Multisource basic probability assignment fusion based on information quality

Li, Dingbin; Deng, Yong; Cheong, Kang Hao

doi:10.1002/int.22363

Cited by 52 publications

(18 citation statements)

References 60 publications

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“…Yager 36 proposed that the information quality is used to measure probability distribution certainty. For any probability distribution

p_{i} (p_{i 1}, p_{i 2}, …, p_{i n})

, whose information quality is calculated as follows 37 :

false∥ p_{i} ∥^{2} = \sum_{j = 1}^{n} {p_{i j}}^{2}

where

false∥ p_{i} false∥ = \sqrt{MathClass-open(p_{i} * p_{i} MathClass-close)} = {MathClass-opentrue({true \sum}_{j = 1}^{n} {p_{i j}}^{2} MathClass-closetrue)}^{\frac{1}{2}}

…”

Section: Preliminariesmentioning

confidence: 99%

A new correlation coefficient of BPA based on generalized information quality

Liang

Cai

2021

Int J of Intelligent Sys

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Dempster rule is commonly used to combine evidence from different sensors because of its excellent mathematical properties (commutativity and associativity). However, the conflict coefficient k of this rule cannot be reasonably and effectively express the conflict between evidence, which makes the Dempster rule greatly questioned. To describe in the relationship between evidence (conflict or correlation) more accurately, an evidence correlation coefficient based on generalized information quality is proposed. First of all, according to Deng entropy to measure the uncertainty of each evidence, and combined with the information quality of Yager, new generalized information quality is proposed, which performs well in measuring the basic probability assignment's certainty. Second, the evidence itself is modified by generalized information quality, and the evidence correlation coefficient is calculated based on the Pearson coefficient formula. A new measurement method of evidence conflict based on evidence correlation coefficient is proposed. Finally, combined with the evidence correlation coefficient and DEMATEL model, the evidence is discounted and combined. Numerous examples are used to analyze and compare the evidence conflict coefficient and the evidence combination results. The experimental results demonstrate that, compared with other evidence conflict measurement methods, the evidence conflict coefficient calculated by this method can reflect the difference between evidence more effectively. The result of evidence combination is more reasonable and accurate.

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“…Yager 36 proposed that the information quality is used to measure probability distribution certainty. For any probability distribution

p_{i} (p_{i 1}, p_{i 2}, …, p_{i n})

, whose information quality is calculated as follows 37 :

false∥ p_{i} ∥^{2} = \sum_{j = 1}^{n} {p_{i j}}^{2}

where

false∥ p_{i} false∥ = \sqrt{MathClass-open(p_{i} * p_{i} MathClass-close)} = {MathClass-opentrue({true \sum}_{j = 1}^{n} {p_{i j}}^{2} MathClass-closetrue)}^{\frac{1}{2}}

…”

Section: Preliminariesmentioning

confidence: 99%

A new correlation coefficient of BPA based on generalized information quality

Liang

Cai

2021

Int J of Intelligent Sys

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“…The treatment of uncertainty problems has promoted the birth of Dempster-Shafer evidence theory. And Dempster-Shafer evidence theory [38,39,40,41,42] has become a useful tool for researchers to measure uncertainty because of its weaker constraints than Bayesian probability theory [20]. Therefore, this section will introduce several important definitions.…”

Section: A Evidence Theorymentioning

confidence: 99%

Uncertainty Measure of Basic Probability Assignment Based on Renyi Entropy and Its Application in Decision-Making

Chen

Luo

2021

IEEE Access

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Since the Dempster-Shafer evidence theory was developed, it has been extensively concerned by researchers. Compared with Bayesian probability theory, Dempster-Shafer evidence theory satisfies weaker constraints and has the advantage to indicate uncertainty, so it is widely used in the information system. However, how to measure the uncertainty of basic probability assignment in Dempster-Shafer evidence theory is still a problem worthy of attention. Therefore, based on Renyi entropy, this paper proposes a novel method to measure the uncertainty of basic probability assignment in Dempster-Shafer evidence theory. In addition, after proving that this method is compatible with Shannon entropy, a large number of comparative experiments are carried out to illustrate its effectiveness. Finally, through the application in decision-making, it is proved that the combination rule considering uncertainty can produce more reasonable results.

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“…As a result, the obtained information may contain mistakes, which makes it difficult to make accurate decisions [ 3 ]. In contrast, fusing multi-sensor information can improve the performance of system and make the results more reliable [ 4 , 5 ]. Due to the advantages of multi-sensor setups, in recent years, it has been widely used in fault diagnosis, target positioning, and UAV system control [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Huang

Wang

Deng

2021

Entropy

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Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.

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Multisource basic probability assignment fusion based on information quality

Abstract: Information quality has received extensive attention recently. Yager and Petry proposed an information

Cited by 52 publications

References 60 publications

A new correlation coefficient of BPA based on generalized information quality

A new correlation coefficient of BPA based on generalized information quality

Uncertainty Measure of Basic Probability Assignment Based on Renyi Entropy and Its Application in Decision-Making

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

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