Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV)

E. I. Adegoke; J. Zidane; E. Kampert; P. A. Jennings; C. R. Ford; S. A. Birrell; M. D. Higgins

doi:10.1109/IVS.2019.8813775

Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV)

E. I. Adegoke, J. Zidane, E. Kampert, P. A. Jennings, C. R. Ford, S. A. Birrell, M. D. Higgins

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

8 Citations (Scopus)

Abstract

Localization accuracy obtainable from global navigation satellites systems in built up areas like urban canyons and multi-storey car parks is severely impaired due to multipath and non-line-of-sight signal propagation. In this paper, a simple classifier was used in discriminating between multipath and line-of-sight GNSS signals. By using the carrier to noise ratio which characterizes the received signal strength of the GNSS signals, and the rate of change of the epochs of the satellite vehicles in view, a prediction accuracy of 98% was attained from the classifier. Also investigated in this paper is the effect of antenna placement on localization accuracy. Our measurement campaign using a Nissan Leaf hatch back model showed that the centre longitudinal line of the roof generated the least localization errors for an urbanized route.

Original language	English
Title of host publication	2019 IEEE Intelligent Vehicles Symposium, IV 2019
Publisher	IEEE
Pages	1007-1012
Number of pages	6
ISBN (Electronic)	978-1-7281-0560-4
ISBN (Print)	978-1-7281-0561-1
DOIs	https://doi.org/10.1109/IVS.2019.8813775
Publication status	Published - 29 Aug 2019
Externally published	Yes
Event	2019 IEEE Intelligent Vehicles Symposium - Paris, France Duration: 9 Jun 2019 → 12 Jun 2019 Conference number: 30 https://iv2019.org/program/

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings
ISSN (Electronic)	2642-7214

Conference

Conference	2019 IEEE Intelligent Vehicles Symposium
Abbreviated title	IV’19
Country/Territory	France
City	Paris
Period	9/06/19 → 12/06/19
Internet address	https://iv2019.org/program/

ASJC Scopus subject areas

Computer Science Applications
Automotive Engineering
Modelling and Simulation

Access to Document

10.1109/IVS.2019.8813775

Cite this

Adegoke, E. I., Zidane, J., Kampert, E., Jennings, P. A., Ford, C. R., Birrell, S. A., & Higgins, M. D. (2019). Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV). In 2019 IEEE Intelligent Vehicles Symposium, IV 2019 (pp. 1007-1012). Article 8813775 (IEEE Intelligent Vehicles Symposium, Proceedings). IEEE. https://doi.org/10.1109/IVS.2019.8813775

Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV). / Adegoke, E. I.; Zidane, J.; Kampert, E. et al.
2019 IEEE Intelligent Vehicles Symposium, IV 2019. IEEE, 2019. p. 1007-1012 8813775 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Adegoke, EI, Zidane, J, Kampert, E, Jennings, PA, Ford, CR, Birrell, SA & Higgins, MD 2019, Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV). in 2019 IEEE Intelligent Vehicles Symposium, IV 2019., 8813775, IEEE Intelligent Vehicles Symposium, Proceedings, IEEE, pp. 1007-1012, 2019 IEEE Intelligent Vehicles Symposium, Paris, France, 9/06/19. https://doi.org/10.1109/IVS.2019.8813775

Adegoke EI, Zidane J, Kampert E, Jennings PA, Ford CR, Birrell SA et al. Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV). In 2019 IEEE Intelligent Vehicles Symposium, IV 2019. IEEE. 2019. p. 1007-1012. 8813775. (IEEE Intelligent Vehicles Symposium, Proceedings). doi: 10.1109/IVS.2019.8813775

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abstract = "Localization accuracy obtainable from global navigation satellites systems in built up areas like urban canyons and multi-storey car parks is severely impaired due to multipath and non-line-of-sight signal propagation. In this paper, a simple classifier was used in discriminating between multipath and line-of-sight GNSS signals. By using the carrier to noise ratio which characterizes the received signal strength of the GNSS signals, and the rate of change of the epochs of the satellite vehicles in view, a prediction accuracy of 98% was attained from the classifier. Also investigated in this paper is the effect of antenna placement on localization accuracy. Our measurement campaign using a Nissan Leaf hatch back model showed that the centre longitudinal line of the roof generated the least localization errors for an urbanized route.",

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AB - Localization accuracy obtainable from global navigation satellites systems in built up areas like urban canyons and multi-storey car parks is severely impaired due to multipath and non-line-of-sight signal propagation. In this paper, a simple classifier was used in discriminating between multipath and line-of-sight GNSS signals. By using the carrier to noise ratio which characterizes the received signal strength of the GNSS signals, and the rate of change of the epochs of the satellite vehicles in view, a prediction accuracy of 98% was attained from the classifier. Also investigated in this paper is the effect of antenna placement on localization accuracy. Our measurement campaign using a Nissan Leaf hatch back model showed that the centre longitudinal line of the roof generated the least localization errors for an urbanized route.

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Evaluating Machine Learning & Antenna Placement for Enhanced GNSS Accuracy for CAVs: 2019 IEEE Intelligent Vehicles Symposium (IV)

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