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Sensors and Materials, Vol. 35, No. 3 (2023) 901–911
MYU Tokyo
S & M 3217

Key Technologies of Spatiotemporal Information Service
Based on Satellite Navigation Augmentation System
Yanyan Zeng,1,2 Xiaojuan Xing,1,2* Fan Shao,3 Xiyue Zhang,1 and Kui Zhang1
1Beijing Institute of Surveying and Mapping, No. 60, Nanlishi Road, Xicheng District, Beijing 100045, China
2Beijing Key Laboratory of Urban Spatial Information Engineering,No. 15, Yangfangdian Road, Haidian District, Beijing 100038, China

3China Dayou Positioning Intelligence Co., Ltd, Intelligent and Connected Vehicle Industry Park,

Shengli Road, Economic Development Zone, Anqing, Anhui 246008, China(Received October 31, 2022; accepted February 16, 2023)

Keywords: spatiotemporal information, high precision positioning, satellite navigation augmentation
systemConsidering the problems of the spatiotemporal information service of the satellite navigation
augmentation system (SNAS) and greater needs from high-tech fields, we present network realtime kinematic (RTK) and precise point positioning real-time kinematic (PPP)–RTK positioning
methods. We studied spatiotemporal information services and regulatory technology, anddesigned a SNAS service management platform that realizes the functions of regional spatial
data dissemination based on SNAS and uses measurement monitoring, measurement locale
setting, and historical trajectory analysis. From the results of the sample test and analysis, weestablished that users can directly receive high-precision network RTK services based onregional data. PPP-RTK accuracy tests were carried out using a car, and the test results show thatthe accuracy of determining the horizontal position and elevation was at the level of centimetersin an open environment, indicating that this method could provide reliable positioning services.

1.

Introduction

A satellite navigation augmentation system (SNAS) is designed to use the BeiDou navigation
satellite system (BDS), global positioning system (GPS), global navigation satellite system(GNSS), and Galileo satellite navigation and positioning systems to correct satellite navigationand positioning errors by uniformly distributing continuous tracking facilities for navigation
satellites on the ground and providing real-time, all-weather, highly reliable, centimeter-levelhigh-precision location services for society.(1–3)
Spatiotemporal information and positioning and navigation services have become importantnew components of infrastructure. These services are used in meteorological forecast analysis,
water pipeline maintenance, survey engineering, safety monitoring, and other fields, and focus
on serving traditional surveying and mapping users such as survey engineers.(4,5) With the
Beidou-3 global satellite navigation system providing comprehensive services, SNAS is now*Corresponding author: e-mail: 784015682@qq.com

https://doi.org/10.18494/SAM4220

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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facing a new situation: the newly revised “Surveying and Mapping Law” has expanded thesystem for managing reference stations,(6) and the relevant departments of the state have also
strengthened the supervision of SNAS. With this as the background for the current developmentof the 5G information technology industry, SNAS can provide high-precision location services
for high-tech fields such as unmanned driving technology, the Internet of Things, and big data,
all of which can promote its transformation from the realm of “professional service” to “public
service”.
We discuss herein the methods and technical research on positioning based on SNAS. We
investigated and analyzed the problems in the user supervision service of SNAS for a specificcity. We also studied user supervision technologies, such as real-time service based on a local
coordinate system, developed a SNAS service management platform, and verified that theaccuracy of a field positioning test meets current requirements.

2.

Methods and Research on Positioning Based on SNAS

SNAS includes several reference stations using the GNSS, system monitoring stations, theplatform, and data transmission links. A diagram of the high-precision positioning structure
based on SNAS is shown in Fig. 1.
The computation can be divided into four steps: first, the reference stations using GNSS
receive satellite navigation signals to generate original observation data and then send these data
to the service platform through the data transmission links. Second, system monitoring stations
receive the satellite navigation signals and augmentation service data of the platform to calculate
positioning data and send the original observation, augmentation service, and positioning data to

Fig. 1.

Diagram of the high-precision positioning structure based on SNAS.

