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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
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 3
Overview
Motivation
GNSS Quality attributes hierarchy
Certification process for GNSS receivers
Description of the accuracy-based GNSS dynamic data evaluation
Example
Summary
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 4
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.
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 5
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
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 6
GNSS Quality attributes hierarchy Focused on accuracy
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 7
Certification process for GNSS receivers Based on accuracy evaluations
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 8
Description of the accuracy-based GNSS dynamic data evaluation
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 9
Description of the accuracy-based GNSS dynamic data evaluation
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 10
Description of the accuracy-based GNSS dynamic data evaluation
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
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 11
Description of the accuracy-based GNSS dynamic data evaluation Artificial Neural Networks
- Quantitative Evaluation
- Qualitative Evaluation
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 12
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 %
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 13
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
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 14
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
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 15
Example Short journey – Map representation
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 16
Example Short journey – ANN validation tools performance analysis
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 17
Example Short journey – ANN validation tools performance analysis
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 18
Example Short journey – ANN validation tools performance analysis
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 19
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.
May 6 2014 | Federico Grasso Toro | Basic Intelligent Models for Validation of Dynamic GNSS Measurements | Slide 20
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