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Journal articles on the topic 'Dempster’s fusion rule'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Jong, Myongnam, Yunhyok Paek, Hyonil Kim, and Cholsok Yu. "Analyzing the degree of conflict between bodies of evidence based on a new distance in data fusion." Advances in Data Science and Adaptive Analysis 13, no. 01 (2021): 2150004. http://dx.doi.org/10.1142/s2424922x21500042.

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Dempster’s combination rule may produce some unreasonable results when dealing with a combination of the conflicting evidence in evidence theory of Dempster–Shafer. Therefore, analyzing the degree of conflict between the bodies of evidence is essential to evaluate the applicability of Dempster’s rule. A new probability function, which is called a supporting probability function, is proposed to describe the correlation between evidences, and its distance is proposed to measure the distance between bodies of evidence. Combining this distance with classical conflict coefficient, a new method of e

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2

Bai, Shenshen, Longjie Li, and Xiaoyun Chen. "Conflicting evidence combination based on Belief Mover’s Distance." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 2005–21. http://dx.doi.org/10.3233/jifs-211397.

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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. Subsequen

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Zhou, Ying, Yongchuan Tang, and Xiaozhe Zhao. "A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy." Entropy 21, no. 5 (2019): 495. http://dx.doi.org/10.3390/e21050495.

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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 fus

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Tang, Yongchuan, Deyun Zhou, Zichang He, and Shuai Xu. "An improved belief entropy–based uncertainty management approach for sensor data fusion." International Journal of Distributed Sensor Networks 13, no. 7 (2017): 155014771771849. http://dx.doi.org/10.1177/1550147717718497.

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In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertaint

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Fan, Wentao, and Fuyuan Xiao. "A New Conflict Management in Evidence Theory Based on DEMATEL Method." Journal of Sensors 2019 (November 19, 2019): 1–12. http://dx.doi.org/10.1155/2019/7145373.

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D-S evidence theory is widely used in data fusion. However, the result of Dempster’s combination rule is not efficient and in highly conflicting situation. Though the existing methods have been proved efficient to deal with conflict in some applications, the indirect conflict among evidence is neglected to some degree. To solve this problem, a new method is proposed based on decision-making trial and evaluation laboratory (DEMATEL) and the belief correlation coefficient in this paper. The application in target recognition illustrates the efficiency of the proposed method. Compared with Dempste

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Klein, Lawrence A., Ping Yi, and Hualiang Teng. "Decision Support System for Advanced Traffic Management Through Data Fusion." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (2002): 173–78. http://dx.doi.org/10.3141/1804-23.

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The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule

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Zhu, WenBo, Huicheng Yang, Yi Jin, and Bingyou Liu. "A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6191035.

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This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the comb

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Elkosantini, Sabeur, and Ahmed Frikha. "Decision fusion for signalized intersection control." Kybernetes 44, no. 1 (2015): 57–76. http://dx.doi.org/10.1108/k-08-2013-0185.

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Purpose – Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system

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9

Zhao, Yuxin, Hancong Feng, Kaili Jiang, and Bin Tang. "Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers." Remote Sensing 15, no. 15 (2023): 3743. http://dx.doi.org/10.3390/rs15153743.

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The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combinati

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10

Deng, Zhan, and Jianyu Wang. "A Novel Evidence Conflict Measurement for Multi-Sensor Data Fusion Based on the Evidence Distance and Evidence Angle." Sensors 20, no. 2 (2020): 381. http://dx.doi.org/10.3390/s20020381.

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As an important method for uncertainty modeling, Dempster–Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster’s combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance. However, it is inaccurate to characterize the evidence conflict only through the evidence distance. To address this issue, we comprehensively consider

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11

Gao, Xiue, Panling Jiang, Wenxue Xie, Yufeng Chen, Shengbin Zhou, and Bo Chen. "Decision fusion method for fault diagnosis based on closeness and Dempster-Shafer theory." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 12185–94. http://dx.doi.org/10.3233/jifs-210283.

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Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its h

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12

Cheng, Jian Xing, and Yi Kai Shi. "The Fault Diagnosis of Aircraft Power System Based on D-S Evidence Theory." Applied Mechanics and Materials 318 (May 2013): 134–39. http://dx.doi.org/10.4028/www.scientific.net/amm.318.134.

