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EGNSS Transport Applications

H2020-GALILEO-GSA-2017-1

Project Number

776355

Deliverable D2.2

Galileo Positioning System for Trucks

Authorised by: Reviewed by:

Axel Koppert

Roland Trauter Axel Koppert Daimler AG TeleConsult Austria GmbH

Authorised date: 31/01/2018

Work package: WP2 – Precise Vehicle Positioning

Prepared By/Enquiries To: Axel Koppert ([email protected])

(TeleConsult Austria GmbH)

Reviewer: Axel Koppert ([email protected])

(TeleConsult Austria GmbH)

Status: Final

Date: 31/01/2019

Version: 1.0

Classification: Public

Ref. Ares(2019)569133 - 31/01/2019

mailto:[email protected]

mailto:[email protected]

Galileo Positioning System for Trucks D2.2

Public/Confidential Deliverable 2 TransSec

TransSec Project Profile

Partners

Partner Country

DAIMLER AG GERMANY

TELECONSULT AUSTRIA GMBH AUSTRIA

FUNDACION CENTRO DE TECNOLOGIAS DE INTERACCION VISUAL Y COMUNICACIONES VICOMTECH

SPAIN

WATERFORD INSTITUTE OF TECHNOLOGY IRELAND

UNIVERSITAET STUTTGART GERMANY

Grant Agreement number: 776355

Acronym: TransSec

Title: Autonomous emergency manoeuvring and movement monitoring for road transport security

URL: www.transsec.eu

Start Date: 01/02/2018

Duration: 36 months

http://www.transsec.eu/


Public Deliverable 3 TransSec

Document Control

This deliverable is the responsibility of the Work Package Leader. It is subject to internal review and formal au-thorisation procedures in line with ISO 9001 international quality standard procedures.

Version Date Author(s) Change Details

0.1 25/01/2019 Axel Koppert Draft Version

0.2 31/01/2019 Martin Wachsmuth Map Preview

1.0 31/01/2019 Axel Koppert Internal review



Executive Summary

Objectives

Deliverable 2.2 documents the demonstration of the TransSec Galileo positioning system. The developed soft-

ware components are the first step towards the integrated positioning system, where further sensor observations

added to the positioning system.

Results

1. We developed a multi-frequency multi-constellation real-time PPP software.

2. The developed system was tested during a demonstration drive in a typical TransSec scenario

3. We report the results of the conducted tests and identify areas of improvement



Table of Contents

1 The TransSec Galileo Positioning System ....................................................................... 8

2 Demonstration ............................................................................................................. 9

2.1 Test Setup ......................................................................................... 9

2.2 Scenario .......................................................................................... 11

3 Results ....................................................................................................................... 12

4 Map Preview .............................................................................................................. 15

5 Conclusion .................................................................................................................. 17

6 References ................................................................................................................. 18



Figures

Figure 1 - The Mercedes-Benz Atego truck used in the test drive ........................................................ 9

Figure 2 – The test setup .............................................................................................................. 10

Figure 3 – Real-time map visualization ............................................................................................ 10

Figure 4 – Test Trajectory ............................................................................................................. 11

Figure 5 – Impressions from the test drive ...................................................................................... 11

Figure 6 – Cumulative density of the positioning errors .................................................................... 12

Figure 7 – Time series of the positioning errors ............................................................................... 13

Figure 8 – Time series of the standard deviations of the ambiguity estimation ................................... 13

Figure 9 – Map representation with a higher zoom factor ................................................................. 15

Figure 10 - Representation with lower zoom factor .......................................................................... 16

Figure 11 - Setup during the demonstration .................................................................................... 16



Tables

Table 1 – Numeric Results for 1480 epochs ..................................................................................... 12



1 The TransSec Galileo Positioning System

We developed a real-time Precise Point Positioning Software Demonstrator (PPP) that is able to process

multi-frequency multi-constellation GNSS observations. The software receives RTCM SSR corrections via

NTRIP and the EGNOS ionospheric model via EDAS. The developed demonstrator is the first step to-

wards the integrated TransSec positioning system, where the raw observations will be fused with further

sensors. The focus during the last period of the project was more on providing a good basis for the inte-

grated system than on building a stand-alone GNSS positioning system.