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the platform through the data transmission links. Third, the data transmission links support datatransmission between the reference stations, the system monitoring stations, users, and theplatform. Finally, the platform processes data and generates augmentation service data toprovide high-precision positioning services for users.
SNAS can provide network real-time kinematic (RTK) and precise point positioning realtime kinematic (PPP-RTK) positioning methods or a single position on demand. The SNAS thatprovides the PPP-RTK positioning method uses the global/regional GNSS reference station
network to provide corrections for satellite orbits and satellite clock bias, and also provides dataservices. In addition, the GNSS observation data acquired by the system monitoring stations are
not involved in the augmentation service data solution of SNAS.
2.1

Methods of positioning based on SNAS

Currently, there are two main popular technical methods of positioning: the network RTK
and PPP-RTK methods.
2.1.1 Network RTK positioning method
The network RTK positioning method is shown in Fig. 2.(7,8) The computation can be divided
into three steps.(9,10) First, after receiving data, the baseline in the GNSS reference station
network is computed and the double-difference integer ambiguity is resolved. Second, theionospheric delay, tropospheric delay, and synthesis error for each baseline are calculated using
the GNSS observations, the double-difference integer ambiguity, and the known accuracy ofFig. 2.

Network RTK positioning method.

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each reference station for each baseline. Finally, according to the approximate coordinates sentby the user, the differential correction information of the network RTK user is determined byinterpolation to provide real-time cm-level positioning services for the network RTK user. The
process is expressed by λ ⋅ ∆∇ϕijpq = ∆∇ρijpq − λ ⋅ ∆∇N ijpq + ∆∇Tropijpq + ε ijpq
∆∇Pijpq = ∆∇ρijpq + ∆∇Tropijpq + γ ijpq ,(1)where ∆∇ is the double-difference operator, ρ is the distance from the station to the satellite, N is
the integer ambiguity, Trop is the tropospheric delay error, ε is the carrier phase observationnoise, γ is the pseudo-range observation noise, the superscripts p and q are the observation andreference satellites, respectively, and the subscripts i and j are the reference stations.
2.1.2 PPP-RTK positioning method
The PPP-RTK positioning method is the real-time processing of the reference stations using
GNSS, realizes the separation and modeling of full-state satellite navigation and positioning
augmentation service data, such as the precise satellite orbit, clock bias, phase/code deviation,and ionospheric/tropospheric atmospheric delay, broadcasts the augmentation service data topositioning users in a one-way broadcast communication mode, and supports real-time highprecision positioning technology with multiprecision and multilevel absolute positioning of alarge range of users of massive data.(11,12)
Using the real-time pseudo-range and phase observation data transmitted by the reference
stations using GNSS, the original observation equation for multiple frequencies is constructed,
and the correction data (for example, satellite orbit, clock bias, tropospheric delay for thereference stations) are obtained by solving Eq. (2).(13)Prs, f = ρ rs + mrs ⋅ Tr + µ f I rs,1 + c ⋅  dtr − dt s + br , f − brs, f 

 s
s
s
s
s
s
s
 Lr , f = ρ r + mr ⋅ Tr − µ f I r ,1 + c ⋅  dtr − dt  + φr , f − φr , f + λ f N r , f ,(2)where the superscript s represents the satellite, the subscript r represents the reference stations
using GNSS, the subscript f represents the satellite frequency, P is the pseudo-range observation,
L is the phase observation, ρ is the distance from the station to the satellite, m is the troposphericprojection function, T is the tropospheric zenith delay, u is the ratio of the wavelength of
frequency f to the wavelength of the first frequency, c is the propagation speed of light invacuum, dtr is the receiver clock bias, dts is the satellite clock bias, br,f is the pseudo-rangeshardware delay of the receiver, br,f
is the pseudo-range hardware delay of the satellite, ϕr,f is thesphase offset of the receiver, ϕr,f is the phase offset of the satellite, λf is the wavelength ofs is the integer ambiguity between the satellite and the receiver.
frequency f, and Nr,fEC

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The ionospheric and ionospheric delays of the reference stations using GNSS for the region
are modeled by Eqs. (3) and (4).
STECIPP =STEC0' +∆φIPP +

dSTEC
dSTEC
1 d 2 STEC
1 d 2 STEC
1 d2
2
∆φIPP +
∆λIPP +
∆
φ
+
∆
φ
∆∆
λ
+
IPP
IPP
IPP
dφ
dλ
2 d 2φ
2 dφ d λ2

dSTEC
1 d 2 STEC
1 d 2 STEC
1 d 2 STEC
2
2
∆λIPP +
∆
φ
+
∆
φ
∆∆
λ
+
∆λIPP
,
IPP
IPP
IPP
2
2
dλ
2 d φ
2 dφ d λ
2 d λ

(3)

where STECIPP is the total ionospheric oblique electron content at the satellite puncture point,∆φIPP is the latitude difference between a point and the central puncture point, ∆λIPP is thelongitude difference between a point and the central puncture point, STEC0' is a constant term for
dSTEC
dSTEC
ionospheric delay polynomial fitting,andare the first derivatives of ionosphericdλdφ
d 2 STEC
d 2 STEC d 2 STEC
delay in latitude and longitude, respectively, and,,andare the
and
d 2λ
d 2φ
dφ d λsecond derivatives of ionospheric delay in latitude and longitude.

