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 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 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.

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 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 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.

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 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.

scholarly journals An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Chen ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Mass Function ◽  
Sensor Data ◽  
Artificial Data ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Fusion Applications ◽  
Basic Probability ◽  
Belief Entropy

Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.

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