The Application of D-S Improving Algorithm in Data Fusion

2012 ◽  
Vol 482-484 ◽  
pp. 684-687
Author(s):  
Zhi Gang Ma
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
Improved Method ◽  
Important Data ◽  
Fusion Systems ◽  
Evidence Combination ◽  
Fusion Methods

As one of the most important data fusion methods used for dealing with uncertainty problems, the D-S evidence reasoning has been applied to lots of data fusion systems. In this paper, the evidence combination principles of D-S evidence theory are represented in detail firstly. In view of the deficiency of D-S method, one of improved evidence combination method is introduced. A given practical example demonstrates that the improved method can be applied to dominating multi evidences with great confliction, and the performance and reliability of fusion can also be improved.

scholarly journals An Improved Method for Multisensor High Conflict Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Like Wang ◽  
Yu Bao
Keyword(s):  
Data Fusion ◽  
Imperfect Information ◽  
Target Identification ◽  
Evidence Theory ◽  
Combination Method ◽  
Identification System ◽  
Improved Method ◽  
High Conflict ◽  
Belief Entropy ◽  
Deng Entropy

Dempster-Shafer evidence theory can effectively process imperfect information and is widely used in a data fusion system. However, classical Dempster-Shafer evidence theory involves counter-intuitive behaviors with the data of multisensor high conflict in target identification system. In order to solve this problem, an improved evidence combination method is proposed in this paper. By calculating the support degree and the belief entropy of each sensor, the proposed method combines conflict evidences. A new method is used to calculate support degree in this paper. At the same time, inspired by Deng entropy, the modified belief entropy is proposed by considering the scale of the frame of discernment (FOD) and the relative scale of the intersection between evidences with respect to FOD. Because of these two modifications, the effect has been improved in conflict data fusion. Several methods are compared and analyzed through examples. And the result suggests the proposed method can not only obtain reasonable and correct results but also have the highest fusion reliability in solving the problem of high conflict data fusion.

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.

Wharf Risk Assessment Based on AHP&D-S Evidence Theory

2013 ◽  
Vol 779-780 ◽  
pp. 769-773
Author(s):  
Ying He ◽  
Zhan Li Jiao ◽  
Fu Cai Jiang
Keyword(s):  
Risk Assessment ◽  
Data Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
New Combination ◽  
Rating Data ◽  
Expert Rating ◽  
Good Expression ◽  
Core Idea ◽  
Expression Of Uncertainty

AHP is a method broadly applied in Risk Assessment. However this method has some shortcomings in evaluations. The subjective qualitative analysis of risk events and impacts in the wharf risk assessment make the conclusion with vagueness, which reduces the credibility of the result. The core idea of the Evidence Theory is the synthesis of evidence, which can be a good expression of "uncertainty" and "unknown", using the theory in the assessment can reveal the uncertainty effectively. In order to improve the accuracy and reliability of the assessment, this method combines AHP with D-S Evidence Theory, in which the D-S Evidence Theory applied to fuse and correct the expert rating data for determine-making of the wharf risk and AHP used to establish the system of assessment and determine the value. The Combination Rule for data fusion of multi- reliability can improve the reliability and make up for the lack of AHP. Through correction and data fusion by Synthetic Formula, the high support reviews get higher credibility, the low support reviews get lower credibility after synthesis, the polarization phenomenon lets the result more realistic and accurate. Taking a wharf project for example to prove the new combination method is more applicable to the wharf risk assessment.

scholarly journals A Multisource Situation Information Fusion Method Based on Dynamic Evidence Combination

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Liu ◽  
ChaoWen Chang ◽  
Yuchen Zhang ◽  
Yongwei Wang
Keyword(s):  
Information Fusion ◽  
Detection Rate ◽  
Evidence Theory ◽  
Fusion Method ◽  
False Detection Rate ◽  
False Detection ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Fusion Methods ◽  
Fusion Efficiency

To address the problems of fusion efficiency, detection rate (DR), and false detection rate (FDR) that are associated with existing information fusion methods, a multisource information fusion method featuring dynamic evidence combination based on layer clustering and improved evidence theory is proposed in this study. First, the original alerts are hierarchically clustered and conflicting evidence is eliminated. Then, dynamic evidence combination is applied to fuse the condensed alerts, thereby improving the efficiency and accuracy of the fusion. The experimental results show that the proposed method is superior to current fusion methods in terms of fusion efficiency, DR, and FDR.

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.

scholarly journals Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 801 ◽  
Author(s):  
Shuang Ni ◽  
Yan Lei ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Belief Function ◽  
Combination Rule ◽  
Improved Method ◽  
Data Set ◽  
Numerical Examples ◽  
Initial Belief ◽  
Belief Entropy ◽  
Fusion Approach

Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D–S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

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