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Journal of Global Positioning Systems Vol. 19, No. 1& No. 2, 2023 |
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. A Survey for GNSS Application of Ionospheric Information Extraction, Modeling, and Forecasting Techniques |
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Jinpei Chen, Nan Zhi, Bingqing Feng, Menglong Wei, Yi Zhao and Mingquan Lu
lonospheric information plays a signifcant role in modern communication and navigation systems, This article provides a comprehensive survey of the application, modelingmethods,and results related to ionospheric infor-mation, The article frst introduces prerequisiteknowledge of the ionosphere,and then describesthe methods and techniques used in the extrac-tion of ionospheric information, the generationof ionospheric Vertical Total Electron Content(VTEC) maps, the modeling and interpolation ofthe ionosphere, and the forecasting of ionosphericinformation, The article also provides illustrativeexamples and fgures to demonstrate the effectiveness of the presented methods. Our surveyprovides insights and guidance for researchers andpractitioners interested in developing ionosphericmodeling and forecasting methods for GNSS applications.
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2. Fractional Kalman filtering model on data of monitoring accelerometer deformation |
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Tao Jiang, Jian Wang and Yv Bai
The deformed or vibratory behaviors will
exceed the threshold of building under the influence
of external factors, so that it is necessary to monitor
the variety of deformed body. Accelerometer is
widely used in deformation monitoring due to small
size and high sampling rate. In this paper, the
fractional Kalman filter is introduced to update the
accelerometer data. The influence of the order of
different fractional derivatives on the filtering results
of the accelerometer is studied and compared. The
results show that when the system noise and
measurement noise are fixed, using different
derivative orders and comparing the filtering results
under different derivative orders, the root mean
square error of the fractional filtering model is
smaller. Compare the filtering results under different
noise variances. By comparing the errors of the two
models, the image shows that the fractional Kalman
filter model has better filtering performance than the
standard Kalman filter model.
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3. Analyzing the influence of the February 26-28th, 2023 Geomagnetic Storm on Global Ionospheric Scintillation and GPS Positioning |
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Shuanglei Cui, Xueli Zhang, Dongsheng Zhao, Qianxin Wang, Craig M. Hancock, Kefei Zhang
Geomagnetic storms, which follow solar activities
like solar flares, coronal mass ejections, and high-speed
solar wind streams, are significant disturbances in the
global space environment. These storms occur when
high-speed plasma clouds, generated by solar activity,
reach the vicinity of Earth a few days later, causing
disruptions in the Earth's magnetic field. This
phenomenon is known as a geomagnetic storm
(Gonzalez et al., 1994). Geomagnetic storms have a
profound impact on GPS Precise Point Positioning
(PPP) by amplifying and varying ionospheric delays in
GPS phase and code data. This, in turn, affects highprecision GPS relative positioning (Odijk, 2001). In
low-to-mid latitudes, geomagnetic storms can even
cause disruptions in total electron content (TEC) and
result in satellite signal loss (Astafyeva et al., 2014).
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4. Practical Studies of Accuracy Enhancement Techniques for Terrestrial Mobile LiDAR Point Clouds in Engineering Surveys |
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Wang, Jianguo; Guannan Liu and Baoxin Hu
Improving the accuracy of Terrestrial
Mobile LiDAR (TML) data has been a challenge in
Engineering Surveys. This research aims at how to
innovatively enhance the accuracy of TML solutions
through post-processing toward meeting high
accuracy specifications in Engineering Surveys.
Three techniques are described and implemented.
Firstly, the linear feature-enhanced 3D Conformal
Coordinate Transformation (3DCCT) is developed by
employing ground control points (GCPs) together
with linear feature constraints. Secondly, a two-stage
Multistrip Adjustment (MA) technique is proposed
that first co-register the overlapped TML strips using
tie points and tie features extracted from them and
then adjust the co-registered LiDAR data by applying
the feature enhanced 3DCCT. Lastly, a post-processing technique for calibrating the LiDAR
boresight errors of a terrestrial LiDAR system is
tested out by using its own point clouds. Their usage
has been strategically studied through their
applications to field-test data. Specifically, multiple
scenarios have been tested, analysed, and compared
in terms of the usage of GCPs, the effect of feature
constraints, MA and the effect of boresight error
compensation etc. As shown from the results, their
utilization is encouragingly contributing to the
accuracy improvement of TML data towards the high
accuracy demand for Engineering Surveys. A
practical implementation dataflow is outlined at the
end of this manuscript.
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5. Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia |
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Bemnet Amsalu Hailegiorgis, Dongkai Yang, Xuebao Hong and Lwin Aung
Soil moisture (SM) plays a vital role in
agriculture, ecosystem functioning, water
conservation, weather predictions and climate models.
High spatial and temporal frequency data of soil
moisture is crucial for agricultural and other
important applications. Recent advancements have
brought attention to the possibility of using GNSS
reflectometry (GNSS-R) for applications on land
such as snow sensing, soil moisture retrieval, sea
surface monitoring and other applications in addition
to positioning, navigation, and timing applications of
GNSS. Cyclone Global Navigation Satellite System
(CYGNSS) is designed to improve hurricane
forecasting by studying the interaction between the
ocean and the atmosphere within tropical cyclones.
However recent studies show the opportunity of this
system for high spatio-temporal soil moisture
retrieval. This study presents a machine
learning-based approach to get SM at a selected
region in Ethiopia using CYGNSS data and analysis
of the result. Artificial Neural Network (ANN) model
is developed and used to predict soil moisture. The
Soil Moisture Active Passive (SMAP) global soil
moisture data have been used as reference data in the
ML algorithm. The proposed approach has achieved a
good correlation between predicted values of soil
moisture and reference values from SMAP.
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6. Improving smartphone-based positioning accuracy with height constraint and application to pedestrian and vehicular positioning |
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Farzaneh Zangenehnejad and Yang Gao
Since the release of Android version 7 in
2016, the smartphone users have had access to the
raw global navigation satellite system (GNSS)
measurements (i.e., pseudorange, carrier-phase,
Doppler, and carrier-to-noise density ratio (C/N0))
through the new application programming interface
(API) called android location (API level 24). This
capability opens opportunities to apply different
positioning techniques, ranging from absolute to
differential techniques, to the smartphone
observations. Precise point positioning (PPP) is a
powerful method for conducting accurate real-time
positioning using a single receiver, and it can be
applied to the smartphone observations as well. Most
PPP smartphone positioning studies have so far
focused on utilizing the GNSS only observations
obtained from the smartphone's API. However,
incorporating additional information as constraints,
such as height information, can enhance accuracy and
overall stability. Although the vertical positioning
accuracy of GNSS is generally lower than the
horizontal accuracy, utilizing recorded height from
the smartphone GNSS chipset can still be beneficial.
This incorporation increases the degree of freedom
and strengthens the geometry between the receiver
and satellites. In this study, we assess the
effectiveness of the uncombined PPP (UPPP) model
in the presence of height constraints. We utilize both
pedestrian walking and vehicular datasets collected
by a dual-frequency Xiaomi Mi8 device to evaluate
the effect of adding height constraint to PPP model.
The results demonstrate an average improvement of
22% and 26% on the root-mean-square (RMS) of
horizontal error and the 50th percentile error,
respectively, when employing the height constraints
UPPP model. Additionally, the findings indicated a
decrease in PPP convergence time, further supporting
the positive impact of incorporating height
constraints.
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7. Three Attitude Models and their Characterization in the Generic Multisensor Integration Strategy for Kinematic Positioning and Navigation |
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Benjamin Brunson and Jianguo Wang .
