A novel combination rule for conflict management in data fusion
被引:12
作者:
Chen, Xingyuan
论文数: 0引用数: 0
h-index: 0
机构:
Kunming Univ, Sch Informat Engineer, Kunming, Peoples R China
Key Lab Data Governance & Intelligent Decis Univ Y, Kunming, Peoples R ChinaKunming Univ, Sch Informat Engineer, Kunming, Peoples R China
Chen, Xingyuan
[1
,2
]
Deng, Yong
论文数: 0引用数: 0
h-index: 0
机构:
Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu, Peoples R ChinaKunming Univ, Sch Informat Engineer, Kunming, Peoples R China
Deng, Yong
[3
]
机构:
[1] Kunming Univ, Sch Informat Engineer, Kunming, Peoples R China
[2] Key Lab Data Governance & Intelligent Decis Univ Y, Kunming, Peoples R China
[3] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu, Peoples R China
How to handle conflict in Dempster-Shafer evidence theory is an open issue. Many approaches have been proposed to solve this problem. The existing approaches can be divided into two kinds. The first is to improve the combination rule, and the second is to modify the data model. A typical method to improve combination rule is to assign the conflict to the total ignorance set T. However, it does not make full use of conflict information. A novel combination rule is proposed in this paper, which assigns the conflicting mass to the power set (ACTP). Compared with modifying data model, the advantage of the proposed method is the sequential fusion, which greatly decrease computational complexity. To demonstrate the efficacy of the proposed method, some numerical examples are given. Due to the less information loss, the proposed method is better than other methods in terms of identifying the correct evidence, the speed of convergence and computational complexity.