Skip to main content

Abstract

An overview of data fusion approaches is provided from the signal processing viewpoint. The general concept of data fusion is introduced, together with the related architectures, algorithms and performance aspects. Benefits of such an approach are highlighted and potential applications are identified. Case studies illustrate the merits of applying data fusion concepts in real world applications.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hall, D.L., Llinas, J.: An introd. to multis. data fus... Proc. IEEE 85, 6–23 (1997)

    Article  Google Scholar 

  2. White Jr., F.E.: Joint directors of laboratories data fusion subpanel report. In: Proceedings of the Joint Service Data Fusion Symposium, DFS 1990, pp. 484–496 (1990)

    Google Scholar 

  3. Worden, K., Dulieu-Barton, J.M.: An overview of intelligent fault detection in systems and structures. Structural Health Monitoring 3, 85–98 (2004)

    Article  Google Scholar 

  4. Chong, C.Y., Kumar, S.P.: Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE 91, 1247–1256 (2003)

    Article  Google Scholar 

  5. Dybowski, R., et al. (eds.): Journal of Machine Learning Research: Special issue on the fusion of domain knowledge with data for decision support (July 2003)

    Google Scholar 

  6. Adali, T., et al. (eds.): IEEE Transactions on Neural Networks: Special issue on intelligent multimedia processing (July 2002)

    Google Scholar 

  7. Dasarathy, B.V.: Sensor fusion potential exploitation – Innovative architectures and illustrative applications. Proceedings of the IEEE 85, 24–38 (1997)

    Article  Google Scholar 

  8. Kantz, H., Schreiber, T.: Nonlinear TSE. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  9. Gautama, T., et al.: A novel method for determining the nature of time series. IEEE Transactions on Biomedical Engineering 51, 728–736 (2004)

    Article  Google Scholar 

  10. Mandic, D.P., Chambers, J.A.: RNNs for Prediction. Wiley, Chichester (2001)

    Google Scholar 

  11. Cichocki, A., Amari, S.I.: Adaptive Blind Signal and Image Proc. Wiley, Chichester (2002)

    Book  Google Scholar 

  12. Deco, G., Obradovic, D.: An I.T. Approach to Neural Computing. Springer, Heidelberg (1997)

    Google Scholar 

  13. Wald, G.: http://www.data-fusion.org/article.php?sid=70

  14. Bass, T.: Intrus. detect. and multis. data fusion. Comm. ASM 43, 99–105 (2000)

    Google Scholar 

  15. Tax, D.M., et al.: Combining multiple classifiers. Pat. Rec. 33, 1475–1485 (2000)

    Article  Google Scholar 

  16. Brooks, R.R., Ramanathan, P., Sayeed, A.M.: Distributed target classification and tracking in sensor networks. Proceedings of the IEEE 91, 1162–1171 (2003)

    Article  Google Scholar 

  17. Coatrieux, J.L.: A look at integrative science: Biosignal processing and modelling. IEEE Engineering in Medicine and Biology Magazine 23, 9–12 (2004)

    Article  Google Scholar 

  18. Zhao, F., et al.: Collaborative signal and information processing: An information–directed approach. Proceedings of the IEEE 91, 1199–1209 (2003)

    Article  Google Scholar 

  19. Sasiadek, J.Z.: Sensor fusion. Annual Reviews in Control 26, 203–228 (2002)

    Article  Google Scholar 

  20. Waltz, E., Llinas, J.: Multisensor Data Fusion. Artech House (1990)

    Google Scholar 

  21. Pau, L.F.: Sensor Data Fusion. Jnl. of Intel. and Robot. Sys. 1, 103–116 (1988)

    Article  Google Scholar 

  22. Alarcon, V., Barria, J.: Anom. det. in com. net. IEE Proc. Com. 148, 355–362 (2001)

    Article  Google Scholar 

  23. Obradovic, D., et al.: Sensor Fusi In Siemens Car Navigation System. In: Proc. of MLSP 2004, pp. 655–664 (2004)

    Google Scholar 

  24. Mandic, D.P.: http://www.commsp.ee.ic.ac.uk/~mandic

  25. Zhu, C., Kuh, A.: Sensor Network Loc. Using Pat. Rec. In: Proc. of HISC (2005)

    Google Scholar 

  26. Sommer, D., et al.: Appl. LVQ to detect drivers dozing–off. In: Proc. EUNITE (2002)

    Google Scholar 

  27. Wang, W.: et al.: Video Assisted Speech Source Sep. In: Proc. ICASSP, pp. 425–427 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mandic, D.P. et al. (2005). Data Fusion for Modern Engineering Applications: An Overview. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_114

Download citation

  • DOI: https://doi.org/10.1007/11550907_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics