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Journal of Global Positioning Systems Vol. 20, No. 1& No. 2, 2024 |
Cover |
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Journal of Global Positioning Systems
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JGPS Team Structure, Copyright and Table of Contents |
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JGPS Team Structure, Copyright
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1. Refinement of elevation angle based stochastic model and positioning performance for QZSS |
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Yao Zong, Kaifei He, Xuchen Ma, Kai Ding, Yu Fu and Xiang Xu
An improved stochastic model refinement method is proposed to address the discrepancies in observation quality between the Quasi-Zenith Satellite System (QZSS) and the Global Positioning System (GPS). The proposed method refines the traditional empirical stochastic model of elevation angle to enhance the accuracy of baseline solutions. Specifically, the parameters of the refined model are estimated based on the least squares method by counting the time series of single difference residuals of QZSS/GPS satellites and analyzing their relationship with the change of elevation angle. To evaluate its effectiveness, the positioning accuracy of the refined model is comparatively assessed against that of the empirical model through baseline experiments of varying lengths. The results indicate that GPS satellite observations exhibit higher accuracy than those of QZSS. Moreover, compared with the empirical model, the refined model significantly improves positioning accuracy and stability. In the East, North, and Up, the root mean square (RMS) errors are reduced by at least 25.01%, 42.08%, and 4.37%, respectively, yielding an overall average positioning accuracy improvement of more than 31.92%.
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2. Double layer dynamic target capture algorithm based on improved EKF and GBNN |
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Qian Sun, Xingyu Zhou, Weiyang Zhao and Xing Jian
To address the challenges of energy constraints, high real-time requirements, and strong adversarial conditions in underwater dynamic target pursuit tasks, a highly efficient two-layer dynamic target pursuit algorithm is proposed to overcome the limitations of multi-autonomous underwater vehicle systems in terms of coordination efficiency and task execution. First, by analyzing the relative velocities of pursuers and the target, the Apollonius circle principle is extended to three-dimensional space, facilitating a pursuit strategy that aligns more effectively with real-world underwater conditions. Second, to mitigate the inherent measurement errors of sonar detection systems, an adaptive Kalman filter is designed to effectively suppress noise interference and achieve real-time, accurate prediction of the target AUV's motion trajectory. Furthermore, by reconstructing the neuronal activity propagation mechanism of the Glasius bio-inspired neural network, the collaborative decision-making process of multiple AUVs is optimized, significantly enhancing task execution efficiency. Simulation results demonstrate that the proposed algorithm improves pursuit distance and time by at least 30% and 24%, respectively. In multi-scenario generalization tests, the average pursuit distance and time are improved by at least 25% and 18%, respectively. In anti-interference tests under varying sonar detection accuracies, the average pursuit distance and time are enhanced by at least 25% and 22%, respectively. These results collectively validate the superior accuracy, robustness, and adaptability of the proposed algorithm.
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3. Real-Time Kinematic Performance Evaluation of Mosaic-H GNSS Receiver on UAV Platforms Under Diverse Environmental Conditions |
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Mnar Hmdy Abdalazeez Salem Saad, Ahmed Wasiu Akande, Odai Redhwan Mohammed Othman and Dongkai Yang
This paper presents a comprehensive
evaluation of the Mosaic-H GNSS receiver's Real-Time Kinematic (RTK) performance when integrated
with unmanned aerial vehicle (UAV) platforms.
Through systematic testing across varied operational
environments including open-sky, urban canyon, and
forested areas, we characterize the receiver's
positioning accuracy, fix reliability, and multipath
resilience. Our experimental methodology employed a
precisely configured base station broadcasting RTCM
corrections via Port 8105, with the UAV-mounted
receiver maintaining centimeter-level accuracy in
optimal conditions. Results demonstrate an 88.2%
RTK fixed solution rate across all test scenarios, with
horizontal accuracy averaging 3.1 cm in open
environments. Notable findings include the
identification of critical operational thresholds:
maintaining >10m clearance from buildings prevents
multipath-induced degradation, canopy coverage
exceeding 70% triggers fallback to float solutions, and
vehicle speeds above 8 m/s challenge phase tracking
capabilities at 5 Hz update rates. The multipath impact
score correlation analysis revealed strong relationships
between environmental factors and positioning quality
(R²=0.87), enabling predictive mitigation strategies.
These findings establish operational guidelines for
reliable centimeter-accuracy UAV navigation in
complex environments, with direct applications to
precision agriculture, infrastructure inspection, and
autonomous aerial surveying.
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4. Landslide hazard levels analysis considering displacement traction |
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Xingchi Chen, Yuzhi Meng, Junzhe Zhou, Yutao Zhou, Zhengdong Leng and Kun Wang
Accurately assessing the hazards posed by landslides is of great importance for disaster prevention and mitigation. This study proposes a method of landslide hazard levels analysis based on displacement traction, a term referring to the correlated directional influence between surface displacement vectors at GNSS (Global Navigation Satellite System) monitoring points. By analyzing these spatial correlations, the optimal grid unit size is determined for refined hazard levels assessment. To construct a representative target area, the improved Sparrow Search Algorithm was combined with the k-means clustering algorithm, integrating displacement characteristics and the derived grid unit size. A hazard assessment dataset was then developed for the target area. Subsequently, a stacking ensemble model was employed to evaluate landslide hazard levels using eleven influencing factors, including surface roughness, elevation, and slope angle. Experimental results demonstrated that the proposed method outperformed the conventional fixed-grid approach, yielding a 2.15% improvement in overall accuracy, a 1.30% increase in F1-score, and a 3.75% gain in the kappa coefficient. This study not only enriches the theoretical foundation of assessing landslide hazard levels but also provides a powerful technical support and practical guidance for the scientific prevention and control of landslide disasters.
