Capabilities-based force aggregation using random sets

2005 7th International Conference on Information Fusion, FUSION

Volumn 2, Issue , 2005, Pages 872-878

  Svenson, Pontus ^a  

^a SWEDISH DEFENCE RESEARCH AGENCY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL SYSTEM ANALYSIS; GRAPH THEORY; PROBLEM SOLVING; RANDOM PROCESSES; SET THEORY;

FORCE AGGREGATION; GRAPH COLORING; RANDOM SET THEORY; SITUATION ANALYSIS SYSTEMS;

FORCE CONTROL;

EID: 33847112173     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2005.1591950     Document Type: Conference Paper

References (17)

1. D. L. Hall and J. Llinas, editors. Handbook of Muhisensor Data Fusion. CRC Press, Boca Raton, FL, USA, 2001.
2. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM Computing Surveys, 31(3):264-323, 1999.
3. Johan Schubert. Clustering belief functions based on attracting and conflicting metalevel evidence using Potts spin mean field theory. Information Fusion, 5(4):309, 2004.
4. J. Schubert. Evidential force aggregation. In Proceedings of the Sixth International Conference on Information Fusion, Cairns, Australia, pages 1223-1229, Sunnyvale, CA, USA, 2003. International Society of Information Fusion.
5. F. P. Lorenz and J. Biermann. Knowledge-based fusion of formels: discussion of an example. In Proceedings of the Fifth International Conference on Information Fusion, Annapolis, MD, USA, pages 374-379, Mountain View, CA, USA, 2002. International Society of Information Fusion.
6. J. K. Johnson and R. D. Chaney. Recursive composition inference for force aggregation. In Proceedings of the Second International Conference on Information Fusion, Sunnyvale, CA, USA, pages 1187-1195, Mountain View, CA, USA, 1999. International Society of Information Fusion.
7. J. Biermann. HADES - A knowledge-based system for message interpretation and situation determination. In A. Pasqual del Pobil, J. Mira, and M. Ali, editors, Tasks and methods in Applied Artificial Intelligence, Proceedings of the Eleventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, LNCS 1416, pages 707-716, Berlin, Germany, 1998. Springer-Verlag.
8. R. Suzić. Representation and recognition of uncertain enemy policies using statistical models. In Proceedings of the NATO/RTO Conference on Military Data and Information Fusion, Prague, Czech Republic, in press, 2003.
9. Ed Wright, Suzanne Mahoney, and Kathryn Blackmond Laskey. Use of domain knowledge models to recognize cooperative force activities. In Proceedings of the 2001 MSS National Symposium on Sensor and Data Fusion, 2001.
10. Kathryn Blackmond Laskey, Suzanne Mahoney, and Ed Wright. Hypothesis management in situation-specific network construction. In Seventeenth Conference on Uncertainty in Artificial Intelligence, 2001.
11. Robert Suzić. Generic representation of military organisation and military behaviour: UML and Bayesian networks. In Proceedings of the NATO RTO Symposium on C31 and M&S Interoperability, October 2003.
12. I. R. Goodman, R. P. S. Mahler, and H. T. Nguyen. Mathematics of Data Fusion. Kluwer Academic Publishers, Dordrecht, Netherlands, 1997.
13. R. Mahler. An Introduction to Multisource-Multitarget Statistics and its Applications. Lockheed Martin Technical Monograph, 2000.
14. G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ, USA, 1976.
15. Ronald P. S. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, i: Boolean logic. IEEE Transactions on Systems, Man and Cybernetics, A26(1):27, 1996.
16. Dale Fixsen and Ronald P. S. Mahler. The modified dempster-shafer approach to classification. IEEE Transactions on Systems, Man and Cybernetics, 27(1):96, 1997.
17. Erik P. Blasch and Susan Plano. JDL level 5 fusion model "user refinement" issues and applications in group tracking. In Signal Processing, Sensor fusion, and Target Recognition X!. volume 4729 of SPIE, page 270, 2002.