This research aims at further completing
our novel Generic Multisensor Integration Strategy
(GMIS) with the systematic development of three
alternate attitude models, i.e., roll-pitch-heading (RPH),
direction cosine matrix (DCM), and quaternion. The
GMIS’ potential for a true sensor level data fusion is
leveraged to its full extent here by facilitating
comprehensive error analysis framework in Kalman
filtering. A comparative analysis between the solutions
resulted from the GMIS associated with each attitude
model have been analysed and compared through real
road test data. The attitude models were found to
perform very consistently, exhibiting the same
behaviours in the residuals of the process noise and
measurement vectors along with the estimated variance
components. Besides, an analysis was conducted to
investigate how each attitude model reacts to a sudden
trajectory variation captured by the IMU. Each attitude
model still performed consistently, but the DCM model
in particular exhibited resistance to absorbing
erroneous observations into its process noise estimates.
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8. Global and Regional Consistency of VTECs Inversed by IPM and GNSS |
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Yunri Fu, Ling Yang, Liping Fu and Ruyi Peng
The ionospheric photometer (IPM)
onboard the Fengyun-3D satellite is the first optical
remote sensing payload in China designed for
space-based surveillance of the ionosphere and
capable of inversing the Vertical Total Electron
Content (VTEC) at night with high sensitivity. Using
the VTECs inversed by IPM, along with other sources
of ionospheric observations, such as the ground-based
Global Satellite Navigation System (GNSS) VTECs,
ionospheric modeling can be established based on
multi-source space observation techniques and is
expected to improve the modeling accuracy in marine
areas. Before the ionospheric modeling using
multi-source data, the consistency between different
sources of VTECs should be analyzed. However, the
consistency between the IPM-VTEC and
GNSS-VTEC has not been adequately investigated.
The authors employ the Global Ionospheric Map
(GIM) products from the International GNSS Service
to assess the global-scale consistency between them,
and the analysis reveals a generally high global-scale
consistency, yet the IPM-VTEC values in specific
regions are too large due to the influence of auroras
and ionospheric equatorial anomalies.
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9. Research on modeling and predicting of BDS3 satellite clock bias using the attention mechanism-based LSTM (AttLSTM) neural network model |
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In the Global Navigation Satellite System
(GNSS), the satellite clock bias (SCB) plays an
important role in the application of real-time precise
point positioning (RT-PPP). Based on the operation
of Beidou satellite global service, it is very important
to establish a reliable Beidou SCB prediction model.
In this research, an attention mechanism-based long
short-term memory neural network (AttLSTM)
model is applied to SCB prediction. The attention
mechanism introduced in modelling can make the
model pay less attention to useless information
through weight allocation. In this paper, the
BeiDou-3 Navigation Satellite System (BDS-3)
satellite precision clock product provided by GFZ is
used for clock prediction experiments. The proposed
AttLSTM model, long short-term memory neural
network (LSTM) model and quadratic polynomial
(QP) model are compared and evaluated, and 12h and
24h SCB prediction experiments of BDS-3 satellite
are set up. The results show that AttLSTM model can
achieve high SCB prediction accuracy, and the
averaged prediction accuracy of 12h and 24h can
reach 1.41ns and 1.75ns. Compared with LSTM and
QP models, the prediction accuracy of AttLSTM
model is improved by 26.1%, 38.4% for 12h and
29.1%, 43.1% for 24h, respectively. Then, the clock
bias predicted by the three models is applied to the
static PPP positioning experiment, respectively.
Through the analysis of the positioning results of 15
MGEX stations, the averaged positioning accuracy of
AttLSTM model in the East, North and Up directions
can reach 0.074m, 0.019m and 0.154m, respectively.
Compared with LSTM and QP models, the
positioning accuracy of AttLSTM model is improved
by 42.5% and 44.4% in the East direction, 44.7% and
58.9% in the North direction, and 21.7% and 21.8%
in the Up direction.
