Modified Evidence Combination Method Based on Pignistic Probability Distance and Uncertainty Degree

2017 ◽  
Author(s):  
Dawei Xue ◽  
Yong Wang
Keyword(s):  
Combination Method ◽  
Evidence Combination ◽  
Probability Distance

scholarly journals A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 526
Author(s):  
Jian Wang ◽  
Jing-wei Zhu ◽  
Yafei Song
Keyword(s):  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Multiple Sources ◽  
Adaptive Combination ◽  
Conflicting Evidence ◽  
Different Types ◽  
Fusion Methods ◽  
Temporal And Spatial ◽  
Probability Distance

Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflicting evidence sets are fused using Dempster’s rule, while conflicting evidence sets are fused using a weighted average combination method based on the power pignistic probability distance. The superior evidence fusion performance of the proposed method is demonstrated by comparisons with the performances of seven other fusion methods based on numerical examples with four different evidence conflict scenarios. The results show that the method proposed in this paper not only can properly fuse different types of evidence, but also provides an excellent focus on the components of evidence sets with high confidence, which is conducive to timely and accurate decisions.

scholarly journals Research on the Combination Method of Temporal Conflict Evidence Based on Transition Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Wu ◽  
Xiao Yi
Keyword(s):  
Transition Matrix ◽  
Transition Probability ◽  
Evidence Theory ◽  
Research Topic ◽  
Combination Method ◽  
Evidence Based ◽  
Important Research ◽  
Transition Matrices ◽  
Important Research Topic ◽  
Evidence Combination

Conflict evidence combination is an important research topic in evidence theory. In this paper, two kinds of transition matrices are constructed based on the Markov model; one is the unordered transition matrix, which satisfies the commutative law, and the other is the temporal transition matrix, which does not satisfy the commutative law, but it can handle the combination of temporal evidence well. Then, a temporal conflict evidence combination model is proposed based on these two transition matrices. First, the transition probability at the first n time is calculated through the model of unordered transition probability, and then, the transition matrix from the N + 1 time is used to solve the combination problem of temporal conflict evidence. The effectiveness of the transition matrix in the research of conflict evidence combination method is proved by the example analysis.

scholarly journals A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

2020 ◽  
Author(s):  
Lifan Sun ◽  
Yuting Chang ◽  
Jiexin Pu ◽  
Haofang Yu ◽  
Zhe Yang
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
Uncertain Information ◽  
Combination Rule ◽  
Simulation Experiments ◽  
Sensor Information ◽  
Bodies Of Evidence ◽  
Probability Distance ◽  
Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

A Method of Fast Weighted Evidence Combination Exhibited by Compound Conflict

2014 ◽  
Vol 536-537 ◽  
pp. 443-449
Author(s):  
Dong Ying Bai ◽  
Jun Han ◽  
Jian Wang ◽  
Song Li
Keyword(s):  
Exponential Growth ◽  
Large Scale ◽  
Evidence Theory ◽  
Combination Method ◽  
Evidence Combination ◽  
Center Distance ◽  
Computing Speed

Aiming at the paradox of D-S evidence theory and computations exponential growth in dealing with large scale conflict evidence combination, a new weighted evidence combination method was proposed, which used conflict coefficient and evidence distance in order to measure the conflict. Through the analysis of single conflict representations weaknesses, compound conflict coefficient has been put forward, meanwhile, the evidence center and current center distance were defined, evidence weight was determined with current center distance and conflict coefficient. The experiment results show that the algorithm settles the paradox effectively, at the same time, computing speed has been greatly enhanced.

An improved conflicting evidence combination approach based on a new supporting probability distance

2015 ◽  
Vol 42 (12) ◽  
pp. 5139-5149 ◽  
Author(s):  
Cholsok Yu ◽  
Jianhong Yang ◽  
Debin Yang ◽  
Xianghong Ma ◽  
Hyonchun Min
Keyword(s):  
Conflicting Evidence ◽  
Evidence Combination ◽  
Combination Approach ◽  
Probability Distance

A Weighted Evidence Combination Method Based on Improved Conflict Measure Factor

2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Xiaochen Xing ◽  
Yuanwen Cai ◽  
Zhengyu Zhao ◽  
Long Cheng ◽  
Yan Li
Keyword(s):  
Combination Method ◽  
Evidence Combination
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