A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

Wang, Jiayi; Yu, Qiuze

doi:10.1007/s10489-020-01739-8

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…The data used in the practical applications comes from some related work in the domain 66‐68 . Multisensor data fusion is a technology with practical significance and is widely used in many important fields 5,69,70 . The applications of target recognition and fault diagnosis in this section are also based on multisensor data fusion technology.…”

Section: Applicationmentioning

confidence: 99%

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Basic probability assignment to probability distribution function based on the Shapley value approach

Huang

Kang

2021

Int J Intell Syst

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In Dempster–Shafer evidence theory, how to use the basic probability assignment (BPA) in decision‐making is a significant issue. The transformation of BPA into a probability distribution function is one of the common and feasible schemes. To overcome the problems of the existing methods, we propose a marginal probability transformation method based on the Shapley value approach. The proposed method allocates BPA values in terms of how much an element contributes to a set, which is an equitable and effective distribution mechanism. Furthermore, we use probabilistic information content to evaluate the effect of each transformation method. Moreover, some numerical examples are used to demonstrate the efficiency and feasibility of the proposed method. Further, two applications, target recognition, fault diagnosis are used to verify the superiority and effectiveness of the proposed method in practice.

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Section: Applicationmentioning

confidence: 99%

“…[66][67][68] Multisensor data fusion is a technology with practical significance and is widely used in many important fields. 5,69,70 The applications of target recognition and fault diagnosis in this section are also based on multisensor data fusion technology. The process of multisensor data fusion can be shown in detail in Figure 7.…”

Section: Applicationmentioning

confidence: 99%

Basic probability assignment to probability distribution function based on the Shapley value approach

Huang

Kang

2021

Int J Intell Syst

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“…Fusion of the decisions: A final decision (a target class or tag) is made after combining the results (considered intermediate) from several classifiers or decision processes [26]. This type of fusion can be seen in the dynamic multi-sensor data fusion approach based on evidence theory and weighted ordered weighted averaging operator [27], Vot, and MVS.…”

Section: Multi-sensor Fusionmentioning

confidence: 99%

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains

Molino-Minero-Re

Aguileta

Brena

et al. 2021

Sensors

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Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.

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“…The Dempster‐Shafer theory (DST) can flexibly handle information with incompleteness and uncertainty, and it is widely applied in many fields, such as data fusion [ 1 , 2 ] , risk evaluation [ 3 ] , security prediction [ 4 ] , multi‐attribute decision making [ 5 ] , clustering [ 6 ] , reliability analysis [ 7 ] , fault diagnosis [ 8 ] , and so on. At present, the existing literature on DST mainly focuses on two aspects.…”

Section: Introductionmentioning

confidence: 99%

Combination for Conflicting Interval‐Valued Belief Structures with CSUI‐DST Method

LI¹,

Guan²,

Xiao³

et al. 2022

Chinese J of Electronics

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Since the basic probability of an interval‐valued belief structure (IBS) is assigned as interval number, its combination becomes difficult. Especially, when dealing with highly conflicting IBSs, most of the existing combination methods may cause counter‐intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose the good property of associativity and commutativity in Dempster‐Shafer theory (DST). To address these problems, a novel conflicting IBSs combination method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)‐DST method is proposed by introducing a similarity measurement to measure the degree of conflict among IBSs, and an uncertainty measurement to measure the degree of discord, non‐specificity and fuzziness of IBSs. Considering these two measures at the same time, the weight of each IBS is determined according to the modified reliability degree. From the perspective of intuitionistic fuzzy sets, we propose the weighted average IBSs combination rule by the addition and number multiplication operators. The effectiveness and rationality of this combination method are validated with two numerical examples and its application in target recognition.

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A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

Cited by 11 publications

References 52 publications

Basic probability assignment to probability distribution function based on the Shapley value approach

Basic probability assignment to probability distribution function based on the Shapley value approach

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains

Combination for Conflicting Interval‐Valued Belief Structures with CSUI‐DST Method

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