GNSSGET and GNSSPLOT Platforms

Matlab GUIs for Retrieving GNSS Products and Visualizing GNSS Solutions

Maria Kaselimi, Nikolaos Doulamis, Demitris Delikaraoglou and Eftychios Protopapadakis

National Technical University of Athens, Department of Rural and Surveying Engineering,

Heroon Polytechnioy 9, Zografou 15780, Athens, Greece

Keywords: Graphical User Interface (GUI), Global Navigation Satellite System (GNSS), Information Communication

Technology (ICT), Multi-GNSS Experiment (MGEX), Visualization, GNSS Data Processing.

Abstract: The emergence of new satellite systems such as Galileo and BeiDou, as well as the modernization of GPS

and GLONASS, indicate dramatic changes in the field of satellite positioning. IGS initiated the MGEX

(Multi-GNSS Experiment) campaign to overcome the challenges that scientific community has to face due

to the arrival of new satellite systems. The variety of products improves and enhances the solution provided

by multi-GNSS techniques. However, these heterogeneous products are provided by various Data Centers

(DC) and Analysis Centers (AC) in many different locations. Users are therefore required to acquire their

data across multiple FTP Servers, a rather cumbersome and time consuming task. The aim of the work

described in this paper is to create a platform for the acquisition of GNSS data from heterogeneous sources,

for performing high accuracy position solutions and building data visualizations concerning positioning

error and solution accuracy. All of these tasks are essential for the purpose of making complex GNSS data

more accessible, understandable and useful for precise point positioning, atmospheric research and other

applications.

1 INTRODUCTION

Current satellite-based navigation systems include

the following technologies: GPS (American),

GLONASS (Russian), GALILEO (European),

BeiDou (Chinese), QZSS (Japanese) and IRNSS

(Indian). The number of satellites of the newly

developed GNSS systems continues to increase, thus

steadily improving accuracy and reliability in

positioning (Li et al., 2015). New signals are more

resistant to environmental conditions and multipath

errors, while also enabling easier detection of low

level signals (Hofmann-Wellenhof et al., 2008). In

addition, the availability of non-encrypted signals at

multiple frequencies gives a new dimension to

resolving the ambiguity resolution and may also

contribute to the analysis of ionosphere delays

(Montenbruck et al., 2014). However, from the

combination of multi-GNSS observations various

other practical problems have emerged concerning

the compatibility and interoperability between

GNSS systems (Håkansson et al., 2016). To

overcome these difficulties, various Analysis

Centers of the International GNSS Service (IGS)

provide new products so as to improve and enhance

the solution provided by multi-GNSS systems:

tracking station data, coordinates and velocities,

satellite ephemeris, earth rotation parameters, zenith

tropospheric path delay estimates and global

ionospheric maps. Among several institutes and

agencies providing such products are: CODE

(Center for Orbit Determination in Europe), GFZ

(Geo Forschung Zentrum), CNES (Centre National

d'Etudes Spatiales), TUM (Technische Universität

München), WU (Wuhan University) and JAXA

(Japan Aerospace Exploration Agency) (Guo et al.,

2017). Over the past five years, the Multi-GNSS

Experiment (MGEX) has been set-up by the IGS to

track and analyze all available GNSS signals and to

extend its services to GNSS user community by

contributing multi-GNSS observations and data

products from newly established or modernized

monitoring IGS stations (Montenbruck et al., 2017).

In this paper, we describe new software suitable for

gathering distributed data needed for GNSS

processing and visualization of the GNSS data

solutions. This software works in full synergy with

Kaselimi M., Doulamis N., Delikaraoglou D. and Protopapadakis E.

GNSSGET and GNSSPLOT Platforms - Matlab GUIs for Retrieving GNSS Products and Visualizing GNSS Solutions.

DOI: 10.5220/0006756206260633

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2018), pages 626-633

ISBN: 978-989-758-290-5

the open source RTKLIB during the stage of data

processing.

The remainder of the paper is structured as

follows: The second section of the paper presents a

brief introduction in GNSS positioning theory. The

third section outlines the features of relevant GNSS

processing software solutions and tools provided by

GNSS receiver’s manufacturers, universities,

institutes and online services, pointing to their

shortcomings with regard to the user effort required

to retrieve various IGS products needed for

processing and to visualize the processing results in

a readily manner. In the fourth section, in lieu of a

comparison, we present the newly created platforms,

highlighting the added functionalities they provide

to the end user. Finally, the fifth section concludes

the paper with a summary of findings.

2 BASIC CONCEPTS IN GNSS

DATA PROCESSING

A variety of applications such as environmental

monitoring, earth-resource mapping and navigation

require high resolution data. These data are derived

from sensors, radars and satellite systems (Massinas

et al., 2017, Protopapadakis et al., 2017).

GNSS positioning is based on the concept of

trilateration, which is the method of determining the

position of an unknown point by measuring

distances to it from points at known coordinates. At

a minimum, GNSS absolute positioning, requires

four simultaneous “pseudoranges” to four satellites,

in order to compute a receiver’s coordinates (x

, 𝑦

𝑟

𝑧

𝑟

) and time (t) (Figure 1).

Figure 1: Geometric interpretation of absolute positioning

(after http://gisgeography.com/trilateration-triangulation-

gps/).

The transmitted signal from each satellite is

encoded with the navigation message in the so-

called broadcast ephemeris, which can be read by

the user’s GNSS receiver (Blewitt, 1997). The

navigation message includes orbital parameters and

other necessary information, from which the receiver

can compute satellite coordinates (𝑋

𝑆

, 𝑌

𝑆

, 𝑍

𝑆

Knowing the signal travel time between receiver and

satellite, the receiver coordinates could be computed

with high accuracy depending on the availability of

the various code and phase measurement

observables.

The observation equations for code and phase

measurements are (Teunissen et al., 2017):

eIT)dtdt(cp 



(1)

MIT

)dtdt(c









(2)

where, dt

and dt

are receiver and satellite clock

offset from GNSS system time, T

is troposphere

delay, I

is ionospheric delay, e

and ε

represent

unmodeled errors, λ is the carrier wavelength, 𝑀

𝑟

𝑆

𝑁

𝑟

𝑆

+ 𝛿

𝑟

− 𝛿

𝑆

is the carrier phase-ambiguity with an

integer part N

and the instrumental receiver and

satellite phase delays 𝛿

𝑟

− 𝛿

𝑆

(cycles).

Figure 2: GNSS measurement errors.

The code pseudoranges and phase-based ranges

are affected by various systematic errors or biases

and random noise effects. The error sources can be

classified into three groups, namely satellite-related

errors, propagation-medium-related errors, and

receiver-related errors (Figure 2). Some of the

systematic errors can be modelled and give rise to

additional terms in the observation equations or can

be eliminated by appropriate combinations of the

observables (Hofmann-Wellenhof et al., 2008).

Through a least squares adjustment process of

the available observations, during which all

corrections are made, the adjusted coordinates of the

user receiver’s position can be obtained as a time

series depicting the convergence of the solution for

each (X, Y, X or North, East, Up) coordinate (Figure

3), and similarly for other estimated parameters (e.g.

tropospheric delays).

Figure 3: GNSS positioning error.

Figure 4: GNSS data processing workflow.

Figure 4 presents schematically the field data

collection and the subsequent computational

procedure involved in order to come up in an

accurate positioning solution, from a given set of

GNSS observations and associated auxiliary IGS

products. First, GNSS receivers collect observations,

remaining in the field for a specific duration (from a

few minutes to several hours or days). Then, the

available products are gathered via FTP Servers.

Having decided on the analysis mode to be applied

to the data, suitable processing is carried out in a

software package with inputs all the data and

available products. The output, in most cases, is a

text file with the positioning solution results

described by time series of various parameters, such

as the estimated states of a Kalman filter, and

residuals of the measurements. For interpreting and

validating the processing results, visualization of the

available position-related time series are used

pervasively in order to enable a rapid visual

assessment and to provide informative ways of

understanding the data itself, as well as the quality

of the positioning solutions obtained.

3 RELATED WORK

Currently, there are many software packages

available for GNSS data processing and

visualization. Most manufactures of GNSS receivers

produce their own software for processing the

observed field data in order to obtain adjusted results

(Mohammed and Eldin, 2011). A typical

shortcoming of available commercial software

solutions is that they do not target many of the

current GNSS data processing and analysis issues

involved. On the other end, universities and research

institutes have developed many high precision

GNSS data processing packages, such as BERNESE

(Dach et al., 2015), developed at the Astronomical

Institute of the University of Bern (AIUB),

GAMIT/GLOBK (MIT) and GIPSY-OASIS (JPL).

Although these packages provide high

performance and high accuracy processing

techniques, currently they only support processing of

GPS and GLONASS observations. Furthermore,

they are not addressed to all professionals and the

average user, as they are not very easy to use. There

are also online tools for post-processing GNSS data,

including the CSRS-PPP (Canadian Spatial

Reference System, Natural Resources Canada), the

Australian GPS processing service AUSPOS, the

GNSS analysis and positioning software GAPS

provided by the University of New Brunswick,

online service APPS (Jet Propulsion Laboratory),

and the commercial magicGNSS by GMV and

CenterPoint RTX post-processing service provided

by Trimble. The processing method mostly used by

many of these online services is based on the Precise

Point Positioning (PPP) technique and the output is

usually a positioning solution file solution and report

is generated in ASCII format. Characteristically,

most of the above services do not provide a user-

oriented graphical interface platform for exploitation

of advanced GNSS algorithms and available IGS

products. Furthermore, they don’t provide the

capability for interactive visualization of GNSS

position tracks for each station, for the purpose of

enabling the identification of temporal relationships,

jumps or deviations from expected trends etc.

The RTKLIB (Takasu, 2013) suite is open

source software for standard and high-precision

positioning using observations from all currently

available GNSS satellite systems (GPS, GLONASS,

Galileo, BeiDou, QZSS). It was developed by T.

Takasu at Tokyo University of Marine Sciences and

Technology and has been available for free since

2006. The fact that RTKLIB code is accessible to

users, allows code modification and improvement,

meeting user’s needs. Since 2006, major

improvements have been made to the software

(latest version v.2.4.2), both by its developers and

RTKLIB users. RTKLIB consists of a portable

program library and several APs (application

programs) utilizing the library in order to facilitate

satellite data processing and determine the exact

station’s location. In addition, RTKLIB supports

several file formats, such as RINEX, RTCM,

NTRIP, ANTEX, and IONEX files. Also provides

limited capability to process and convert data, to

visualize the visibility (azimuths and elevations) and

the number of the used satellites against time, and to

export position results. This paper targets to deal

with RTKLIB's disadvantages by improving

RTKLIB's platforms for data acquisition and

visualization. One disadvantage is that RTKGET has

a complex procedure for data acquisition, causing

confusion in most of the users. RTKPLOT interface

doesn’t include statistics and metrics of computed

solutions. Furthermore, RTKLIB’s interface doesn’t

plot code and phase residuals.

4 THE GNSSGET AND

GNSSPLOT PLATFORMS

The newly developed GNSSGET and GNSSPLOT

platforms have been created with the aim to allow

the user, in a more transparent way, to communicate

with the RTKLIB software while retrieving data

from FTP Servers and performing user-selected

visualization tasks at the end of the data processing

stage. The technology and programming techniques

applied for the development of these platforms was

based mainly on MATLAB which provides the

ability to develop GUI applications through the

GUIDE (Graphical User Interface Development

Environment). GUIDE is a graphical environment

where a developer can build a GUI application, by

selecting various objects -included in the final

application- and by selectively adjusting various

application parameters related to the GUI. In that

way, certain properties of the user interface and

graphics components corresponding to a specific

user action can be associated with specific call-back

functions. This provides point-and-click control of

software applications, eliminating the need to learn a

language or type commands in order to run the

application. These platforms aren’t open source and

freely available to the users as they have been

developed with MATLAB 2008 version.