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Dempster’s rule of combination is commonly used in the field of information fusion, Aiming at the problem of related fault of aircraft power system , D-S theory is used to fuse multiple fault alarm information to get the only fault type accurately. Mathematical model of aircraft power system fault diagnosis, and method of fusion was established by analyzing the fault phenomena and fault causes ,for 270 V high-voltage DC power system. The accuracy of the D-S Theory data fusion is better than single sensor judged fault by simulating and testing. A example is given to show that this fusion method

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13

Yi, Ping, and Songling Zhang. "Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (2017): 32–37. http://dx.doi.org/10.3141/2645-04.

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This paper introduces applications of the Dempster–Shafer (D-S) data fusion technique in transportation system decision making. D-S inference is a statistics-based data classification technique, and it can be used when data sources contribute discontinuous and incomplete information and no single data source can produce an overwhelmingly high probability of certainty for identifying the most probable event. The technique captures and combines the information contributed by the data sources by using Dempster’s rule to find the conjunction of the events and to determine the highest associated pr

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14

Chen, Yutong, and Yongchuan Tang. "An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis." Mathematics 9, no. 11 (2021): 1292. http://dx.doi.org/10.3390/math9111292.

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The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the open world assumption has been discovered as a new type of uncertain information. The classical Dempster’s combination rules are difficult to solve the related problems of incomplete information under the open world assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the open world assumption. Therefore, this paper proposes a new method framework to process uncert

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15

Yager, Ronald R. "On the Fusion of Multiple Measure Based Belief Structures." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 2 (2018): 63–88. http://dx.doi.org/10.1142/s0218488518400123.

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We introduce the concept of a fuzzy measure and describe the process of combining fuzzy measures to form new measures. We discuss the role of fuzzy measures in modeling uncertain information and its use in modeling granular uncertain information with the aid of measure based belief structures. We turn to the problem of fusing multiple measure based belief structures. First we look at the case when the belief structures being fused have the same focal elements. Then we turn to case where the structures being fused have different focal elements. Finally we compare measure-based fusion with Demps

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Delavar, Mahmoud Reza, and Mansoureh Sadrykia. "Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 195. http://dx.doi.org/10.3390/ijgi9040195.

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Earthquake is one of the natural disasters which threaten many lives every year. It is impossible to prevent earthquakes from occurring; however, it is possible to predict the building damage, human and property losses in advance to mitigate the adverse effects of the catastrophe. Seismic vulnerability assessment is a complex uncertain spatial decision making problem due to intrinsic uncertainties such as lack of complete data, vagueness in experts’ comments and uncertainties in the numerical data/relations. It is important to identify and model the incorporated uncertainties of seismic vulner

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17

Huang, Fanghui, Yu Zhang, Ziqing Wang, and Xinyang Deng. "A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion." Entropy 23, no. 9 (2021): 1222. http://dx.doi.org/10.3390/e23091222.

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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

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18

Han, De Qiang, Chong Zhao Han, Yong Deng, and Yi Yang. "Classifier Fusion Based on Inner-Cluster Class Distribution." Applied Mechanics and Materials 44-47 (December 2010): 3220–24. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3220.

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Multiple classifier fusion is an effective way to improve the classification performance. In this paper, member classifiers are designed based on the training dataset’s different feature spaces. By utilizing ISODATA technique, various clustering results can be obtained in different feature spaces. For each member classifier (corresponding to one feature space), the given test sample is assigned to the cluster according to the distance between each cluster centroid and the test sample itself. The mass functions and the classification decision of each member classifier for the given test sample

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19

Ma, Mengmeng, and Jiyao An. "Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach." Journal of Sensors 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/509385.

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To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but al

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20

Chen, Yujie, Zexi Hua, Yongchuan Tang, and Baoxin Li. "Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy." Entropy 24, no. 8 (2022): 1164. http://dx.doi.org/10.3390/e24081164.

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Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribut

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21

Qin, Bowen, and Fuyuan Xiao. "An improved method to determine basic probability assignment with interval number and its application in classification." International Journal of Distributed Sensor Networks 15, no. 1 (2019): 155014771882052. http://dx.doi.org/10.1177/1550147718820524.

