Proposal of a Troposphere Model in Simulation for Automotive

Applications

Angelo Arieta

, Mauro Tropea

and Danilo Amendola

Dimes Department, University of Calabria, via P. Bucci 39/c, 87036 Rende (CS), Italy

University of Trieste, Piazzale Europa, 1, 34127, Trieste, FVG, Italy

Keywords:

Automotive, Troposphere Model, GNSS Simulator, GPS Receiver.

Abstract:

The Global Navigation Satellite System (GNSS) is the standard generic term for satellite navigation systems

that provide global coverage and it includes GPS, GLONASS, Galileo and other regional satellite navigational

systems. The use of simulator for performing different kinds of test is a practice largely used that provides

many advantages in different navigation systems thanks to the possibility of performing tests under controlled

and repeatable conditions in a secure laboratory. The use of GPS simulation is largely used for testing GPS

receivers. This paper presents a GNSS simulator for automotive applications, in particular the software used is

GPS-SDR-SIM, an open source simulator written in C language and it proposes a simulator improvement pro-

viding the implementation of the Troposphere Collins model in order to improve the accuracy of the simulation

experiments.

1 INTRODUCTION

The Global Navigation Satellite System (GNSS) is

the standard generic term for satellite navigation sys-

tems that provide global coverage. This term includes

GPS, GLONASS, Galileo, Beidou and other regional

satellite navigational systems (Van Sickle and Dutton,

2014; Groves, 2015).

It is a geo-radiolocation for a terrestrial, marine or

air navigation system, which uses a network of artiﬁ-

cial satellites in orbit. Thanks to this global coverage,

the receivers that are located anywhere on the Earth’s

surface or on the atmosphere, can determine their ge-

ographic coordinates by processing the RF signals

transmitted by satellites.

The GNSS network is composed of several con-

stellations. A satellite constellation is a group of satel-

lites used in a coordinated way that they can offer

global or partial coverage. Thanks to the trilateration

operation, the receiver located on the earth’s surface

is able to obtain its position, as each satellite con-

tinuously sends information regarding the ephemeris

(Tsui, 2005).

Ephemeris are information concerning the posi-

tion of the satellites, the clock (timing) and health

(Zhang and Ji, 2015). This information is sent

through the navigation message that is transmitted by

satellites and in the case of the Global Positioning

System (GPS) constellation, it is transmitted in two

frequencies which are 1575.42MHz and 1227.6MHz.

Figure 1: GNSS in a VANET scenario.

Today the GPS is considered an integrated part

of the system that composes the Vehicular Ad-Hoc

Network (VANET) (Ghori et al., 2018). The vehi-

cles are equipped with GPS receivers and, in the next

future, all vehicles will have a GNSS receiver on

board (Hasan et al., 2018). The research in the joint

use of GNSS and VANET shows a certain conver-

gence toward a common objective: how to improve

the GNSS systems exploiting VANETs by GNSS re-

searchers and the use of GPS information to improve

Quality of Service (QoS) and scalability by VANET

ones. In Figure 1, a typical use case of global po-

120

Arieta, A., Tropea, M. and Amendola, D.

Proposal of a Troposphere Model in Simulation for Automotive Applications.

DOI: 10.5220/0009970901200127

In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020), pages 120-127

ISBN: 978-989-758-444-2

sition system in a VANET environment is shown.

The vehicular network aims to improve road safety,

trafﬁc and energy efﬁciency giving also attention to

the emission issues (Santamaria et al., 2019), (Fazio

et al., 2017). Different works focuses their attention

on mechanisms for advising dangerous or emergency

situations by exploiting on-board sensors (Santamaria

et al., 2018) also using predictive mechanism as in

(De Rango et al., 2008), (Fazio et al., 2016), (Frnda

et al., 2013). Moreover, very studied aspects for the

ad-hoc network are routing issue as it is possible to

view in (Zhou et al., 2006), (Fotino et al., 2007), (So-

cievole et al., 2011), and also for VANET is an impor-

tant topic as it is possible to view in (De Rango et al.,

2009), (Fazio et al., 2013).

Moreover, in the literature a lot of works exist that

base their approach on the use of geo-localization data

for routing purpose in order to improve the scalabil-

ity of routing within vehicular networks (Devangavi

and Gupta, 2017). Incoming automatic driving ap-

plications are going to require even tighter level of

precision and security in order to guarantee the high

required standard. The aviation sector, the ﬁrst one to

develop GNSS integrity solutions, analysed in a very

detailed way many important aspects concerning the

use of GNSS accuracy information.

In this paper, the proposal of a Troposphere model

for GNSS simulator in automotive ﬁeld has been pre-

sented. The simulator is GPS-SDR-SIM, an open

source simulator written in C language. The proposed

module implements the Troposphere Collins model in

order to improve the accuracy of the simulation exper-

iments.

The rest of this paper is organized as follows: Sec-

tion 2 presents the related work on the considered re-

search topic. In Section 3, a description of the GNSS

applications in VANET scenario is given. In Section

4, we describe the used simulator considered for de-

veloping additional software module.The numerical

results are presented in Section 5. Finally, Section

6 concludes the paper.

2 RELATED WORK

The scientiﬁc research of last decade has been char-

acterized by a rapid evolution of GNSS software re-

ceivers. Since the ﬁrst GPS Standard Positioning Ser-

vice, different new systems are arisen able to pro-

vide global coverage and, they include GLONASS,

Galileo and other regional satellite navigational sys-

tems. And, in order to perform different tests about

navigation system in a secure and repeatable way, dif-

ferent software and hardware simulators are proposed

in the scientiﬁc community. In the literature, several

works were devoted to architectural and implementa-

tion aspects of GNSS simulators.

In (Deng and Wang, 2011) the authors propose

a simulation design of digital IF signal based on the

mathematical model of digital IF GPS signals. Their

have designed a simulator and their experimental re-

sults show that the structure of simulated signals is

closer to real signals.

The TUTGNSS Reference Receiver is a fully op-

erational GPS I Galileo receiver, developed at Tam-

pere University of Technology for educational pur-

poses (Paakki et al., 2010). It provides a platform for

testing and developing new algorithms for GNSS ﬁeld

without ”black boxes”, allowing developers to have

full control over its further development.

The GNSS-SDR, an open source Global Naviga-

tion Satellite System software-deﬁned receiver is pro-

posed in (Fernandez-Prades et al., 2001). The paper

describes the software architecture design and pro-

vides details about its implementation, targeting a

multiband, multisystem GNSS receiver. The authors

have built a testbed for GNSS signal processing able

to allow any kind of customization, including inter-

changeability of signal sources, signal processing al-

gorithms, interoperability with other systems, output

formats, and the offering of interfaces to all the inter-

mediate signals, parameters and variables.

An open source implementation of a GNSS soft-

ware receiver that targets Galileo E1B and E1C sig-

nals is discussed in (Fern

andez-Prades et al., 2012),

where the authors provide detailed descriptions of the

main signal processing algorithms involved in acqui-

sition and tracking of such navigation signals.

An open source GPS receiver software and labora-

tory hardware that is a straightforward modiﬁcation of

a COTS receiver to interface it to a PC bus are shown

in (Kelley et al., 2002). In the paper, the authors de-

scribe the hardware and software architecture, the fea-

tures added to allow debugging of the code and carrier

tracking loops.

3 GNSS APPLICATION IN A

VANET SCENARIO

The number of applications that use the Global Nav-

igation Satellite System (GNSS) technology is con-

stantly increasing. Many of these have increasingly

stringent requirements so that in some cases it is nec-

essary to integrate GNSS technology with other tech-

nologies in order to meet the requirements of a partic-

ular application.

Proposal of a Troposphere Model in Simulation for Automotive Applications

121

To ensure that GNSS technology is forefront and

reliable, the design of the various parts of the system,

in particular GNSS receivers, must respect high stan-

dards and ensure reliable performance. To enable this,

it is important that the product development process

is based on appropriate tests from the concept to the

production.

