We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory.
- Authors
Fan, Xiaojing; Guo, Yinjing; Ju, Yuanyuan; Bao, Jiankang; Lyu, Wenhong
- Abstract
The Dempster–Shafer evidence theory has been widely applied in multisensor information fusion. Nevertheless, illogical results may occur when fusing highly conflicting evidence. To solve this problem, a new method of the grouping of evidence is proposed in this paper. This method uses a combination of the belief entropy and the degree of conflict of the evidence as the judgment rule and divides the entire body of evidence into two separate groups. For the grouped evidence, both the credibility weighted factor based on the belief entropy function and the support weighted factor based on the Jousselme distance function are taken into consideration. The two determined weighted factors are integrated to adjust the evidence before applying the DS combination rule. Numerical examples are provided to demonstrate the theoretical feasibility and rationality of the proposed method. The fusion results indicate that the proposed method is more accurate than the compared algorithms in handling the paradoxes. A decision-making case analysis of the biological system is performed to validate the practical applicability of the proposed method. The results confirm that the proposed method has the highest belief degree of the target concentration (50.98%) and has superior accuracy compared to other related methods.
- Subjects
DEMPSTER-Shafer theory; DATA fusion (Statistics); MULTISENSOR data fusion; BIOLOGICAL systems; ENTROPY (Information theory)
- Publication
Journal of Sensors, 2020, p1
- ISSN
1687-725X
- Publication type
Article
- DOI
10.1155/2020/7917512