A method for combining conflicting evidences with improved distance function and Tsallis entropy

Li, Hanwen; Xiao, Fuyuan

doi:10.1002/int.22273

Cited by 25 publications

(19 citation statements)

References 74 publications

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“…However, the results generated by other methods all allocate the larger values to the target θ 1 , which is consistent with intuitive judgement. In addition, the accuracy of recognition by the proposed method is approximately 98.96%, which is higher than those by currently popular methods, such as the Murphy method [26], the Lin et al method [72], the Deng et al method [27], the Yan et al method [31], the Jiang et al method [73], the Li et al method [38], the Yuan et al method [37], and the Xiao method [1].…”

Section: Comparison and Discussionmentioning

confidence: 75%

“…It is believed that BOEs with various importance will have different effects on the final fusion result. Hence, it is an effective tool to modify the initial BOEs based on their weight to further resolve the counterintuitive problems in D-S evidence theory [38]. Recently, belief entropy has been widely applied to determine the evidence weight [60].…”

Section: Determination Of the Boe'smentioning

confidence: 99%

“…Based on the aforementioned works, the initial BOEs can usually be preprocessed in consideration of internal indeterminacy and external support, which are quite distinct notions and measurements in D-S evidence theory [30]. As for internal indeterminacy, belief entropy has been widely studied and applied to measure information volume and evidence uncertainty [37,38]. Hence, evidence weight with larger information volume will be greater since its indeterminacy and uncertainty should be lower based on belief entropy [39].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Bao

Sang

et al. 2021

Mathematical Problems in Engineering

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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.

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Section: Comparison and Discussionmentioning

confidence: 75%

Section: Determination Of the Boe'smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

Bao

Sang

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…Comparatively, the proposed method and Wang et al’s 29 method similarly achieve the highest belief degree of the correct target. According to the time complexity calculation method presented in Li and Xiao, 39 the proposed method calculates the divergence value between two evidences for k ( k − 1 ) times. Since Bet P m ( A i ) = ∑ A i ⊆ B m ( B ) / | B | ( i = 1 , 2 , … , n ) , we need to process n × 2 n − 1 elements to obtain PPT ( m 1 , m 2 ) .…”

Section: Discussionmentioning

confidence: 99%

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion

Hou

Deng

et al. 2021

International Journal of Distributed Sensor Networks

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Dempster–Shafer (D–S) evidence theory is more and more extensively applied in multi-sensor data fusion. However, it is still an open issue that how to effectively combine highly conflicting evidence in D–S evidence theory. In this article, a novel divergence measure, called pignistic probability transformation divergence, is proposed to measure the difference between evidences. The proposed pignistic probability transformation divergence can reflect the interaction between single-element and multi-element subsets by introducing the pignistic probability transformation, and satisfies the properties of boundedness, non-degeneracy, and symmetry. Moreover, the pignistic probability transformation divergence can degenerate as Jensen–Shannon divergence when mass function and the probability distribution are consistent. Based on the pignistic probability transformation divergence, a new multi-sensor data fusion method is presented. The proposed method takes advantage of pignistic probability transformation divergence to measure the discrepancy between evidences in order to obtain the credibility weights, and belief entropy to measure the uncertainty of the evidences in order to obtain the information volume weights, which can fully mine the potential information between evidences. Then, the credibility weights and the information volume weights are integrated to generate an appropriate weighted average evidence before using Dempster’s combination rule. The results of two application cases illustrate that the proposed method outperforms other related methods for combining highly conflicting evidences.

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“…It is widely used in fault diagnosis [11][12][13][14], decision-making [15,16], risk assessment [17], and so on. Many studies in recent years have focused on conflict resolution [18][19][20], evidence revision [21], combination rules [22][23][24][25], and information volume [26,27]. Many methods about uncertainty quantification have also been proposed [28].…”

Section: Introductionmentioning

confidence: 99%

A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

Huang

Deng

Jiang

2021

Entropy

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The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.

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A method for combining conflicting evidences with improved distance function and Tsallis entropy

Cited by 25 publications

References 74 publications

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

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

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion

A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

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