Conflict Evidence Combination Method Based on Interval Distance

doi:10.3969/j.issn.1000-1093.2020.06.010

Abstract

Abstract: A conflict evidence combination method based on uncertain interval distance is proposed for the anti-intuitive problem about combination results of multiple pieces of evidences containing conflict evidence. Through the analysics in Refs. ［13, 15, 17］, it is found that the interval composed of Bel and Pl function contains all the information of an event, which solves the problem of information missing in the process of evidence combination directly composed of Mass function. The expression of uncertain interval is given, and then an interval distance is defined according to the uncertain interval. The interval distances among the same focal elements of different evidences are calculated, and the interval distances of all focal elements are summed. A distance matrix and weights among evidences are generated through the distance matrix, and the original evidences are corrected according to the weights. The evidence combination is completed with Dempster-Shafer(D-S) and Proportional Conflict Redistribution 6 (PCR 6). The example analysis shows that the proposed method agrees with the methods in Ref. ［33］ in the representation change of conflict degree, which shows the effectiveness of the proposed method. In the combination of multiple conflict evidences, the Bel of the proposed method is significantly improved compared with the existing methods. Key

Key words: conflictevidence, evidencetheory, intervaldistance, combinationmethod, weighting

CLC Number:

TP212.9

WU Bin, YI Xiao, LI Shuangming. Conflict Evidence Combination Method Based on Interval Distance[J]. Acta Armamentarii, 2020, 41(6): 1140-1150.

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第41卷第6期2020 年6月
兵工学报ACTA ARMAMENTARII
Vol.41No.6Jun.2020

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[2]	XU Ting-xue, LI Zhi-qiang, GU Jun-yuan, CONG Lin-hu, AN Jin, ZHAO Jian-zhong. Missile Condition Assessment Based on Multi-state Bayesian Network [J]. Acta Armamentarii, 2018, 39(2): 391-398.
[3]	MIAO Cheng-lin, LI Tong, LYU Jun， LI Hao. Cooperative Spectrum Sensing Algorithm against Spectrum Sensing Data Falsification Based on Dempster-Shafer Evidence Theory [J]. Acta Armamentarii, 2017, 38(12): 2406-2413.