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Optimization and Benchmarking of Truncated Dempster-Shafer for Airborne Surveillance

  • P. Valin
  • D. Boily
Chapter
Part of the NATO Science Series book series (NAII, volume 70)

Abstract

Evidential reasoning requirements that Decision Support Systems (DSS) must often address necessitate that one must:
  1. (1)

    process incomplete information, which implies that ignorance should be defined mathematically

     
  2. (2)

    not require a priori information, which usually rules out Bayes reasoning

     
  3. (3)

    handle conflicts between contact/track so that formalisms such as Dempster- Shafer (DS), where conflict is precisely defined, are favored

     
  4. (4)

    have a real-time method, which means that DS truncation is a must

     
  5. (5)

    present the operator with the best ID, i.e. give preference to singletons

     
  6. (6)

    present the operator with the next best thing, i.e. doublets, then triplets...

     
  7. (7)

    be tested operationally, which translates into building complex scenarios

     
  8. (8)

    ensure that ordinary DS explodes, which requires a large complex Platform Data Base (PDB)

     
  9. (9)

    resist Counter Measures (CM) such that several contact reports should conflict with information contained in the PDB

     
  10. (10)

    resist false associations, i.e. ESM reports associated to wrong track

     

Keywords

Decision Rule Synthetic Aperture Radar Sensor Fusion Basic Probability Assignment Canadian Contingent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Valin, P. and Boily, D., (2000) “Truncated Dempster-Shafer Optimization and Benchmarking”, SPIE Aerosense 2000, in Sensor Fusion: Architecture, Algorithms and Applications IV, Vol. 4051, pp. 237–246, Orlando.CrossRefGoogle Scholar
  2. 2.
    Jouan, A. Valin, P. and Bossé, E., (1999) “Testbed for Fusion of Imaging and Non-Imaging Sensor Attributes in Airborne Surveillance Missions”, FUSION’ 99 conference, Vol. 2, pp. 823–830, Sunnyvale, CA.Google Scholar
  3. 3.
    Cheaito, A. Lecours, M. and Bossé, E., (1999) “Study of a modified Dempster-Shafer approach using an Expected Utility Interval decision rule”, SPIE Aerosense 1999, in Sensor Fusion: Architecture, Algorithms and Applications III, Vol. 3719, pp. 34–42, Orlando.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • P. Valin
    • 1
  • D. Boily
    • 2
  1. 1.Lockheed Martin CanadaMontréalCanada
  2. 2.Centre de Recherches MathématiquesUniversité de MontréalMontréalCanada

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