GNSSGET is essentially an Information

Communication Technology (ICT) platform which

acts like an umbrella, collecting information from

distributed data sources. Products vary, based on

parameters such as: time, space, file format,

Analysis Centers and product type. Various IGS-

coordinated Institutes and Data Centers provide

GNSS data via communication protocol FTP. The

objective of the application is to retrieve data from

isolated data sources and store them in the local

disk. This is accomplished by selecting few desired

parameters. The application functions are: storage of

GNSS observations and preview, retrieval of

broadcast and post-fit precise orbital data and

associated products and their storage on the local

hard disk. GNSS data and products, retrieved from

FTP Servers, are spatiotemporal big data, that is can

be in high volumes, and in compressed or

uncompressed form. Following the data collection,

GNSS data processing is carried out, by

communicating automatically the data to RTKLIB

and applying appropriate models and methods for

positioning and eventually generating position

solution results.

The next stage is the visualization of the results

which prompted us to build GNSSPLOT GUI.

Visualization includes time series diagrams,

statistics and metrics, plotting position history of

each GNSS station or georeferenced ground tracks

using Google Maps as background, Kalman filter

residuals and parameters as a function of time.

These two interfaces aim to direct the user,

minimizing errors from handling the available data.

In order to achieve this goal, these platforms provide

dynamic lists and warning messages to prevent the

user from frequent or common mistakes. The most

important functions are:

 Gathering isolated GNSS data and products

via numerous FTP servers

 Capability of local data storage and

decompression

 Positioning error time series visualization

 Code and phase residual visualization

 GNSS systems and satellites residuals

isolation

 Kalman filter parameters visualization

These platforms cooperate fully with the

RTKLIB software in order to provide the best

possible result.

4.1 GNSSGET Platform

GNSSGET platform has the ability to gather the

distributed information needed through separated

servers. A major advantage is that GNSSGET

platform is already interconnected with these FTPs,

so user doesn’t need to establish data

communication links.

Figure 5: GNSSGET platform overview.

The user has the ability to select a specific data

time duration, which is necessary in order to pick the

suitable data from the FTP servers. Also, the user

should fill in the tab showing the local path where

downloaded data will be stored (Figure 5).

In the next step, the user is required to select the

desirable parameters concerning the observation

data, through multiple sets of paired drop down lists.

The option between selecting global or regional

networks triggers different categories of available

networks appearing in the pop-up menu that follows.

Subsequently, the Data Centers pop-up menu

changes dynamically, depending on the selected

network. Each selected Data Center deals with data

from stations in different countries. Having selected

the preferred location (country), the associated

stations list is displayed. Hence, the user selects via

a menu with variable networks, the data bases

containing specific types of data and the desirable

location of each station. (Figure 6).

Selected observation and navigation files could

be previewed, pressing the corresponding button.

Also, the platform has the ability to inform the user

in case of a missing file.

Figure 6: Options for observation files selection.

Figure 7: Selection of download data type products, in

GNSSGET.

From the tab named “Products”, the user can

choose through a variety of products such as: clock

products, precise orbit ephemeris and earth rotation

parameters, as they provided by various data bases

and institutes that can be seen through the click-and-

display menus (Figure 7). Among each product

(CLK, SP3, ERP), a set of two paired select boxes

exists. User’s selection in the first select box affects

the options in the other select box. By pressing the

button “Get Products”, the desirable products are

downloaded and stored in the local disk. When the

process is finished, a suitable message appears on

the white bar.

Figure 8: Introductory menu, in GNSSPLOT.

4.2 GNSSPLOT Platform

GNSSPLOT is a platform for GNSS solutions

visualization, including time series diagrams,

statistics and metrics.

Figure 9: Tab “Position”, in GNSSPLOT.

Initially, an introductory menu is displayed as

“welcome window”, to allow the user to enter into

the program. Platform has the capability to search

the file folder as extracted from RTKLIB. The

platform has been created to automatically recognize

the number and useful types of files (.pos, .stat) that

are contained in this folder. In the event that the

folder does not contain files with .pos format, as

extracted from RTKLIB, then a corresponding

warning message appears on the screen (Figure 8).

On the other hand, GNSSPLOT reads

positioning files (.pos) and status files (.stat) and

stores their names in order to appear in the main

interface, in a pop-up menu form. Then, a window

with four tabs -displaying the main menu- opens. As

already mentioned, the select box up-left contains

the file names that exist in the specified folder. Thus,

the activation of the first tab named “Position”

(Figure 9) requires the existence of a positioning file

in the specified folder.

Figure 10: Code residuals visualization.

Among GNSSPLOT’s advantages, the

visualization of positioning errors is included.

Furthermore, statistics such as mean and standard

deviation are computed. This platform also allows

users to visualize the georeferenced ground track (by

pressing the button “Ground Track”), in Google

Maps background. GNSSPLOT provides the ability

to easily display ground track at any scale, using the

zoom in property, thus it is possible to visualize the

ground track. In addition, the number of visible

satellites over time can be plotted (by pressing the

button “View of satellites”).

Reading a status file, if it exists, code and phase

residuals can be plotted, in the second (Figure 10)

and the third tab.

Selecting a single GNSS system, from the second

pop-up menu, the residuals from all observations to

this satellite system are plotted (Figure 11).

Figure 11: Code residuals for Galileo satellites, G31, on

1/1/2016.

Also, there is the possibility of selecting a single

satellite from the third pop-up menu (Figure 10).

This way the errors from this specific satellite can be

displayed (Figure 12).

Figure 12: Code residuals for GPS satellite, G31, on

1/1/2016.

Figure 13: Kalman filter parameters visualization.

Finally, zenith total delay (ZTD) and receiver

clock bias are displayed in the fourth tab (Figure

13). In the first step, the user selects the type of data

between ZTD and receiver clock bias in order to be

plotted. In the next step, the user selects the status

file name. Selecting any of the above-mentioned

lists the plot is triggered.

All plots can be locally stored by pressing right-

click on the image. Both newly developed platforms

can be extracted in an application executable from

MATLAB environment, thus providing a standalone

application running in Windows. Necessary libraries

needed for installation, are extracted from MATLAB

and are installed together with a standard application

installation.

5 CONCLUSIONS

The usage of multi-GNSS positioning systems offers

several advantages for the user, mainly due to the

increased number of available satellites and the

enhanced user-to-satellites relative geometry (Cai et

al., 2015, Li et al., 2015). As a result, the quality of

positioning solutions is being improved, while the

time required for field observations is being reduced.

System interoperability is very important towards

ensuring, higher accuracies in the future.

Many services and Analysis Centers provide

products and observations from permanent stations

which can be extremely useful in that respect.

However, their proper usage is often confusing to

the new users. To ease the tasks involved,

GNSSGET is a platform, which can be used to

download data and products from existing online

databases through the FTP communication protocol.

Proposed platform provides the ability of data

extraction among different Analysis Centers. This is

achieved by selecting a desirable set of features

provided by dynamically changing lists that

communicate with each other and at the same time

complement one another. Data are stored in a

location per service, type of processing, and time,

with the initial path for the storage of data indicated

by the user. GNSSPLOT, focuses on visualizing

solutions, derived from RTKLIB.

Unlike the extension of RTKLIB -RTKPLOT-

which permits the visualization of a single set of

solutions, the GNSSPLOT interface has the ability

to visualize multiple sets of solutions by simply

changing the options of a dynamic list. Furthermore,

GNSSPLOT is a powerful tool to visualize GNSS

position time series and other corrections and

residuals.

The authors plan to create a web service in order

to fully provide the platform’s abilities to the users.

This future service will include all the above

mentioned functions that are provided through

platforms GNSSGET and GNSSPLOT.

Furthermore, the web service is more convenient as

the installation of a standard application in the PC

wouldn’t be prerequisite. The exploitation of similar

platforms should be considered in other applications

as well (e.g. intangible cultural heritage digitization).

ACKNOWLEDGEMENTS

This work was supported by the EU H2020

TERPSICHORE project “Transforming Intangible

Folkloric Performing Arts into Tangible

Choreographic Digital Objects” under the grant

agreement 691218.

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