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ABSTRACTS OF PHD DISSERTATIONS |
1. Investigating the Impacts of Tropical Cyclones in Hong Kong Region using GNSS Techniques |
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Shiwei YU
A tropical cyclone (TC) is a severe weather
phenomenon over the equatorial and subequatorial
ocean areas. During the TC period, the intense air
convection often forms and progresses in the lower
atmosphere, i.e. troposphere. One of TC’s energy
sources is water vapor, which is evaporated from the
sea surface and released in the form of rainfalls and
rainstorms. That is why TC is often in company with
heavy rainfalls, strong winds, and frequent lightning.
These weather phenomena often cause severe
damage to coastal regions during the landfall period.
Furthermore, the deep convection in the troposphere
can trigger ionospheric disturbances. Sequentially,
the abnormal variation of the total electron content
(TEC) in the ionosphere will affect the performance
of radio communication applications, e.g. the Global
Positioning System (GPS) or the Global Navigation
Satellite System (GNSS). Therefore, it is essential
and meaningful to investigate the characteristics and
impacts of TCs on the troposphere, ionosphere, and
GPS performance. Deep investigation of TCs can
help further improve the understanding of the TC’s
mechanism and the capacity of TC forecasting.
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2. GNSS/UWB/VIO Integration for Precise and Seamless Robotic Localization |
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Liu Tianxia
Autonomous mobile robots are becoming
increasingly prominent in modern societies,
revolutionizing various industries with their wideranging applications. To ensure precise autonomous
operations, seamless localization with centimeter to
decimeter level accuracy is required. However, the
complex urban environments and the equipment of
low-cost devices pose significant challenges in
achieving reliable and accurate robotic localization.
In this thesis, a multi-sensor integrated seamless
positioning system combining low-cost GNSS, UWB,
MEMS IMU, and stereo cameras is developed to
enhance robotic localization in urban environments.
The core idea is to first improve absolute ranging
accuracy and continuity, thus ensuring globally
precise positioning. Then, incorporate relative
positioning sensors to enhance performance in highly
obscured environments.
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3. Improving Generic GNSS Receiver Performance with Intermediate Frequency Adaptive Anti-Jamming Technology |
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Mengyu Ding
Jamming attacks can severely disrupt Global
Navigation Satellite Systems (GNSS) performance,
posing significant threats to the resilience of
Positioning, Navigation and Time (PNT) service. To
mitigate jamming influence, extensive anti-jamming
technologies, such as multiple antenna arrays or
robust baseband processing algorithms, are
conducted. However, these methods are typically
employed in specialized GNSS receivers, as they
require extra antenna or modification of baseband
algorithms. Massive off-the-shelf GNSS receivers,
widely used in personal devices, communications,
and reference stations, rely on Intermediate
Frequency (IF) anti-jamming filters. However, these
filters have limitations that they are effective for
specific jamming signals and cause GNSS signal
distortion. Due to diverse and variable jamming
conditions in real environments, there is still an
urgent demand of adaptive jamming countermeasures
for generic GNSS receivers in the market. This thesis
aims to improve the anti-jamming performance of
generic GNSS receivers without altering antenna or
baseband structure. To achieve the purpose, the
following key issues have been addressed.
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4. Research on Algorithms of High Precision Acoustic Positioning for Seafloor Geodetic Networks |
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Shuang Zhao
As an important national marine spatial
information infrastructure, seafloor geodetic network
(SGN) is the vital support for the construction of
marine positioning, navigation and timing service
system. High-precision positioning is a prerequisite
for the construction and maintenance of SGN. The
dynamic change of marine environment and the
multi-source and complex observation errors bring
severe challenges to the data processing of SGN,
which seriously restricts the positioning accuracy and
reliability. Aiming at realizing the high-precision
positioning of SGN, according to the research main
line from "single point station layout" to "multi-point
network", systematic theoretical research, algorithm
improvement and experimental analysis have been
carried out in view of the shortcomings existing in
the construction of mathematical model of seafloor
geodetic point (SGP) positioning and sound velocity
error processing, optimal design and network
adjustment of SGN.
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Back Cover |
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Journal of Global Positioning Systems
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