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Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on

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22

Li, Yuting, and Fuyuan Xiao. "Bayesian Update with Information Quality under the Framework of Evidence Theory." Entropy 21, no. 1 (2018): 5. http://dx.doi.org/10.3390/e21010005.

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Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster’s combination rule to obtain the fusion result. Finally, with the aid of pignist

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Xu, Shijun, Yi Hou, Xinpu Deng, Peibo Chen, Kewei Ouyang, and Ye Zhang. "A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion." International Journal of Distributed Sensor Networks 17, no. 7 (2021): 155014772110314. http://dx.doi.org/10.1177/15501477211031473.

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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 proper

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Wang, Jian, Jing-wei Zhu, and Yafei Song. "A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance." Symmetry 12, no. 4 (2020): 526. http://dx.doi.org/10.3390/sym12040526.

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Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflict

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25

Liu, Nikun, Zhenfeng Zhou, Lijun Zhu, Yixin He, and Fanghui Huang. "Fault Diagnosis of Unmanned Aerial Systems Using the Dempster–Shafer Evidence Theory." Actuators 13, no. 7 (2024): 264. http://dx.doi.org/10.3390/act13070264.

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Unmanned aerial systems (UASs) find diverse applications across military, civilian, and commercial sectors, including military reconnaissance, aerial photography, environmental monitoring, precision agriculture, logistics, and rescue operations, offering efficient, safe, and cost-effective solutions to various industries. To ensure the stable and reliable operation of UASs, fault diagnosis is essential, which can enhance safety, and minimize potential risks and losses. However, most existing fault diagnosis methods rely on a single physical quantity as the primary information source or solely

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Tan, Fu, Xiaolong Chen, Rui Chen, Ruijie Wang, Chi Huang, and Shimin Cai. "Identifying Influential Nodes Based on Evidence Theory in Complex Network." Entropy 27, no. 4 (2025): 406. https://doi.org/10.3390/e27040406.

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Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quan

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Xiang, Xinjian, Kehan Li, Bingqiang Huang, and Ying Cao. "A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory." Sensors 22, no. 15 (2022): 5902. http://dx.doi.org/10.3390/s22155902.

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The essential factors of information-aware systems are heterogeneous multi-sensory devices. Because of the ambiguity and contradicting nature of multi-sensor data, a data-fusion method based on the cloud model and improved evidence theory is proposed. To complete the conversion from quantitative to qualitative data, the cloud model is employed to construct the basic probability assignment (BPA) function of the evidence corresponding to each data source. To address the issue that traditional evidence theory produces results that do not correspond to the facts when fusing conflicting evidence, t

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Ma, Li, Wenlong Yao, Xinguan Dai, and Ronghao Jia. "A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion." Sensors 23, no. 2 (2023): 722. http://dx.doi.org/10.3390/s23020722.

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A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability funct

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Liu, Jingyu, and Yongchuan Tang. "Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance." Entropy 23, no. 7 (2021): 820. http://dx.doi.org/10.3390/e23070820.

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The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment

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Tang, Yongchuan, Shuaihong Wu, Ying Zhou, Yubo Huang, and Deyun Zhou. "A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion." Entropy 25, no. 3 (2023): 462. http://dx.doi.org/10.3390/e25030462.

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Dempster–Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. Many methods have been proposed to solve conflict evidence fusion, and it is still an open issue. This paper proposes a new reliability coefficient using betting commitment evidence distance in Dempster–Shafer evidence theory for conflict and uncertain information fusion. The single belief function fo

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Xiaoning Bo, Xiaoning Bo, Jin Wang Xiaoning Bo, Guoqin Li Jin Wang, Yanli Tan Guoqin Li, and Yi Sui Yanli Tan. "Data Fusion for Target Recognition Based on Evidence Theory in IOT Environment." 電腦學刊 32, no. 5 (2021): 258–71. http://dx.doi.org/10.53106/199115992021103205022.

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Data fusion using evidence theory in IOT applications has been used extensively to recoginze targets because it offers the advantage of handling uncertainty. But the traditional Dempster&rsquo;s combination rule cannot deal with highly conflicting information because it often generates counter-intuitive results. In this paper, a new weighted evidence combination approach is proposed to solve this problem. First, two measures, i.e., an uncertainty measure of each evidence and a probabilistic-based dissimilarity measure between two evidences, are introduced to estimate the value of weight of

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Dezert, Jean, and Albena Tchamova. "On the Validity of Dempster's Fusion Rule and its Interpretation as a Generalization of Bayesian Fusion Rule." International Journal of Intelligent Systems 29, no. 3 (2013): 223–52. http://dx.doi.org/10.1002/int.21638.

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Li, Fenglian, Xueying Zhang, Xiaolei Chen, and Yu-Chu Tian. "Adaptive and robust evidence theory with applications in prediction of floor water inrush in coal mine." Transactions of the Institute of Measurement and Control 39, no. 4 (2017): 483–93. http://dx.doi.org/10.1177/0142331216687816.

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The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sou

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Liu, Yifan, Tiantian Bao, Huiyun Sang, and Zhaokun Wei. "A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory." Mathematical Problems in Engineering 2021 (October 8, 2021): 1–15. http://dx.doi.org/10.1155/2021/6558843.

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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

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Chen, Yong, Yongchuan Tang, and Yan Lei. "An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment." Journal of Mathematics 2020 (November 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/1594967.

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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 n

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Wang, Zhi Song, Li Wei Tang, Wen Wen Yu, and Jin Hua Cao. "Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory." Applied Mechanics and Materials 241-244 (December 2012): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.288.

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Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.

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Su, Xiaoyan, Shuwen Shang, Leihui Xiong, Ziying Hong, and Jian Zhong. "Research on dependent evidence combination based on principal component analysis." Mathematical Biosciences and Engineering 21, no. 4 (2024): 4853–73. http://dx.doi.org/10.3934/mbe.2024214.

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<abstract><p>Dempster-Shafer evidence theory, as a generalization of probability theory, is a powerful tool for dealing with a variety of uncertainties, such as incompleteness, ambiguity, and conflict. Because of its advantages in information fusion compared with traditional probability theory, it is widely used in various fields. However, the classic Dempster's combination rule assumes that evidences are independent of each other, which is difficult to satisfy in real life. Ignoring the dependence among the evidences will lead to unreasonable fusion results, and even wrong conclus

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Li, Juan, and Hong Wei Guo. "Study on Defect Detection Based on D-S Evidential Reasoning in Natural Gas Pipeline." Advanced Materials Research 717 (July 2013): 315–19. http://dx.doi.org/10.4028/www.scientific.net/amr.717.315.

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Based reasoning data fusion method, Dempster-Shafer evidence reasoning applies to natural gas pipeline detecting experiments. Through the use of multiple sensors to sample the defect information in natural gas pipeline, extracting characteristic information, using neural network to carry on the fusion recognition, and then the neural networks for normalized output value as evidence, Dempster combination rule to fuse the data further and improve reliability, the final identification of the effective decision-making a type of pipeline defect. This experiment shows the D-S evidence reasoning has

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Khan, Md Nazmuzzaman, and Sohel Anwar. "Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion." Sensors 19, no. 21 (2019): 4810. http://dx.doi.org/10.3390/s19214810.

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Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a succ

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Ni, Shuang, Yan Lei, and Yongchuan Tang. "Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory." Entropy 22, no. 8 (2020): 801. http://dx.doi.org/10.3390/e22080801.

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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 f

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Wang, Zhe, and Fuyuan Xiao. "An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure." Entropy 21, no. 6 (2019): 611. http://dx.doi.org/10.3390/e21060611.

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Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information vol

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Sabre, Rachid, and Ias Wahyuni. "Laplacian Pyramid and Dempster-Shafer with Alpha Stable Distance in Multi-Focus Image Fusion." Signal & Image Processing : An International Journal 16, no. 1 (2025): 1–15. https://doi.org/10.5121/sipij.2025.16101.

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Multi-focal image fusion occupies a place in image processing research. It allows, from several images of the same scene with different blurred regions, to give a fused image without blur. This allows fusing photos taken by drones at different heights by zooming in each image a different object. Several methods are developed in the literature but which are made independently of the nature of the images. The aim of our work is to propose a method adapted essentially to images of significant fluctuations (of very large variance) considered as an alpha stable signal. For these images, we propose

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Pan, Guang, and Lin Li Wu. "Information Fusion Based on Improved D-S Evidence Theory." Applied Mechanics and Materials 411-414 (September 2013): 49–52. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.49.

