A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

A new fusion approach based on distance of evidences

Based on the framework of evidence theory, data fusion aims at obtaining a single Basic Probability Assignment (BPA) function by combining several belief functions from distinct information sources. Dempster’s rule of combination is the most popular rule of combinations, but it is a poor solution for the management of the conflict between various information sources at the normalization step. Even when it faces high conflict information, the classical Dempster-Shafer’s (D-S) evidence theory can involve counter-intuitive results. This paper presents a modified averaging method to combine conflicting evidence based on the distance of evidences; and also gives the weighted average of the evidence in the system. Numerical examples showed that the proposed method can realize the modification ideas and also will provide reasonable results with good convergence efficiency.

New weighting factors assignment of evidence theorybased one vidence distance

Evidence theory has been widely used in the information fusion for its effectiveness of the uncertainty reasoning. However, the classical DS evidence theory involves counter-intuitive behaviors when the high conflict information exists. Based on the analysis of some modified methods, Assigning the weighting factors according to the intrinsic characteristics of the existing evidence sources is proposed, which is determined on the evidence distance theory. From the numerical examples, the proposed method provides a reasonable result with good convergence efficiency. In addition, the new rule retrieves to the Yager's formula when all the evidence sources contradict to each other completely.

Modification of evidence theory based on feature extraction

Although evidence theory has been widely used in information fusion due to its effectiveness of uncertainty reasoning, the classical DS evidence theory involves counter-intuitive behaviors when high conflict information exists. Many modification methods have been developed which can be classified into the following two kinds of ideas, either modifying the combination rules or modifying the evidence sources. In order to make the modification more reasonable and more effective, this paper gives a thorough analysis of some typical existing modification methods firstly, and then extracts the intrinsic feature of the evidence sources by using evidence distance theory. Based on the extracted features, two modified plans of evidence theory according to the corresponding modification ideas have been proposed. The results of numerical examples prove the good performance of the plans when combining evidence sources with high conflict information.

A novel approximation of basic probability assignment based on rank-level fusion

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment （BPA） approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.