ZWD= ZWD0 +dZWD
dZWD
d 2 ZWDdZWDx+
y+x⋅ y +
h,
dx
dy
dxdy
dh

(4)

where ZWD is the tropospheric wet delay for the zenith, ZED0 is a constant term for troposphericdelay polynomial fitting, x, y, and h refer to plane coordinates and elevation for the reference
dZWD
dZWD dZWD,, and
are the first derivatives of tropospheric delay
stations using GNSS,dydydx
d 2 STEC d 2 STEC
d 2 STEC
at x, y, and h, respectively, and,,andare the second derivatives ofd 2φ
dφ d λd 2λtropospheric delay at the plane.
Finally, the platform sends the regional delay model parameters and correction data to users
in the coverage area through the communication base station to complete the PPP-RTK
positioning service.
2.2

Technical research on positioning services based on SNAS

By collecting network data, reading the literature, interviewing relevant personnel such asindustry authorities, associations, enterprises and institutions, and scientific research institutes,
we visited the company to investigate the emerging fields and the public’s demand for GNSS.
We found that the reference stations had problems, such as the urgent need for positioning
services based on SNAS using the Beidou-3 data, spatial data service capacity needs to be
improved, and a better supervision of users. In response to these problems, research on
spatiotemporal information services and regulatory technology is being carried out and the
SNAS service supervision platform is being developed to improve the capabilities of the system
to provide service.

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2.2.1 Establishing spatial data and dissemination service technology
According to laws and regulations, relevant national requirements, and the actual
development needs of a city, projection schemes have been designed. Using the collected data,
the projection deformation numbers were calculated according to the length deformation
equations. According to the principle that length deformation should not exceed the limit overthe largest proportion of the area, analysis and optimization are carried out across different
dimensions to establish spatial data for urban development.
The equations for length deformation are

S = D(1 −Hmy2+ m2 ),
Rn 2 Rm(5)∆S = D(−Hmy2+ m2 ),
Rn 2 Rm(6)

where D is the horizontal distance of side length, ym is the approximate average of the two
endpoints of the abscissa, Rn is the curvature radius of the ellipsoidal surface arc truncation, Rm
is the average curvature radius of the ellipsoidal surface at the midpoint of the side length, and
Hm is the average elevation of the side length higher than the reference ellipsoid. These
parameters are all expressed in units of meters.
The dissemination service technology based on SNAS supports the addition, query,management, modification, and deletion of solution services, and user job source nodes realize
the control of user job services and sources. This new benchmark will be applied to highprecision location services.
2.2.2 Measurement monitoring
The technology described herein enables the monitoring of a user’s operation location, unit,
status, online duration, number of satellites used, login time, and other information. According
to the situation, RTK users can be disconnected to strengthen supervision. At the same time, thetotal fixed rate of users is calculated to evaluate the quality of service of the system.
2.2.3 Measurement locale setting
To achieve the purpose of user control of the area of operation, the technical research on file
uploading, adding, querying, managing, modifying, and deleting for the operation area is
performed. In addition, an electronic fence setting has been realized.

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2.2.4 Historical trajectory analysis
The SNAS service supervision platform can query the historical trajectory of a specific RTK
account at different times, download and display the trajectory map, which is convenient for the
management department to query and manage the records of the user terminal, and analyze big
data on the basis of the location to assess the development trends of urban construction.

3.

Experimental Results and Analysis

3.1

Network RTK positioning

Twelve GNSS control points were selected evenly across the entire city. The network RTK
test was carried out using representative types of GNSS receivers commonly used at home andabroad, and the measured coordinate system and GNSS control point results were analyzed. At
the same time, we analyzed the convergence and communication times of the network RTK to
test the functionality, applicability, and usability of the satellite navigation and positioning
reference service supervision platform and provide a basis for its official use.
Using the differential information released by the satellite navigation and positioning
reference service system, real-time dynamic positioning measurements were carried out at thesame test point. At each point, four sets of positioning were carried out. The average value ofmultiple positioning of each group was used to examine the accuracy of the inner and outercompliance of the test points.
The accuracy of both the inner compliance of the detection points and the external compliance
between the positioning results and the known results at each detection point was measured on
the basis of neutral errors. The methods of calculating the values are shown in the “technicalstandard for urban surveying using satellite positioning system”.(14)
Observation, data processing, and accuracy evaluation must be carried out for the points.
From the results of the analysis of an example, the internal compliance medium error was 12mm, and the external compliance medium error was 24 mm, both of which are less than thetechnical requirements.
In addition to the evaluation of the accuracy of GNSS control points, the results of the user
supervision service system platform were compared with those of the Trimble Pivot system. As
shown in Table 1, the accuracy reached the centimeter level.
3.2

PPP-RTK positioning

On the basis of three reference stations using GNSS (15) and the positioning reference service
supervision platform, the positioning effect of the PPP-RTK positioning terminal mounted on avehicle in the urban environment was evaluated. The distribution of the reference stations using
GNSS is shown in Fig. 3. The car equipped with a PPP-RTK terminal and test environments isshown in Fig. 4.

