Nonlinear Algorithms for Combining Conflicting Identification Information in Multisensor Fusion

Hurley, Jeffery D.; Johnson, Clint; Dunham, Joel; Simmons, Jimmy

doi:10.1109/aero.2019.8741967

Cited by 9 publications

(11 citation statements)

References 19 publications

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“…The first rule defined for this network is that all evidence updates at each node uses an evidential combination rule, of which there are multiple available [ 1 , 13 , 14 , 15 , 28 , 29 ] as shown in Figure 5 . This rule aligns with previous work [ 3 , 27 ].…”

Section: Transition Potentials Updates Based On Evidencementioning

confidence: 99%

“…The original DS rule [ 1 ] and ECR [ 15 ] apply when only the combined evidence is known at each update; the history of evidential inputs is not retained. Further, by combining Zhang’s rule [ 13 ] with sufficient evidence history, the winning decision will be emphasized, which is one of the primary propositions of the Rayleigh methods [ 28 ]. Finally, the chief property of Yager’s rule [ 29 ]—avoiding issues with Dempster’s rule [ 4 ]—is also a property of Murphy’s rule [ 14 ], Zhang’s rule [ 13 ], and ECR [ 15 ].…”

Section: Transition Potentials Updates Based On Evidencementioning

confidence: 99%

“…Due to the modifications in Figure 8 , it was necessary to introduce weights to algorithms which could be used in the Dempster-Shafer network. Murphy’s Rule [ 14 ] and the two Rayleigh methods [ 28 ] were easily modified to include weight by weighing the evidential inputs. For Murphy’s Rule, this equates to a weighted average.…”

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confidence: 99%

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Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Dunham

Johnson

Féron

et al. 2020

Sensors

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Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

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Section: Transition Potentials Updates Based On Evidencementioning

confidence: 99%

Section: Transition Potentials Updates Based On Evidencementioning

confidence: 99%

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Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Dunham

Johnson

Féron

et al. 2020

Sensors

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“…Conflicting management is still an open issue . Some of the means to resolve conflicts in evidence theory mentioned in Section are described in detail as follows.…”

Section: Related Work On Conflict Measurementmentioning

confidence: 99%

An association coefficient of a belief function and its application in a target recognition system

Pan

Deng

2019

Int J Intell Syst

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The conflict problem in D-S evidence theory has attracted the attention of many scholars. Conflict coefficients are proposed to describe conflicts between bodies of evidence. The association coefficient as the opposite of the conflict coefficient is also used to measure the conflict. The larger the association coefficient, the smaller the conflict degree, and the higher the similarity between the evidence bodies, and vice versa.In this paper, the degree of association is defined by Deng Entropy, and a new association coefficient is proposed based on the basic inequality. The nature of the new association coefficient and conflict coefficients is explored using examples. Finally, the association coefficient combined with the D-S combination rule is applied to the target recognition system, and accurate results are obtained.association coefficient, conflict coefficients, conflict problem, Deng entropy, D-S evidence theory, target recognition system | INTRODUCTIONThe D-S evidence theory was first proposed by Dempster 1 in 1967 and later developed by his student Shafer 2 in 1976 as an imprecise reasoning theory, which is widely promoted. [3][4][5][6] The theory was first applied to expert systems, 7-9 and it was gradually applied in the fields of information fusion, 10-12 intelligence analysis, 9,13-16 evidential reasoning, [17][18][19] and multiattribute decision analysis. [20][21][22][23][24] As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express "uncertainty" 25 and "don't know." 26 Therefore, the D-S evidence theory is flexible and convenient for describing uncertainties. However, D-S evidence theory has produced counterintuitive results in the fusion of a highly conflicting example proposed by Zadeh. 27 Therefore, the issue of conflict between bodies of evidence has attracted more and more researchers so far. [28][29][30][31][32] Domestic and foreign scholars have proposed a large number of improved methods, which can be divided into two major categories: (a) modification of the combination rules of the classical D-S evidence theory to achieve the redistribution of conflicts in the case of evidence conflicts. [33][34][35][36] Such measures alleviate the conflict between the bodies of evidence to a certain extent, but at the same time, it is difficult for the new rules to maintain the original properties of the D-S combination rules. (b) Keep the classic combination rules unchanged, and preprocess conflict data before combination. [37][38][39][40][41][42] Haenni 43 believes that the modification of the data model is in engineering, mathematics and philosophically more reasonable. However, regardless of the type of method used to deal with conflicts between evidence, the conflict coefficient is an extremely important parameter. Therefore, the most critical step before choosing the right combination of evidence is the measure of evidence conflict.To measure the conflict between evidence bodies, many researchers have proposed...

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“…Besides, D‐S evidence theory can handle imprecise and unknown information by assigning probabilities to the power set of events, which has attracted many people to study . For this reason, D‐S evidence theory has been used in many fields thanks to the flexibility and effectiveness in modelling uncertainties, such as pattern recognition, decision‐making, conflicting management, evidential reasoning and uncertainty modelling …”

Section: Introductionmentioning

confidence: 99%

Quantum model of mass function

Gao

Deng

2019

Int J Intell Syst

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Dempster-Shafer (D-S) evidence theory has been used in many fields due to the flexibility and effectiveness in modelling uncertainties, which is the extension of classical probability. Uncertainty principle is one of the most important principles in quantum theory, which has been used in many fields. How to set the connection between quantum theory and D-S evidence theory is also an open issue. Hence, the paper proposed the quantum model of mass function to consider the quantum theory and D-S evidence theory. In the proposed quantum method, quantum mass function uses euler formula to represent. The paper also discusses some operations based on the quantum model of the mass function. Moreover, the paper also discusses the relationship between quantum mass function and classical mass function by using some numerical examples. Classical mass function is a special case when there is no interference in quantum mass function. Similar to the other quantum models, this study provides a more wide application in quantum information. K E Y W O R D S basic probability assignment, belief function, Dempster-Shafer evidence theory, mass function, quantum theory

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Nonlinear Algorithms for Combining Conflicting Identification Information in Multisensor Fusion

Cited by 9 publications

References 19 publications

Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

An association coefficient of a belief function and its application in a target recognition system

Quantum model of mass function

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