Basic Intelligent Models for Validation of Dynamic GNSS

8th Workshop on Analysis of Dynamic Measurements, Turin, Italy – May 5-6 2014

Basic Intelligent Models for Validation of Dynamic GNSS Measurements Federico Grasso Toro, Prof. Eckehard Schnieder

May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 2

Technische Universität Braunschweig

Technische Universität Braunschweig

Braunschweig

NFF Braunschweig

Carl Friedrich Gauss


Overview

Motivation

GNSS Quality attributes hierarchy

Certification process for GNSS receivers

Description of the accuracy-based GNSS dynamic data evaluation

Example

Summary


Motivation

Situation: Global Navigation Satellite System (GNSS) based localisation systems

Evaluation of the position related to an independent reference.

Focussed (safety-relevant) applications:

Advanced driver assistance systems GPS-based vehicle localisation with intelligent maps

Track selective localisation Safety case demands dynamic measurement evaluations ⇒ Use of deviation evaluation procedures.


Motivation

Problem:

Dynamic measurements conditions Continuously varying GNSS constellation conditions Deviation uncertainty

⇒ Not covered by the deviation evaluation

Goal:

Real-time deviation evaluation: With very limited on-board computing power Intelligent interpretation of accuracy-based GNSS evaluation

Approach:

Artificial Neural Networks for quantitative and qualitative evaluation of dynamical systems

Gauß-Krüger Easting

Gau

ß-K

rüge

r Nor

thin

g

4.420.800 4.420.900 4.421.000 4.421.100 4.421.200 4.421.300 4.421.400 4.421.500 4.421.6005.214.100

5.214.200

5.214.300

5.214.400

5.214.500

5.214.600

5.214.700

5.214.800

5.214.900GNSS dataReference


GNSS Quality attributes hierarchy Focused on accuracy


Certification process for GNSS receivers Based on accuracy evaluations







0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

-4

-2

0

2

4

Easting deviation [m]

Easting deviationAverage easting deviation

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

-4

-2

0

2

4

Northing deviation [m]

Time [s]

Northing deviationAverage northing deviation

-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Easting deviation [m]

Nor

thin

g de

viat

ion

[m]

2D Mahalanobis Distance colored scatter-plotwith Mahalanobis Ellipses for 1, 2, 3 and 4σ

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330123456

Deviation module [m]

Deviation moduleAverage deviation module

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33-180

-90

0

90

180 Deviation angle [degrees]

Deviation angleAverage deviation angle

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50

0.10.20.30.40.50.60.70.8

Data

Density

Deviation module dataLognormal distribution for deviation module

-4 -3 -2 -1 0 1 2 30

0.1

0.2

0.3

0.4

0.5

0.6

Data

Density

-4 -3 -2 -1 0 1 2 3 40

0.1

0.2

0.3

0.4

Data

Density

Easting deviation dataNormal distribution for Easting deviation

Northing deviation dataNormal distribution for Northing deviation


Description of the accuracy-based GNSS dynamic data evaluation Artificial Neural Networks

- Quantitative Evaluation

- Qualitative Evaluation


Description of the accuracy-based GNSS dynamic data evaluation ANN quantitative evaluation

Trueness (deviation module)

Precision (Mahalanobis

Distance)

Gauß Krüger Easting 8.18 % 20.80 % Gauß Krüger Northing 31.32 % 31.40 %

Number of Used Satellites 12.62 % 2.47 %

HDOP 19.18 % 31.29 % Speed 00.05 % 7.56 %

Geometric mean of SNR 28.64 % 6.48 %


Description of the accuracy-based GNSS dynamic data evaluation ANN qualitative evaluation

Date 03.02.2009 Samples 2709

Number of used satellites Minimum 4 Average 6

Maximum 10

HDOP Minimum 1 Average 2.04

Maximum 14.4

Speed [Km/h] Minimum 0 Average 25.06

Maximum 60.68

Geometric mean of the SNR. [dB]

Minimum 32 Average 45.73

Maximum 50


Example Short journey

Date 29.05.2008 Samples 2411

Number of used satellites Minimum 4 Average 6

Maximum 9

HDOP Minimum 0.9 Average 2.24

Maximum 9.4

Speed [Km/h] Minimum 0 Average 27.99

Maximum 60.77

Geometric mean of the SNR. [dB]

Minimum 37.95 Average 47.08

Maximum 50


Example Short journey – Map representation


Example Short journey – ANN validation tools performance analysis






Summary

Problems: Dynamic measurements conditions Continuously varying GNSS constellation conditions Deviation uncertainty

Approach: Artificial Neural Networks for quantitative and qualitative evaluation of dynamical systems

Results: Behaviour of accuracy-based quality of the localisation system in quantitative and

qualitative evaluations.

Intelligent evaluation of the GNSS data, focused on the trueness and precision (accuracy)

Presented approach is suitable for advanced applications in transportation: 1) on-board uncertainty evaluation of vehicle localisation. 2) Advanced driver assistance systems. 3) GNSS-based vehicle localisation with intelligent maps. 4) Track selective localisation.


Thank you

Contact: Ing. Federico Grasso Toro

Institute for Traffic Safety and Automation Engineering Technische Universität Braunschweig, Germany

[email protected] www.iva.ing.tu-braunschweig.de Research project QualiSaR is funded by:

www.qualisar.eu

mailto:[email protected]

http://www.iva.ing.tu-bs.de/

http://www.qualisar.eu/

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Basic Intelligent Models for Validation of Dynamic GNSS