Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-24T02:05:58.011Z Has data issue: false hasContentIssue false
  • English
  • Français

Information Fusion for Localization Within Vehicular Networks

Published online by Cambridge University Press:  07 June 2011

Mahmoud Efatmaneshnik ,
Allison Kealy ,
Asghar Tabatabei Balaei  and
Andrew G. Dempster
Mahmoud Efatmaneshnik*
Affiliation:
(The University of New South Wales, School of Surveying and Spatial Information Systems)
Allison Kealy
Affiliation:
(The University of Melbourne, Department of Geomatics)
Asghar Tabatabei Balaei
Affiliation:
(The University of New South Wales, School of Surveying and Spatial Information Systems)
Andrew G. Dempster
Affiliation:
(The University of New South Wales, School of Surveying and Spatial Information Systems)
*
(Email:[email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained from a research study undertaken to demonstrate the capabilities of DSRC for meeting the positioning accuracies of road safety applications. The results show that a CP algorithm that fully integrates both measured/sensed data as well as navigation information such as map data can meet the positioning requirements of safety related applications of DSRC (<0·5 m). This paper presents the results of a Cramer Rao Lower Bound analysis which is used to benchmark the performance of the CP algorithm developed. The Kalman Filter (KF) models used in the CP algorithm are detailed and results obtained from integrating GPS positions, inter-vehicular ranges and information derived from in-vehicle maps are then discussed along with typical results as determined through a variety of network simulation studies.

Keywords

Cooperative positioningDSRCKalman filterMap matchingVehicular networks
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 3 , July 2011 , pp. 401 - 416
Copyright
Copyright © The Royal Institute of Navigation 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alam, N., Balaei, A. T., & Dempster, A. (2009, 1–3 December). Ranging with DSRC: Facts and Challenges. Paper presented at the International Global Navigation Satellite Systems Society IGNSS Symposium 2009 Holiday Inn Surfers Paradise, Qld, Australia.Google Scholar
Bernstein, D., & Kornhauser, A. (1996). An Introduction to Map Matching for Personal Navigation Assistants. Newark, New Jersey: TIDE Center.Google Scholar
Bétaille, D., & Bonnifait, P. (2000). Road Maintenance Vehicles Location using DGPS, Map-Matching and Dead-Reckoning: Experimental Results of a Smoothed EKF. Paper presented at the IAIN World Congress in Association with the U.S. ION 56th Annual Meeting, San Diego.Google Scholar
Blum, J. J., Eskandarian, A., & Hoffman, L. J. (2004). Challenges of Intervehicle Ad Hoc Networks IEEE Transactions On Intelligent Transportation Systems 5(4), 374–351.CrossRefGoogle Scholar
Chen, Q., Jiang, D., Taliwal, V., & Delgrossi, L. (2006). IEEE 802.11 based vehicular communication simulation design for NS-2, Proceedings of the 3rd international workshop on Vehicular ad hoc networks. Los Angeles, CA, USA: ACM.Google Scholar
Chui, C. K., & Chen, G. (1987). Kalman Filtering with Real-Time Applications. Berlin Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Costa, J. A., Patwari, N., & Alfred, O.Hero, I. (2006). Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Trans. Sen. Netw., 2(1), 39–64.CrossRefGoogle Scholar
Cox, T., & Cox, M. (1994). Multidimensional Scaling: Chapman & Hall.Google Scholar
Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (1999). Monte Carlo localization for mobile robots. In Artificial Intelligence.Google Scholar
Doherty, L., Pister, K. S. J., & Ghaoui, L. E. (2001). Convex Position Estimation in Wireless Sensor Networks. Paper presented at the IEEE INFOCOM.Google Scholar
Efatmaneshnik, M., Balaei, A. T., & Dempster, A. G. (2009). A Channel Capacity Perspective on Cooperative Positioning Algorithms for VANET ION GNSS. Savannah, Georgia.Google Scholar
Greenfeld, J. S. (2002). Matching GPS Observations to Locations on a Digital Map. Newark: Department of Civil and Environmental Engineering, New Jersey Institute of Technology.Google Scholar
Gustafsson, F., & Gunnarsson, F. (2005). Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements. Signal Processing Magazine, IEEE, 22(4), 41–53.Google Scholar
Kealy, A., Scott-Young, A., & Collier, P. (2004). Improving Land Vehicle Safety Using Augmented Reality Technologies. Paper presented at the 4th International Symposium on Mobile Mapping Technology, Kunming, China.Google Scholar
Kealy, A., & Scott-Young, S. (2005). An Integrated Position and Attitude Determination System to Support Real-Time, Mobile, Augmented Reality Applications. Journal of Global Positioning Systems 4(1+2), 12–26.Google Scholar
Parker, R., & Valaee, S. (2007a). Cooperative Vehicle Position Estimation. Paper presented at the Communications, 2007. ICC'07. IEEE International Conference on.Google Scholar
Parker, R., & Valaee, S. (2007b). Vehicular Node Localization Using Received-Signal-Strength Indicator. Vehicular Technology, IEEE Transactions on, 56(6), 3371–3380.Google Scholar
Savvides, A., Garber, W., Adlakha, S., Moses, R., & Srivastava, M. (2003). On the Error Characteristics of Multihop Node Localization in Ad-Hoc Sensor Networks. In Information Processing in Sensor Networks (pp. 555–555).Google Scholar
Tay, J. H., Chandrasekhar, V. R., & Seah, W. K. (2006). Selective Iterative Multilateration for Hop Count-Based Localization in Wireless Sensor Networks, Proceedings of the 7th International Conference on Mobile Data Management: IEEE Computer Society.Google Scholar
Taylor, G., Blewitt, G., Steup, D., Corbett, S., & Car, A. (2001). Road Reduction Filtering for GPS-GIS Navigation. Transactions in GIS, 5(3), 193–207.CrossRefGoogle Scholar