On-the-fly integration of soft and sensor data for enhanced situation assessment-Reference-Cited by-同舟云学术

On-the-fly integration of soft and sensor data for enhanced situation assessment

Published:2017 Issue: Volume:112 Page:1263-1272
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Dragos Valentina,Gatepaille Sylvain

Publisher

Elsevier BV

Subject

General Engineering

Reference17 articles.

1. Stanag 2511 intelligence reports. Technical report, NATO, 2003.

2. Joachim Biermann, Louis de Chantal, Reinert Korsnes, Jean Rohmer, and Cagatay Ündeger From unstructured to structured information in military intelligence-some steps to improve information fusion. Technical report, DTIC Document, 2004.

3. Joachim Biermann, Vincent Nimier, Jesus Garcia, Kellyn Rein, Ksawery Krenc, and Lauro Snidaro. Multi-level fusion of hard and soft information. In Information Fusion (FUSION), 2014 17th International Conference on, pages 1-8. IEEE, 2014.

4. Erik Blasch, Pierre Valin, and Eloi Bosse. Measures of effectiveness for high-level fusion. In Information Fusion (FUSION), 2010 13th Conference on, pages 1-8. IEEE, 2010.

5. Oliver Carr and Dominique Estival. Document classification in structured military messages. In Australasian Language Technology Workshop, 2003.

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