We make use of undifferenced and uncombined GNSS pseudorange and carrier phase observations, i.e.

instead of forming explicitly an ionosphere-free linear combination of the dual frequency observations

we estimate the ionosphere slant delay from the observations. Moreover, we can constrain the iono-

sphere slant parameter using an ionosphere model [e.g. De Bakker 2015]. Due to the uncombined ap-

proach, the software is very flexible with respect to the observables provided by the GNSS receiver and

is in general suitable for single-, double- or triple-frequency positioning.

We estimate the following parameters:

Antenna position and velocity

Receiver clock bias (one per GNSS used)

Receiver hardware biases (one per signal used for code and phase)

Ionospheric residual slant delay (one per satellite)

Tropospheric zenith delay

The processing relies on precise orbit and clock corrections as well as satellite signal biases provided in

their State Space Representation (SSR). We implemented an NTRIP client, which is able to decode all

RTCM messages relevant for multi-frequency and multi-constellation positioning. We use the NTRIP

streams from the IGS RTS service [IGS2014]. The implemented functional models are compatible with

the RTCM standard, especially the satellite code and phase biases provided by the NTRIP streams.

As ionosphere information, we use the EGNOS ionosphere model, which is available in real-time either

through the EGNOS signal in space or through internet (EDAS) [ESA 2015]. We implemented a SiSNet

client for receiving the ionosphere data from EDAS, which makes the system independent from satellite

EGNOS visibility.

All software components have been developed in C++ to ensure appropriate real-time performance on

several computing platforms.



2 Demonstration

We tested the positioning system on 22nd and 23rd of January 2019 and demonstrated its functionality in

test drives between Stuttgart-Untertürkheim and Esslingen on 23rd of January 2019.

2.1 Test Setup

We installed the system in a Mercedes-Bent Atego truck (Figure 1).

Figure 1 - The Mercedes-Benz Atego truck used in the test drive

The setup for the demonstration drive is depicted in Figure 2. The PPP software and the clients for

NTRIP and EDAS ran on a laptop. The Laptop was connected to a LTE router, which provided the inter-

net connection to receive the SSR correction data via NTRIP and the EGNOS ionosphere model via

EDAS. We used a Trimble BX982 receiver and a geodetic antenna. The GNSS antenna was installed on

the roof of the truck. The receiver provides dual-frequency observations of GPS, Galileo, Beidou and

GLONASS. During the test observations from GPS (L1 C/A, L2C) and Galileo (E1BC, E5bIQ) where used

for the position estimation. The interface to the GNSS receiver was realized via TCP/IP and the observa-

tion data has been received in the BINEX format. The PPP result was visualized in real-time on a map

(Figure 3). The interface between the PPP software and the map software was established using a serial

port connection and the position was given as NMEA string.



Figure 2 – The test setup

Figure 3 – Real-time map visualization



2.2 Scenario

For the demonstration, we selected a typical TransSec scenario in the inner city of Esslingen in Germany

(Figure 4). In this area several TransSec use cases can be found in a small area: A pedestrian zones, a

bus station (Figure 5 left), one-way streets and lively places where Christmas markets and festivals take

place. The drive was conducted mainly in a typical European urban environment. The length of the pre-

sented test drive section was 25 minutes.

Figure 4 – Test Trajectory

Figure 5 – Impressions from the test drive



3 Results

To assess the quality of the trajectory obtained during the demo drive we compare it to a reference tra-

jectory. The results are presented in Table 1. Figure 6 and 7 visualize the cumulative density of the posi-

tioning errors and the time series of the positioning errors.

The 95-percentiles in north and east direction are 0.76 m and 0.57 m respectively. The time series of

the positioning errors (Figure 7) show that there are several short sections with degraded accuracy,

where the errors exceed the target accuracy level of 0.5 m.