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Dempster-Shafer evidence theory is an efficient method to process uncertain, incomplete and vague information in data fusion. Aiming at the failure of conditional D-S evidence theory in dealing with conflicting evidences, an improved D-S combination rule is recommended. The proposed method utilizes the information quantity of evidence and assigns the conflict factor to every focus element based on average support degree. The simulations show that the proposed D-S combination rule can effectively solve thorny problems for conflicting evidences.

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Liang, Qian, Zhongxin Liu, and Zengqiang Chen. "A Networked Method for Multi-Evidence-Based Information Fusion." Entropy 25, no. 1 (2022): 69. http://dx.doi.org/10.3390/e25010069.

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Dempster–Shafer evidence theory is an effective way to solve multi-sensor data fusion problems. After developing many improved combination rules, Dempster–Shafer evidence theory can also yield excellent results when fusing highly conflicting evidence. However, these approaches still have deficiencies if the conflicting evidence is due to sensor malfunction. This work presents a combination method by integrating information interaction graph and Dempster–Shafer evidence theory; thus, the multiple evidence fusion process is expressed as a network. In particular, the credibility of each piece of

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Zhang, Yang, Yun Liu, Qing-An Zeng, and Qing Liu. "An Integrated Data Combination Method in Wireless Sensor Networks." International Journal of Interdisciplinary Telecommunications and Networking 10, no. 4 (2018): 61–76. http://dx.doi.org/10.4018/ijitn.2018100104.

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This article proposes an integrated information fusion approach in wireless sensor networks (WSNs) based on the Dempster-Shafer evidence theory, which includes four main aspects: the construction of basic probability assignment; a novel reliability coefficient function converting similarity to initial weight factors; an improved fusion approach by reassigning reliability coefficients; and the “Discount Rule.” Utilizing the integrated approach, conflicting data are fused more accurately and effectively than using the traditional fusion method. Experimental results show that the combined belief

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Chen, Lei, Ling Diao, and Jun Sang. "Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/5858272.

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Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fau

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Vijayarajan, Jeyalakshmi. "Multi-Biometric System Based On The Fusion Of Fingerprint And Finger-Vein." ELCVIA Electronic Letters on Computer Vision and Image Analysis 23, no. 1 (2024): 32–46. http://dx.doi.org/10.5565/rev/elcvia.1822.

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Biometrics is the process of measuring the unique biological traits of an individual for identification and verification purposes. Multiple features are used to enhance the security and robustness of the system. This study concentrates exclusively on the finger and employs two modalities - fingerprint and finger vein. The proposed system utilizes feature extraction for finger vein and two matching algorithms, namely ridge-based matching, and minutiae-based matching, to derive matching scores for both biometrics. The scores from the two modalities are combined using four fusion approaches: holi

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Zhao, Peng, Zhen-Yu Li, and Cheng-Kun Wang. "Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion." Journal of Spectroscopy 2021 (July 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/6088435.

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A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership funct

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Maseleno, Andino, Md Mahmud Hasan, and Norjaidi Tuah. "Combining Fuzzy Logic and Dempster-Shafer Theory." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 3 (2015): 583. http://dx.doi.org/10.11591/tijee.v16i3.1651.

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This research aims to combine the mathematical theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer theory by calculating the similarity between Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The Dempster-Shafer mathematical theory of evidence has attracted considerable attention as a promising method of

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Alpert, Sofiia. "THE NEW APPROACH TO APPLYING THE DEZERT-SMARANDACHE THEORY IN LAND-COVER CLASSIFICATION IN UAV-BASED REMOTE SENSING." Management of Development of Complex Systems, no. 49 (April 11, 2022): 33–39. http://dx.doi.org/10.32347/2412-9933.2022.49.33-39.

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Nowadays UAV-based Remote Sensing gives a new opportunities for conducting scientific research in a much more detail way. Classification is one of the most important procedures in Remote Sensing tasks. This procedure can be applied in solution of numerous ecological and practical tasks, such as: forest classification, determing of soil types, exploring of oil and gas. Classification of incomplete, imprecise and high conflicting data has always been and still remains the one of most important procedures of remote sensing. In this paper the new approach to applying Dezert-Smarandache Theory in U

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