Skip to main content
SpringerLink
Log in
Book cover

Information Quality in Information Fusion and Decision Making pp 587–606Cite as

Adaptive Fusion

  • Chapter
  • First Online:

  • 1232 Accesses

Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

This chapter describes a methodology for adaptively incorporating reliability of information, provided by multiple sensors, to improve the quality of the data fusion result. The adaptivity is achieved by dynamically utilizing auxiliary information, comprising the measure of performance of each sensor or contextual information when it is available. A comparative study of the results obtained by using either source of auxiliary information for adaptivity is presented in this paper.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. L. Snidaro, I. Visentini, Integration of contextual information for tracking refinement, in International Conference on Information Fusion, Chicago, 2011

    Google Scholar 

  2. I. Visentini, J. Llinas, G.L. Foresti, L. Snidaro, Context in fusion: Some considerations in a JDL perspective, in International Conference on Information Fusion, Istanbul, Turkey, 2013

    Google Scholar 

  3. V. Nimier, G. Rogova, Reliability in information fusion: Literature survey, in International Conference on Information Fusion, Stokholm, 2004

    Google Scholar 

  4. E. Duflos, D. Pomorski, P. Vanheeghe, F. Caron, GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects. Information Fusion 7, 221–230 (2006)

    Article  Google Scholar 

  5. J. G. Herrero, L. Snidaro, J. Linas, G. Steetharrama, K. Palaniappan, E. Blasch, Overview of contextual tracking approaches in information fusion, in SPIE Signal Processing, Sensor/Information Fusion, and Target Recognition, 2013

    Google Scholar 

  6. L.A. Zadeh, Probability measures of fuzzy events. J. Math. Anal. Appl. 23, 421–427 (1968)

    Article  MathSciNet  Google Scholar 

  7. V. Nimier, Supervised multisensor tracking algorithm, in International Conference on Information Fusion, Las Vegas, 1998

    Google Scholar 

  8. V. Nimier, Supervising the fusion process by context for target tracking, in Context Enhanced Information Fusion, Springer, 2016

    Google Scholar 

  9. C.Y. Chong, Hierarchical estimation, in ONR Workshop on distributed Communication and Decision Problems, Monterey, 1979

    Google Scholar 

  10. E. Tse, R.P. Whisner, C.Y. Chong, S. Mori, Distributed estimation in distributed sensor networks, in IEEE American Control Conference, Arlington, 1979

    Google Scholar 

  11. Y. Bar-Shalom, A. Houles, Multisensor tracking of a maneuvering target in clutter. IIEE trans. on AES 25(2), 176–189 (1989)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank Dr. Galina Rogova from the State University of New York at Buffalo for her helpful advice on various technical issues examined in this chapter.

Author information

Authors and Affiliations

  1. DTIS/ONERA, Palaiseau, France

    Vincent Nimier & Kaouthar Benameur

Authors
  1. Vincent Nimier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaouthar Benameur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Vincent Nimier or Kaouthar Benameur .

Editor information

Editors and Affiliations

  1. IMT-Atlantique, Brest, France

    Éloi Bossé

  2. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nimier, V., Benameur, K. (2019). Adaptive Fusion. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03643-0_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03642-3

  • Online ISBN: 978-3-030-03643-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation