A new combination of evidence based on compromise

Yamada, Kôichi

doi:10.1016/j.fss.2007.11.019

Cited by 50 publications

(34 citation statements)

References 27 publications

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“…• Other authors share the mass m 1 (A 1 ) · m 2 (A 2 ) between A 1 and A 2 when they are disjoint [108], and more generally between A 1 ∩ A 2 , A 2 \ A 1 , and A 1 \ A 2 regardless of whether A 1 and A 2 are disjoint or not [127].…”

Section: Fusion Of Belief Functionsmentioning

confidence: 99%

Formal Representations of Uncertainty

Dubois¹,

Prade²

2009

Decision‐making Process

100

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Section: Fusion Of Belief Functionsmentioning

confidence: 99%

Formal Representations of Uncertainty

Dubois¹,

Prade²

2009

Decision‐making Process

100

View full text Add to dashboard Cite

“…In order to achieve the identification, the features used to emitter recognition must have time-invariant feature, scale transform feature and phase stability feature [12]. Thus, this paper has selected wavelet analysis and selected bispectra to extracting features in target signal.…”

Section: Proposed Method's Application In Emitter Identificationmentioning

confidence: 99%

“…The commutative and associative characteristic of the Dempster's fusion rule is one important reason why the evidence theory is widely applied in diverse areas. Although various improved algorithms of the latter idea can convert the conflict section into other focal elements, these algorithms generally destroy the above favorable properties of the fusing rule [12]. The first type of methodology neglects fusion efficiency and condition that there is practically no conflict among the evidences.…”

Section: Figure 1 Functional Block Diagram Of Emitter Identificationmentioning

confidence: 99%

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Sequential emitter identification method based on D-S evidence theory

et al. 2018

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This paper proposes a novel sequential identification method for enhancing the antijamming performance and for accurate recognition rate of the emitters' individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information.

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“…When two pieces of evidence are collected from two distinct sources, it is necessary to combine them to get an overall result. So far, many combination rules (e.g., [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], etc.) have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Revision Rules in the Theory of Evidence

Liu

Dubois

et al. 2010

2010 22nd IEEE International Conference on Tools With Artificial Intelligence

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Abstract-Combination rules proposed so far in the DempsterShafer theory of evidence, especially Dempster rule, rely on a basic assumption, that is, pieces of evidence being combined are considered to be on a par, i.e. play the same role. When a source of evidence is less reliable than another, it is possible to discount it and then a symmetric combination operation is still used. In the case of revision, the idea is to let prior knowledge of an agent be altered by some input information. The change problem is thus intrinsically asymmetric. Assuming the input information is reliable, it should be retained whilst the prior information should be changed minimally to that effect. Although belief revision is already an important subfield of artificial intelligence, so far, it has been little addressed in evidence theory. In this paper, we define the notion of revision for the theory of evidence and propose several different revision rules, called the inner and outer revisions, and a modified adaptive outer revision, which better corresponds to the idea of revision. Properties of these revision rules are also investigated.

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A new combination of evidence based on compromise

Cited by 50 publications

References 27 publications

Formal Representations of Uncertainty

Formal Representations of Uncertainty

Sequential emitter identification method based on D-S evidence theory

Revision Rules in the Theory of Evidence

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