Efficient combination approach of conflict evidence

Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves

Volumn 23, Issue 1, 2004, Pages 27-32

  Deng, Yong ^a   Shi, Wen Kang ^a   Zhu, Zhen Fu ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b National Key Laboratory on Optical Features of Environment and Target   (China)

Author keywords

Combination rule; Conflict evidence; Evidence theory; Target recognition

Indexed keywords

AUTOMATIC TARGET RECOGNITION; FUNCTION EVALUATION; MODIFICATION; NUMERICAL METHODS;

COMBINATION RULE; CONFLICT EVIDENCE; INFORMATION FUSION;

INFORMATION TECHNOLOGY;

EID: 2542513117     PISSN: 10019014     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Dempster A P. Upper and lower probabilities induced by a multi-valued mapping [J]. Annual Math Statist, 1967, 38(4): 325-339
2. Shafer G. A Mathematical Theory of Evidence [M]. Princeton University. Press, Princeton, 1976
3. Yang J B. Singh M G. An evidential reasoning approach for multiple-attributed decision making with uncertainty [J]. IEEE Transaction on System, Man and Cybernetics, 1994, 24(1): 1-18
4. Yang J, Sen P. A general multilevel evaluation process for hybrid MADM with uncertainty [J]. IEEE Transaction on System, Man and Cybernetics, 1994, 24(10): 1458-1473
5. Drukopoulos E, Hsia Y T, Smets P. Transferable belief model for decision making in the evaluation based systems [J]. IEEE Trans on System, Man and Cybernetics, 1996, 26(1): 698-712
6. Walley P. Measure of uncertainty in expert system[J], Artificial Intelligence, 1996, 83(1): 1-58
7. Zadeh L. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination [J]. AI Magazine, 1986, 7(1): 85-90
8. KANG Yao-Hong. The Theory of Data Fusion [M], Xi'an: Xidian University Press, 1997
9. Yager R R. On the Dempster-Shafer framework and new combination rules [J]. Information Science, 1989, 41(2): 93-137
10. Dubois D, Prade H. Representation and combination of uncertainty with belief functions and possibility measures [J], Computational Intelligence, 1998(4): 244-264
11. Smets P. The combination of evidence in the transfer belief model [J]. IEEE Transaction on Pattern and Machine Intelligence. 1990, 6(5): 447-458
12. Smets P, R. Kennes. The transfer belief model [J]. Artificial Intelligence, 1994, 66(3): 191-234
13. Lefevre E, Colot O, Vannoorenberghe P. Belief function combination and conflict management [J], Information Fusion, 2002, 3(3): 149-162
14. ZHANG Shan-Ying, PAN Quan, ZHANG Hong-Cai. A new kind of combination rule of evidence theory [J]. Control and Decision. 2000, 15(5): 540-544
15. SUN Quan, YE Xiu-Qing, GU Wei-Kang. A new combination rule of evidence [J]. Acta Electronica Sinica. 2000, 28(8): 117-119
16. PAN Quan, ZHANG Shan-Ying, CHEN Yong-Mei, et al. Some research on robustness of evidence theory [J], Acta Automatica Sinica, 2001, 27(6): 4798-4805
17. Haenni R. Are alternatives to Dempster's rule of combination real alternatives: Comments on About the belief function combination and the conflict management problem [J]. Information Fusion, 2002, 3(4): 237-239
18. Murphy C K, Combining belief functions when evidence conflicts [J], Decision support systems, 2000, 29(1): 1-9
19. Jousselme A L, Grenier D, Bosse E. A new distance between two bodies of evidence [J]. Information fusion, 2001, 2(1): 91-101
20. WANG Yang, ZHENG Qin-Bo, ZHANG Jun-Ping, Target classification of the data fusion in multichannel using Dempster-Shafer method [J]. Journal of Infrared Millimeter Waves, 2002, 21(3): 229-232
21. LI Qiu-Hua, LI Ji-Cheng, SHEN Zhen-Kang, et al. A kind of method for IR small target recognition based on multi-sensors spatio-temporal information fusion [J], Journal of Infrared Millimeter Wave, 2002, 21(3): 209-212