Table 1 – Numeric Results for 1480 epochs

Percentile North [m] East [m]

50 % 0.28 0.22

75 % 0.44 0.33

95 % 0.76 0.57

Figure 6 – Cumulative density of the positioning errors



Figure 7 – Time series of the positioning errors

Figure 8 – Time series of the standard deviations of the ambiguity estimation

Figure 8 reveals an interesting aspect of the obtained solution. The plot shows the standard deviation of

the estimated ambiguities. In an ideal situation, each ambiguity is initialized once and converges to-

wards its true value. This is reflected by a decreasing standard deviation. In case of tracking losses or

detected cycle slips the ambiguity parameter is reset in the current software implementation. This is

realized by increasing its standard deviation of the corresponding parameter to allow a convergence to

its new value. This explains the behaviour of the standard deviations in Figure 8. We observe frequent

ambiguity resets in the urban environment, which hinders the convergence of the individual ambiguities.



Consequently, we cannot fully exploit the accuracy of the phase observations. We identify this as the

main area of improvement. In the next phase of the project, the PPP software will be extended in order

to process the GNSS observations together with IMU and computer vision information. There are several

approaches, which exploit information from those sensors to detect cycle slips and estimate their size.

Accurate estimation of the size of a cycle slip (and fixing the size to their true integer value) can greatly

improve the accuracy in situations with frequent tracking losses (e.g. [Banville and Langley 2009], [Sim-

sky 2014]).



4 Map Preview

During the demonstration a real-time map preview was shown to demonstrate that the interface be-

tween the positioning system and the map software was established.

USTUTT used Here Maps map data in the shape file format for this preview presentation. The software

was written in Mathworks MATLAB and also the map display was done using MATLAB. USTUTT received

the coordinates of the vehicle from the GNSS positioning system through a serial connection as NMEA

strings. Once a new coordinate is provided by the GNSS positioning system, the vehicle position can be

presented in the map by the MATLAB program in real time.

Figure 9 – Map representation with a higher zoom factor



Figure 10 - Representation with lower zoom factor

In Figure 9 and Fehler! Verweisquelle konnte nicht gefunden werden. one can see the street

network with its lines and nodes in green and the vehicle positions with little black stars. It could be

shown, that the representation ran in real time and was displayed for the co-driver on a laptop and for

the driver as well on a separate monitor. The following Figure 1 shows the setup used at this demon-

strator. On the left, one can see the monitor for the driver and in the lower middle part is the laptop

running the software.

Figure 11 - Setup during the demonstration



5 Conclusion

In this deliverable, we described the demonstrator of the TransSec positioning system. After Month 12 of

the project, a first real-time implementation of the PPP software is available. The software is able to

process multi-constellation multi-frequency GNSS observations together with precise orbit, clock and

ionosphere information obtained from the internet. It has been tested in the Truck in a relevant scenar-

io. Currently we reach an accuracy between 0,5 m and 0,75 m in light urban environment. We demon-

strated the integration of the positioning software with TransSec’s map module.

Our analysis shows that the positioning accuracy of the current implementation suffers from the fre-

quent cycle slips in the urban environment. Therefore, we will implement a cycle-slip repair algorithm. In

the next project phase, the software will be extended in order to integrate IMU and computer vision

information. This will improve the accuracy in urban environment.



6 References

[Banville and Langley

2009]

Simon Banville, and Richard B. Langley. "Improving real-time kinematic PPP with instantaneous cycle-slip correction" Proceedings of ION GNSS. Vol. 1619, 2009.

[De Bakker 2015] Peter de Bakker, “On User Algorithms for GNSS Precise Point Positioning”, Doctoral Thesis, TU Delft, 2015.

[ESA 2015] European GNSS Agency, “EGNOS Open Service (OS) SDD”, 2015.

[IGS 2014] International GNSS Service, “Introducing the IGS Real-time Service”, 2014.

[Simsky 2014] Simsky, Andrew, "Gap Bridging in Precise Point Positioning," Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, September 2014, pp. 1035-1045.

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