Traditionally, GNSS testing has been subdivided

into following three distinct methods:

• Live sky testing

• Record and Replay methods

• Simulators

Nowadays, the best practices for testing GNSS re-

ceivers concern tests performed in a controlled and

repeatable manner in a safe laboratory (Paakki and

Nurmi, 2014).

This kind of approach allows to test all conditions,

including tests performed to the limits of real and

theoretical performance. This also allows the devel-

opment of receivers for GNSS systems currently un-

available or able to operate also with a non-complete

constellation.

The aim is to emulate the environment of a GNSS

receiver on a dynamic platform by modeling the

movement of the vehicle, the satellite, the character-

istics of the signal, the atmospheric effects and other,

making sure that the receiver actually navigates ac-

cording to the parameters of the test scenario. Un-

like road tests, simulator tests provide full control of

simulated satellite signals and simulated environmen-

tal conditions. In this way, testers can easily gener-

ate and run many different test scenarios for different

types of tests, with complete control over all simula-

tion parameters such as date, time position, vehicle

movement and environmental conditions.

The performance of a receiver varies on the basis

of errors and effects applied to the RF signal. Figure

2 shows a representation of the signal ﬂow through a

GNSS simulator where additional effects are added,

up to the output that represents the input signal of the

trial receiver. The analysis of the simulation results

can be done in real time or by post-test analysis of

the recorded data. The access to simulation data (the

data used to create the test signal) can be obtained in

various ways: using streaming data or recording it in

a ﬁle. This data can then be used to compare receiver

performance with ”true” simulation data.

3.1 GPS-SDR-SIM: An Open Source

Simulator to Test GPS Receivers

GPS-SDR-SIM (Hu, 2019) is an open source software

written in C language that allows to generate base-

Figure 2: Representation of the signal ﬂow through a GNSS

simulator.

band GPS data streams, which can be converted into

RF signals using Software Deﬁned Radio (SDR) plat-

forms such as for example ADALM-Pluto (ada, 2019)

and HackRF One (hac, 2019).

To generate a GPS signal, the GPS constellation to

be used must be speciﬁed to the software. This can be

done thanks to a ﬁle called GPS broadcast ephemeris

ﬁle, which in the case of this study was downloaded

from the site (eph, 2019) where it is possible to down-

load the daily ﬁle containing information about the

constellation at a given moment (in RINEX format

(Gurtner and Estey, 2013)).

These ﬁles are then used to generate the simu-

lated pseudorange and doppler frequency for the GPS

satellites in sight. These simulated data are then used

to generate digitized I/Q samples for the GPS signal

(Rao and Falco, 2012).

The instant of simulation starting can be speciﬁed

if the corresponding set of ephemerides is available,

otherwise the ﬁrst instant of ephemerides present in

the RINEX ﬁle is selected. In addition to the RINEX

ﬁle containing the satellite broadcast ephemeris, an

National Marine Electronics Association (NMEA)

ﬁle (a text ﬁle describing the coordinates of the re-

ceiver in the form of strings) for the vehicle coordi-

nates to simulate has to be provided. These coordi-

nates must have a frequency of 10Hz, which means

that 10 NMEA strings correspond to one second of

simulation. An NMEA GGA string speciﬁes the po-

sition of the receiver; some of the most relevant pa-

rameters are: latitude, longitude, GPS signal quality,

and so on.

4 PROPOSED SOLUTION

To improve and add novel functionality to the simu-

lator, a model that takes into account the delay of the

signal when it crosses the space portion of the tropo-

sphere has been implement. In order to realize this

purpose, a valid model was selected to calculate the

effects of the signal based on the parameters avail-

able in the simulator. In Figure 3, a scheme that sum-

marizes the simulator functionality is shown. This

scheme highlights the additional block implemented

SIMULTECH 2020 - 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

122

Figure 3: Simulator Block Diagram.

in the software.

In this work the Troposphere Collins model

(Collins, 1999) has been used. This model is also used

by Satellite-Based Augmentation System (SBAS)

systems for maximum precision differential correc-

tions, with the aim of increasing the accuracy and in-

tegrity of the GPS system data, such as the Wide Area

Augmentation System (WAAS) and European Geo-

stationary Navigation Overlay System (EGNOS), that

are air navigation aids developed to augment the GPS,

with the goal of improving its accuracy, integrity, and

availability.

Delays due to the troposphere can be divided into

two parts. One dues to the dry component of the at-

mosphere called T

z,dry

, the other one dues to the wet

component called T

z,wet

T (E) = (T

z,dry

+ T

z,wet

)M(E) (1)

T (E) is the total delay dues to the two compo-

nents, while M(E) is deﬁned as follows:

M(E) =

1.001

0.002001 + sin

(E)

(2)

Where E represents the satellite elevation with re-

spect to the receiver, in degrees. The most difﬁcult

part of the model regards the estimating the values of

z,dry

and T

z,wet

These two values depend on meteorological pa-

rameters such as:

• Atmospheric pressure [P (mbar)].

• Temperature [T (K)].

• Water vapor pressure [e (mbar)].

• Lapse rate temperature [β (K/m)].

• Lapse rate of water vapor [λ (adimensional)]

Since these data are not available in the simulator,

a formula was used to estimate them, based on various

factors, such as: receiver latitude and day of the year:

ξ(φ,D) = ξ

(φ) − ∆ξ(φ)cos



2π(D − D

min

)

365,25



(3)

Where D

min

assumes the value of 211 for the south

latitudes and the value of 28 for the north latitudes.

Values of ξ

(φ) and ∆ξ(φ) represent the average

seasonal variations at latitude (φ) and day of the year

(D) of the receiver, which must be linearly interpo-

lated from the table shown in Figure 4 (Collins, 1999).

Figure 4: Average environmental values.

The terms T

z,dry

and T

z,wet

at zero altitude are the

following:

,dry

−6

(4)

,wet

−6

(λ + 1)g

− βR

(5)

While, to calculate the delay taking into account

also the height of the receiver, the following equations

are used:

z,dry



1 −

βH



· T

,dry

(6)

z,wet



1 −

βH



(λ+1)g

−1

· T

,wet

(7)

Where H is the height of the receiver above

sea level (m), k

=77.604 (K/mbar), k

=382000

/mbar, R

=287.054 J/Kg/K, g

=9.784 m/s

g=9.80665 m/s

Proposal of a Troposphere Model in Simulation for Automotive Applications

123

4.1 Implementation of The Model In

The Simulator

In order to develop the model in the considered simu-

lator, three functions have been created in C program-

ming language able to calculate the signal delay, and

then to add it in the pseudorange calculated for each

satellite.

The main method has been called troposphericDe-

lay, it takes as input the g variable of type gpstime t,

which is a struct that represents the GPS time in the

week-seconds format; an array of three double ele-

ments called llh which contains the latitude, longitude

and altitude of the GPS receiver and ﬁnally an array

of two double elements called azel, which indicates

azimuth and elevation of the satellite with respect to

the receiver. The diagram is shown in Figure 5:

Figure 5: troposphericDelay function.

The other two functions are: gpsTimeToGiorni

and parametroTropos.

The ﬁrst has the aim of simply converting the GPS

time from the week-seconds format, to the day of the

year ranging from 1 to 365, the diagrams can be seen

in Figure 6:

The parametroTropos function, see Figure 7, has

the aim of taking the listed values and, on the basis

of the involved parameter such as latitude, satellite

elevation and day of the year, it calculates the correct

value which will then be used in the main method.

The task of the calcVal function (see Figure 8)

used in the parametroTropos function, is to interpo-

late the values taken from the table.

Figure 6: gpsTimeToGiorni function.

Figure 7: parametroTropos function.

Figure 8: calcVal function.

5 PERFORMANCE EVALUATION

In this section the simulation environment is de-

scribed and the experimental results are discussed in

order to show the behavior of the software simulator

with the adding of the proposed model based on the

Tropospheric Collins algorithm.

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124

5.1 Simulation Environment

In order to test the changes introduced into the sim-

ulator, an appropriate hardware needs to be used.

The sending device uses the HackRf One card with

the ANT500 antenna and the Nooelec Module Tiny

TCXO 10Mhz module, a very precise oscillator with

very low phase noise. It was chosen from the various

options available for the HackRf One card in order to

use GPS applications (see Figure 9).