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Table 1Results of accuracy evaluation for network RTK positioning.
Name of points
N/mm
E/mm
U/mm
t01
6
6
15
t02
1
4
11
t03
2
1
5
t04
2
2
2
t05
6
5
7
t06
5
2
6
t08
1
3
5
t09
4
8
5
t10
3
1
8
t11
6
5
3
t12
5
8
2
t13
7
4
2
Fig. 3. (Color online) Distribution of the reference
stations using GNSS.

(a)

(c)

(b)
Fig. 4.

(Color online) (a) Car equipped with test terminal, (b) its trace over, and (c) a test environment.

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PPP-RTK static and dynamic accuracy tests were carried out. The results are shown in Fig. 5.
In Fig. 5(a), the red line is the standard deviation in elevation, the blue line is the standard
deviation in horizontal position, and the green line is the standard deviation in three-dimensional
position. After about 50 s after powering on the terminal, the horizontal accuracy converges toless than 5 cm, the horizontal accuracy is stable within 2 cm after 400 s, and the elevation
accuracy is stable within 5 cm. A few small fluctuations in the middle may be caused byfluctuations in the environment, which is normal. The blue line in Fig. 5(b) refers to the PPPRTK dynamic accuracy; the positioning accuracy at the centimeter level can be guaranteed innormal open areas, but it is significantly reduced in the case of multiple paths and obstructions.

(a)

(b)
Fig. 5.

(Color online) Results of tests of PPP-RTK (s) static and (b) dynamic accuracies.

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Sensors and Materials, Vol. 35, No. 3 (2023)

Conclusions

According to the requirements of the new surveying and mapping method for SNAS
combined with the new demand for high-precision positioning, the user supervision problems
existing in SNAS were analyzed, key technologies such as the real-time acquisition of local
coordinate systems and historical query tracks were studied, and a user supervision platform
was developed. After the Beijing case test, we conclude that the accuracy of the field positioning
test meets the requirements and the needs of the user supervision service. The system describedherein can provide users with network RTK and PPP-RTK services. It is necessary to test thestability of the two services by considering more scenarios, which will be the focus of subsequent
research.

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About the Authors
Yanyan Zeng received her B.S. degree from China University of Petroleum,
Shandong, China, in 2010 and her Ph.D. degree from the University of theChinese Academy of Sciences, Beijing in 2015. Since 2015, she has been asenior engineer at the Beijing Institute of Surveying and Mapping. Her
research interests are GNSS data processing, new fundamental surveying, andmapping. (zengyanyan1989@163.com)
Xiaojuan Xing received her B.S. and M.S. degrees from Beijing Forestry
University, Beijing, China, in 2012 and 2015, respectively. Since 2015, she has
been an engineer at the Beijing Institute of Surveying and Mapping. Her
research interests are urban planning, geographic information, andcartography. (784015682@qq.com)

Fan Shao received his Ph.D. degree from Shanghai Astronomical Observatory,
Chinese Academy of Sciences, China, in 2019. Since 2019, he has been asenior GNSS engineer at China Dayou Positioning Intelligence Co., Ltd. His
research interests are GNSS, N-RTK/PPP-RTK, and data processing.
(shaofan@dpi.net.cn)

Xiyue Zhang was born in Shandong, China, in 1991, and obtained a master's
degree from the Chinese Academy of Surveying and Mapping Sciences inJune 2017. His research interests are high-precision satellite navigation andpositioning. He is currently an engineer in the Beijing Surveying and Mapping
Design and Research Institute and is engaged in the research on CORS
construction, operation and maintenance, service, and high-precision
positioning. (zxycasm@163.com)
Kui Zhang received his M.S. degree from the School of Information
Engineering, China University of Geosciences in Beijing, China, in 2021. Now
he is an assistant engineer at the Beijing Institute of Surveying and Mapping,Beijing, China. He focuses on comprehending very high spatial resolution
images. His specific research interests include the theory of object-based
image analysis (OBIA) and image information extraction by deep learning
methods.