Blackboard Architectures for Sonar Data Association

  • B. A. Mcarthur
Part of the NATO Science Series book series (NAII, volume 70)


Semi-automated systems are being developed to aid naval command teams in building undersea warfare (USW) tactical pictures representing the surface and subsurface environment and containing information regarding the number of detected targets, target identities, and target positions, courses, and speeds. The fusion of multiple acoustic data sources, as well as the incorporation of supporting information from non-acoustic data sources, is recognised as a key requirement for achieving accurate and efficient USW tactical picture compilation. This paper provides an overview of work conducted at the Defence Research Establishment Atlantic (DREA) on the application of the blackboard architecture, a software architecture for distributed problem solving, to sonar data association, a specific aspect of sonar data fusion involving the association of multiple acoustic signals corresponding to a given platform.


Knowledge Source Data Association Temporal Association Acoustic Source Signal Track 
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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  1. 1.
    Iotek, Inc. (1992) Expert Systems Study, Document DN0929.Google Scholar
  2. 2.
    Mclntyre, CM., and Roger, W.A. (1993) Data association in passive acoustic tracking, in O.E. Drummond (ed.), Signal and Data Processing of Small Targets, Proc. SPIE vol. 1954, pp. 376–385.Google Scholar
  3. 3.
    Nii, H.P. (1986) Blackboard Systems: The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures, AI Magazine, pp. 38–53.Google Scholar
  4. 4.
    Nii, H.P. (1986) Blackboard Systems: Blackboard Applications Systems, Blackboard Systems from a Knowledge Engineering Perspective, AI Magazine, pp. 82–106.Google Scholar
  5. 5.
    Nii, H.P., Feigenbaum, E.A, Anton, J.J. and Rockmore, A.J. (1982) Signal-to-Symbol Transformation: HASP/SIAP Case Study, AI Magazine, pp. 23–35.Google Scholar
  6. 6.
    Shahbazian, E., Duquet, J., Valin, P. (1998) A Blackboard Architecture for Incremental Implementation of Data Fusion Application, Proc. Fusion’ 98, pp. 455–461.Google Scholar
  7. 7.
    McArthur, B.A. (1993) An Expert Systems Approach to Track Segment Association, Proc. TTCP GTP-1 Applications Working Group Specialists’ Meeting, Portland, UK.Google Scholar
  8. 8.
    McArthur, B.A. (1994) Sonar Track Segment Association using Fuzzy Confidence Measures, Proc. 6th Symposium on Applications of Expert Systems in DND, Kingston, Canada.Google Scholar
  9. 9.
    McArthur, B.A. (1995) A Knowledge-Based Approach to Processing Unmodelled Events in Contact Data Management, Proc. TTCP GTP-1 Applications Working Group Spring Workshop, Portland, UK.Google Scholar
  10. 10.
    McArthur, B.A. (1997) Goal-Directed Data Integration for Sonar Picture Compilation, Proc. IEEE Intl. Conf. on Systems, Man and Cybernetics, pp. 488–493.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • B. A. Mcarthur
    • 1
  1. 1.Defence Research Establishment AtlanticDartmouthCanada

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