Figure 9: HackRf One with its ANT500 antenna and Tiny

TCXO 10Mhz oscillator.

Thanks to Google Earth Pro software, a path was

created consisting of eleven points on the map and

then converted into NMEA format, to be simulated

with the GPS-SDR-SIM software. Subsequently, the

output ﬁle of the simulator was transferred in input

to the HackRf One board through the Windows 10

command prompt shown in Figure 10.

Figure 10: Command used to transfer the binary ﬁle to the

HackRf One card.

Once the HackRf One hardware is transmitting the

GPS signal, the smartphone’s GPS receiver is used

to make some considerations. Two experiments were

carried out: the ﬁrst by deactivating the troposphere

correction algorithm and the second one by activating

the implemented algorithm.

5.2 Experimental Results

In the following section some graphics of experimen-

tal results are shown in order to discuss the goodness

of the proposal module implemented in the software

simulator. The line called ”true” path is related to the

real coordinates given in input to the simulator.

Figure 11: Comparison of the two simulated paths with the

’true’ path (intermediate latitude).

Figure 12: Difference in meters between simulated and

’true’ path (intermediate latitude).

Figure 13: Altitude difference between the points of the

simulated and ’true’ path (intermediate latitude).

In Figure 11, it is possible to observe a comparison

between the ”true” path and the two simulated ones:

in red the coordinates given in input at the simulator

are highlighted. These coordinates represent the ref-

erence points from which a GPS receiver should not

deviate too much in order to guarantee a correct GPS

operation. The coordinates calculated by the GPS re-

ceiver are respectively shown in black, with correc-

tion algorithm disabled, and blue, with the correction

algorithm is activated. A big deviation from red line

means a big error by algorithm in calculating the cor-

rect position.

The graphic in Figure 12 shows the positioning

error committed in meters, calculated with respect to

the simulated path.

These two graphics show the results obtained

starting from a path located at an intermediate latitude

and an altitude of about 200 meters above sea level.

The graphic in Figure 13 shows the error commit-

Proposal of a Troposphere Model in Simulation for Automotive Applications

125

ted in calculating the altitude above the sea level by

the receiver.

As it is possible to observe in this Figure the error

on latitude and longitude is on average lower in the

case in which the tropospheric algorithm is activated,

for each point of the considered path.

In order to verify the correct operation of the

model in different situations, experiments were also

carried out at extreme and equatorial latitudes. For

that concern the considered path at extreme latitudes,

a route in Norway was chosen by Google Earth Pro

and the coordinates were given in input to the simula-

tor.

Figure 14: Comparison of the two simulated routes with the

’true’ path (extreme latitude).

Figure 15: Difference in meters between simulated and

’true’ path (extreme latitude).

Figure 16: Altitude difference between the points of the

simulated and ’true’ path (extreme latitude).

Figure 14, 15, 16 show the same output parame-

ters illustrated above (in the case of intermediate lati-

tudes) considering the extreme latitude coordinates.

The same consideration made for the previous

case is possible to done also for this new simulative

campaign. It is possible to observe a better behav-

ior of the system when the proposed module is acti-

vated in respect to the one with troposphere module

disabled.

6 CONCLUSIONS

GNSS simulation is a widely used in practice for test-

ing GPS receivers. In this paper, a particular GPS

simulation software with the additional of a novel

Troposperic Collins model has been considered. The

improvement introduced regards the addition of the

delay of the signal transmitted in the form of a pseu-

dorange in order to simulate a very realistic scenario.

The experiments made to test the troposphere model

in the considered simulator were possible thanks to

a special hardware: Hackrf One with its original

ANT500 antenna and a very precise 10MHz nooelec

oscillator. To better evaluate the applied model, three

different tests were carried out at three different lat-

itudes and, as it can be seen from the graphics, the

main improvements in the application of the model

are in the case of intermediate and equatorial lati-

tudes. The results show how the implemented tro-

posperic software module introduces an improvement

in the system guaranteeing better performance in term

of committed errors.

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