scholarly journals Multisensor Fusion Method Based on the Belief Entropy and DS Evidence Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Yuanyuan Ju ◽  
Jiankang Bao ◽  
Wenhong Lyu
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Case Analysis ◽  
Entire Body ◽  
Combination Rule ◽  
Target Concentration ◽  
Conflicting Evidence ◽  
Multisensor Information Fusion ◽  
Belief Entropy ◽  
Weighted Factor

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.

scholarly journals An Extended Base Belief Function in Dempster–Shafer Evidence Theory and Its Application in Conflict Data Fusion

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2137
Author(s):  
Dingyi Gan ◽  
Bin Yang ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Data Management ◽  
Information Fusion ◽  
Evidence Theory ◽  
Belief Function ◽  
Mass Function ◽  
Combination Rule ◽  
Basic Probability ◽  
Belief Entropy ◽  
Management Method

The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time. In order to solve this problem, the base belief function is proposed to modify the basic probability assignment (BPA) in the exhaustive frame of discernment (FOD). However, in the non-exhaustive FOD, the mass function value of the empty set is nonzero, which makes the base belief function no longer applicable. In this paper, considering the influence of the size of the FOD and the mass function value of the empty set, a new belief function named the extended base belief function (EBBF) is proposed. This method can modify the BPA in the non-exhaustive FOD and obtain intuitive fusion results by taking into account the characteristics of the non-exhaustive FOD. In addition, the EBBF can degenerate into the base belief function in the exhaustive FOD. At the same time, by calculating the belief entropy of the modified BPA, we find that the value of belief entropy is higher than before. Belief entropy is used to measure the uncertainty of information, which can show the conflict more intuitively. The increase of the value of entropy belief is the consequence of conflict. This paper also designs an improved conflict data management method based on the EBBF to verify the rationality and effectiveness of the proposed method.

Conflicting evidence combination based on Belief Mover’s Distance

2021 ◽  
pp. 1-17
Author(s):  
Shenshen Bai ◽  
Longjie Li ◽  
Xiaoyun Chen
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Rule ◽  
The Earth ◽  
Conflicting Evidence ◽  
Evidence Combination ◽  
Final Weight ◽  
The Difference ◽  
New Evidence

The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods.

scholarly journals Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 993 ◽  
Author(s):  
Bin Yang ◽  
Dingyi Gan ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Incomplete Information ◽  
Information Fusion ◽  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Open World ◽  
Belief Entropy ◽  
Open World Assumption ◽  
Deng Entropy

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

scholarly journals Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

2019 ◽  
Vol 14 (3) ◽  
pp. 329-343 ◽  
Author(s):  
Yukun Dong ◽  
Jiantao Zhang ◽  
Zhen Li ◽  
Yong Hu ◽  
Yong Deng
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Distance Function ◽  
Evidence Theory ◽  
Sensor Data ◽  
Combination Rule ◽  
Sensor Data Fusion ◽  
Real Application ◽  
Belief Entropy

Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

scholarly journals A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 495 ◽  
Author(s):  
Ying Zhou ◽  
Yongchuan Tang ◽  
Xiaozhe Zhao
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Uncertainty Management ◽  
Management Approach ◽  
Uncertain Information ◽  
Improved Method ◽  
Situation Assessment ◽  
Air Combat ◽  
Belief Entropy ◽  
Dempster’S Rule Of Combination

Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–Shafer evidence theory (DST) framework. A better fusion result regarding the prediction of military intention can be helpful for decision-making in an air combat situation. To obtain a more accurate fusion result of situation assessment, an improved belief entropy (IBE) is applied to preprocess the uncertainty of situation assessment information. Data fusion of assessment information after preprocessing will be based on the classical Dempster’s rule of combination. The illustrative example result validates the rationality and the effectiveness of the proposed method.

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.

scholarly journals A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yifan Liu ◽  
Tiantian Bao ◽  
Huiyun Sang ◽  
Zhaokun Wei
Keyword(s):  
Data Fusion ◽  
Conflict Management ◽  
Evidence Theory ◽  
External Support ◽  
Divergence Measure ◽  
Fusion Method ◽  
Combination Rule ◽  
Special Cases ◽  
Novel Method ◽  
Belief Entropy

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.

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.

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