was constructed. Validation using five months of in situ measurement data shows that the fused model achieves a Mean Absolute Error (MAE) of 0.93 meters for SSH retrieval. This result significantly outperforms traditional single-source GNSS-R physical retrieval methods, with a 48% improvement in accuracy compared to the CYGNSS single-satellite retrieval result (MAE = 1.78 m). This study demonstrates that by fusing multi-source GNSS-R data, the retrieval results outperform traditional single-source GNSS-R physical retrieval methods, thereby providing a complementary SSH dataset with unique observational advantages for regions such as the South China Sea, distinct from conventional radar altimetry.
|
ABSTRACTS OF PHD DISSERTATIONS |
1. Research on the method and key technologies of timely water vapor monitoring with low-cost BDS/GNSS terminals |
|
Luohong Li
Atmospheric water vapor is an important greenhouse gas that plays a key role in atmospheric energy exchange, the formation and evolution of weather events such as clouds, rain, and lightning, the global water cycle, and the energy balance of the Earth-atmosphere system. With global climate warming, extreme weather events are becoming more frequent and intense, such as extreme heavy rainfall, typhoons, and floods, which pose significant threats to human production and daily life. As an important index for characterizing extreme weather and climate change, high spatiotemporal resolution and high-timeliness monitoring of atmospheric water vapor is of great significance for the forecasting and early warning of disaster weather and climate change research.Currently, existing real-time water vapor monitoring systems rely on high-cost geodetic GNSS (Global Navigation Satellite System) terminals and meteorological equipment, which are expensive to construct and maintain and difficult to expand in the short term. Low-cost BDS (BeiDou Navigation Satellite System)/GNSS terminals, with advantages of low cost and high accuracy, have potential for densifying the existing water vapor monitoring network, but there are still research limitations and application bottlenecks in real-time water vapor monitoring. To achieve high-density, high-precision, and high-timeliness water vapor monitoring, this study focuses on low-cost BDS/GNSS terminal equipment and explores methods for real-time tropospheric estimation, auxiliary parameter models for real-time water vapor retrieval, as well as low-cost and high-timeliness water vapor monitoring method and application in complex environments.
|
2. Precise GNSS Atmospheric Corrections and Services for Mass-market Applications |
|
Jianping Chen
Global Navigation Satellite Systems (GNSS) have seen a broad expansion in applications over recent decades. With the growth of multi-constellation systems including Global Positioning System (GPS), GLObal Navigation Satellite System (GLONASS), Galileo and Beidou, GNSS is increasingly used in diverse areas, from disaster monitoring, autonomous driving to smartphone-based navigation. However, as standard positioning methods often lack the precision needed for emerging technologies, advanced techniques like Precise Point Positioning (PPP) have been developed. To achieve high-precision positioning results, various corrections must be applied to mitigate various errors in GNSS measurements. Among major GNSS errors, the atmospheric effects including tropospheric and ionospheric errors have become the most significant error sources. Developing accurate atmospheric corrections and distributing them in real-time is crucial to enable fast convergence of PPP. The International GNSS Service (IGS) has been providing Real-Time Global Ionosphere Maps (RT-GIM) which are the combined products from the IGS real-time ionosphere centers. With the latest interpolation improvements, the RT-GIM accuracy is close to that of the IGS rapid GIMs. However, the prediction methods still need globally distributed real-time GNSS stations.This thesis explores the development of a regional precise atmospheric service, incorporating ionospheric and tropospheric models using machine learning techniques. For ionospheric prediction, a sequence-to-sequence long short-term memory (LSTM) deep learning method is used to predict ionospheric vertical delay maps. The Recurrent Neural Network (RNN) model, which evolved from feed-forward neural networks, is designed to deal with complex temporal problems. LSTM, a special form of the powerful Artificial Neural Network (ANN) model, is used to overcome the gradient varnishing issue from the traditional RNN. In this work, the IGS rapid GIMs are used as prediction bases so real-time GNSS datalinks are not needed. Various time windows are selected based on different Kp-index to represent different solar activity strengths over a region of southwestern Canada and northwestern US. The ionospheric corrections estimated from GNSS stations within the testing region are used to verify the model performance. Since the training is based on the IGS rapid GIM product and no more real-time GNSS data is needed, the model can provide improved cost-effective regional ionospheric services with high stability. Regarding tropospheric prediction, the IGS has provided precise troposphere correction data in TRO format post-mission, but its long latency of 1 to 2 weeks makes it unable to support real-time applications. In this work, a real-time troposphere prediction method based on the IGS post-processing products was developed using FFNN and LSTM machine learning techniques to eliminate the long latency problem. The test results of tropospheric predictions over the year of 2023 using the proposed method indicate that the new method can achieve a prediction zenith tropospheric accuracy (RMSE) of 2 cm, making it suitable for real-time applications.
|
3. Attitude Estimation Algorithms and Comprehensive Error Analysis in the Generic Multi-sensor Integrattegy |
|
Benjamin Brunson
Modern multi-sensor integrated kinematic position/attitude systems serve a diverse range of industries, including precision agriculture, construction,mobile mapping,and autonomous driving.These industries have diverse needs that must be met by their position/attitude systems, which emphasizes the need for PosAtt systems that are highly customizable and robust. Lower-cost sensors have become increasingly prevalent in modern position/attitude systems, and accurately modeling the systematic errors in these sensors is of paramount importance for high-accuracy applications.This dissertation focuses on extending and refining the tools for comprehensive error analysis in the Generic Multi-sensor Integration Strategy (GMIS), with a particular focus on statistical analysis in pre-and post-processing environments. This research leverages the strengths of the GMIS to rigorously characterize the sensor performance and systematic errors of tactical grade MEMS IMU sensors, even in situations where a reference solution is unavailable.
|
4. Research on the Theory and Methodology of GNSS/INS/Vision Resilient Fusion Navigation |
|
Long Li
With the rapid development of technologies such as autonomous driving, unmanned aerial vehicles, and robotics, high-precision and reliable navigation services have become an indispensable core requirement and fundamental guarantee. However, in complex environments with obstructions and interference, a single navigation modality often fails to meet the demand for high-precision positioning across all regions and scenarios. Multi-source heterogeneous sensor fusion navigation, by leveraging the complementary characteristics of different sensors and jointly processing various types of observations, can effectively compensate for the limitations of individual sensors and is regarded as a key approach to achieving high-precision and highly available navigation services in challenging environments. At present, issues such as dynamic allocation of fusion weights for heterogeneous observations, as well as online estimation and optimization of noise parameters, remain bottlenecks that hinder the further advancement of multi-source fusion navigation technology. Motivated by this, this dissertation focuses on the theory and techniques of GNSS/INS/vision multi-source sensor fusion navigation and positioning, with global navigation satellite system (GNSS) technology as the foundation, targeting complex environments characterized by obstructions and interference. The objective is to enhance the accuracy and robustness of navigation algorithms under such conditions.
|
Back Cover |
|
Journal of Global Positioning Systems
|