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.
Highlights
Advances in technology have led to difficulties with the use of a single sensor to meet the requirements of information diversification
Information fusion technology plays a significant role in practical applications of intelligent multisensor systems [4,5,6,7]
Multisensor information fusion technology [8] deals with the independent observation data that are obtained from multiple sensors by selecting the appropriate information-processing algorithm
Summary
Advances in technology have led to difficulties with the use of a single sensor to meet the requirements of information diversification. An 8-step algorithm had been developed which can eliminate the inherent paradoxes of a classical DS theory These methods can solve the problem of illogical results of evidence fusion to some extent and affirmed the validity of the Jousselme distance function and the information entropy function. The main contributions of this paper can be summarized as follows: First, the proposed method improves the accuracy of evidence fusion by considering both the evidence distance function and the information volume. The support weighted factor of the evidence is determined by making use of the Jousselme distance function Both the credibility weighted factor and the support weighted factor are integrated to form the final weight to adjust the bodies of evidence before applying the DS combination rule.
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