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5. A Deep Learning-Based Regional Atmospheric Weighted Mean Temperature Model for China |
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Ruizhao Jiang, Bo Li, and Huizhong Zhu
To further enhance the accuracy of atmospheric weighted mean temperature (Tm) models in ground-based Global Navigation Satellite System (GNSS) retrieval of precipitable water vapor (PWV), we propose and develop a regionally adaptive Multi-Hidden-Layer neural network for Tm, hereafter referred to as MHL_Tm. A multiparameter cooperative Tm-modeling framework has been established using radiosonde observations from 65 launch sites across China during 2014-2018. We analyzed the nonlinear coupling between surface temperature (Ts), surface water-vapor pressure (e), latitude (Lat), elevation (H), and the temporal factor day of year (DOY) with radiosonde-derived integral Tm values. Radiosonde data from 2019 served as an independent reference to evaluate MHL_Tm's performance, which was then compared against the Bevis, GPT3, and Elastic Net models. Experimental results showed that the annual mean bias of MHL_Tm was -0.61 K, representing reductions of 30 % and 58 % relative to Bevis and GPT3, respectively, and slightly higher than Elastic Net (-0.11 K). The annual mean RMSE of MHL_Tm was 2.77 K, corresponding to improvements of 35 %, 62 %, and 18 % over Bevis, GPT3, and Elastic Net, respectively. Across different latitudinal and altitudinal zones in China, MHL_Tm exhibited superior accuracy and stability compared to Bevis, GPT3, and Elastic Net, demonstrating excellent regional applicability.
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6. Precision Evaluation in Discrete Kalman Filtering: A Posteriori Perspective |
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Jianguo Wang, Benjamin Brunson and Boxiong Wang
This manuscript is focused on standardizing the process of the a posteriori precision evaluation in discrete Kalman filtering. Although the a posteriori precision evaluation of the solution was considered as indispensable within the method of least squares, the solution of a Kalman filter shows a lack of a posteriori precision evaluation for too long. Even worse, there often exists appalling confusion about what is considered as the a posteriori precision of the solution in Kalman filtering. The authors hereto propose to put the a posteriori precision evaluation of the solution into practice at four different levels in Discrete Kalman filtering through estimating: (i) the a posteriori variance of unit weight (or reference variance), (ii) the separate a posteriori variance factors for the process and measurement noise vectors, respectively, (iii) the individual a posteriori variance factors for the independent noise groups, and (iv) the individual a posteriori variance factors (or components) for the independent process noise factors and measurement types. A working example is presented to illustrate the proposed a posteriori precision evaluation in Kalman filtering using a road test based on the double-differenced GPS L1 C/A, L1 and L2 carrier phases and the specific force and angular rate measurements from an MEMS IMU. With the rapidly increasing utilization of the Kalman filter in modern applications, the inclusion of the proposed a posteriori solution precision evaluation, as part of the standard solution, in discrete Kalman filtering is not only necessary, but also can be expected to happen soon within our grasp.
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ABSTRACTS OF PHD DISSERTATIONS |
1. Structural Health Monitoring Based on Integration of GNSS and In-situ Sensors |
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Xuanyu Qu
Structural health monitoring (SHM) is
essential in ensuring the safety of large civil
engineering structures. Global Navigation
Satellite Systems (GNSS) based technology has
been commonly used in SHM systems due to its
unique ability to obtain real-time threedimensional (3D) displacement information.
GNSS equipment with data sampling rate of up
to about 20 Hz has been widely used for this
purpose. High-rate GNSS systems (typically up
to about 100 Hz) offer additional advantages in
structural health monitoring as some highly
dynamic civil structures such as some bridges
require high-rate monitoring data to capture the
dynamic behaviors. The performance of highrate GNSS positioning in the context of
structural health monitoring is however not
entirely known as studies on structural
monitoring with high-rate GNSS positioning are
very limited, especially considering that some of
the satellite systems just reached their full
constellations very recently. In addition, GNSS
measurements are often integrated with other
sensors to enhance the performance of the
systems. However, measurement outliers
including biases and systematic errors that can
significantly reduce the accuracy of SHM results
have rarely been considered in the integration
processes. In addition, a comprehensive
deformation mechanism analysis facilitates
structural health assessment and maintenance.
Numerous studies have explored the relationship
between displacements and single loading,
whereas analyzing the interactions of individual
factors with deformation in a multi-loading
scenario has not been commonly studied.
Therefore, understanding displacement
mechanisms of structures accurately is still an
urgent issue to be addressed. Some integration
approaches are developed in this thesis to fill
these gaps.
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2. Smartphone precise positioning in urban environments using internal GNSS and IMU sensors |
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Yi Ding
The last decade has seen the modernization of Global
Navigation Satellite Systems (GNSS) and the
proliferation of multi-constellation, multi-frequency
GNSS chipsets. This progress has boosted the surge of
GNSS-enabled handsets and revolutionized receiver
industries and Location-Based Services (LBS) for
mass-market applications. These applications,
including autonomous driving, social networking,
health tracking, and personal/property security,
increasingly demand higher levels of user positioning
accuracy and reliability. In this context, a fundamental
push for smartphone positioning came in 2016 after
Google released the Android 7.0 platform, allowing
users and developers the capability to access GNSS raw
measurements, including carrier-phase observations,
catalyzing the progress of smartphone precise
positioning using Real-Time Kinematic (RTK) and
Precise Point Positioning (PPP) technologies. However,
challenges such as high received signal noise, limited
multipath suppression capabilities, and frequent signal
losses are prevalent in smartphone-grade GNSS
receivers and antennas. These factors notably degrade
the performance of smartphone positioning for precise
applications.
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Back Cover |
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Journal of Global Positioning Systems
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