A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution

Intelligent Data Analysis

Volumn 15, Issue 3, 2011, Pages 399-411

  Yang, Jianping ^b   Huang, Hong Zhong ^a   Miao, Qiang ^b   Sun, Rui ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b Unit 95389 of People's Liberation Army   (China)

Author keywords

belief convergence; D S evidence theory; evidence conflict; information fusion

Indexed keywords

ABSOLUTE DIFFERENCE; BASIC PROBABILITY ASSIGNMENT; BELIEF CONVERGENCE; COMPARISON STUDY; CONFLICT RESOLUTION; D S EVIDENCE THEORY; DEMPSTER SHAFER EVIDENCE THEORY; DEMPSTER-SHAFER; EVIDENCE CONFLICT; EVIDENCE THEORIES; NOVEL INFORMATION; NUMERICAL EXAMPLE; TWO SOURCES;

ALGORITHMS; INFORMATION FUSION;

CONVERGENCE OF NUMERICAL METHODS;

EID: 79957792849     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2010-0474     Document Type: Article

References (27)

1. N.A. Bomberger, A.M. Waxman and B.J. Rhodes, A new approach to higherlever information fusion using associative learning in semantic networks of spiking neurons, Information Fusion 8(2007), 227-251. (Pubitemid 46497874)
2. L.I. Perlovsky, Cognitive high level information fusion, Information Science 177 (2007), 2099-2188.
3. S. Yue and F.C. Rind, Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes, Computer Vision and Image Understanding 104 (2006), 48-60. (Pubitemid 44349072)
4. G.A. Carpenter, S. Marens and O.J. Ogas, Selforganizing information fusion and hierarchical knowledge discovery: A new framework using ARTMAP neural networks, Neural Network 18 (2005), 287-295. (Pubitemid 40719530)
5. G. Kamberova, R. Mandelbaum and M. Mintz, Decisiontheoretic approach to robust fusion of location data, Journal of the Franklin InstituteEngineering and Applied Mathematics 366 (1999), 269-284. (Pubitemid 129665638)
6. A. Gelman, J.B. Carlin et al., Bayesian Data Analysis, 2nd ed, Chapman and Hall/CRC, 2004.
7. S. Fabre, A. Appriou and X. Briottet, Presentation and description of two classification methods using data fusion based on sensor management, Information Fusion 2 (2001), 49-71. (Pubitemid 33632550)
8. G. Shafer, A mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
9. B.N. Nelson and P.D. Gader, Fuzzy set information fusion in landmine detection, Proceedings of SPIEThe International Society for Optical Engineering 3710(11) (1999), 1168-1178.
10. M. Oussalah, H. Maaref and C. Barret, New fusion methodology approach and application to mobile robotics: Investigation in the framework of possibility theory, Information Fusion 2 (2001), 31-48. (Pubitemid 33632549)
11. C. Sossai, P. Bison and G. Chemello, Sensor fusion for localization using possibility theory, Control Engineering Practice 7 (1999), 773-782.
12. O. Basir and X.H.Yuan, Engine fault diagnosis based onmultisensor information fusion using DempsterShafer evidence theory, Information Fusion 8 (2007), 379-386. (Pubitemid 46818139)
13. X. Fan and M.J. Zuo, Fault diagnosis of machines based on DS evidence theory. Part 1: DS evidence theory and its improvement, Pattern Recognition Letters 27(5) (2006), 366-376. (Pubitemid 43242914)
14. X. Fan and M.J. Zuo, Fault diagnosis of machines based on DS evidence theory. Part 2: Application of the improved DS evidence theory in gearbox fault diagnosis, Pattern Recognition Letters 27(5) (2006), 377-385. (Pubitemid 43242915)
15. C.R. Parikh, M.J. Pont and N.B. Jones, Application of Dempster-Shafer theory in condition monitoring applications: A case study, Pattern Recognition Letters 22(6-7) (2001), 777-785. (Pubitemid 32411934)
16. R.W. Jones, A. Lowe and M.J. Harrison, A framework for intelligent medical diagnosis using the theory of evidence, Knowledgebased Systems 15 (2002), 77-84. (Pubitemid 34040864)
17. D. Cai,M.F.Mctear and S.I.Mcclean, Knowledge discovery in distributed databases using evidence theory, International Journal of Intelligent Systems 15 (2000), 745-761.
18. L. Zadeh, A simple view of the Dempstershafer framework and new combination rules, AI Magazine 7 (1986), 85-90. (Pubitemid 16592237)
19. R.R. Yager, On the DempsterShafer framework and new combination rules, Information Sciences 41 (1987), 93-138.
20. D. Dubois and H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence 3 (1988), 244-264.
21. P. Smets, The combination of evidence in the transferable belief model, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5) (1990), 447-458. (Pubitemid 20702887)
22. E. Lefevre, O. Colot and P. Vannoorenberghe, Belief function combination and conflict management, Information Fusion 3 (2002), 149-162.
23. C.K. Murphy, Combining belief functions when evidence conflicts, Decisions Support Systems 29 (2000), 1-9.
24. L.Z.Chen,W.K. Shi et al., Anew fusion approach based on distance of evidence, Journal of Zhejiang University (Science) 6A(5) (2005), 476-482.
25. A. Ayoun and P. Smets, Data association in multitarget detection using the transferable belief model, International Journal of Intelligent Systems 16(10) (2001), 1167-1182. (Pubitemid 32975205)
26. A. Rakar, D.X. Juricic and P. Balle, Transferable belief model in fault diagnosis, Engineering Application of Artificial Intelligence 12 (1999), 555-567. (Pubitemid 32082364)
27. W. Liu, Analyzing the degree of conflict among belief functions, Artificial Intelligence 170 (2006), 909-924. (Pubitemid 44037538)