A Modified D-S Decision-Making Algorithm for Multi-sensor Target Identification

Xiao-long, Liang; Feng, Jing; Liu, An

doi:10.1007/978-3-642-13498-2_45

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Additionally, we compute the weights using the approaches of Deng et al (2004) (similarity measures) and Liang et al (2010) (uncertainty and credibility degree). The calculations are shown in Table 11.…”

Section: Resultsmentioning

confidence: 99%

“…Sensor reading reliability needs to be incorporated into the fusion process so as to avoid decreasing system performance (Elouedi, Mellouli, & Smets, 2004;Liu, http://dx.doi.org/10.1016/j.ejor.2014.08.024 0377-2217/Ó 2014 Elsevier B.V. All rights reserved. Dezert, Pan, & Mercier, 2011;Liang, Feng, & Liu, 2010). In fact, the reliability of pieces of evidence is an index for quantifying sensor performance and weighing readings.…”

Section: Introductionmentioning

confidence: 99%

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Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Frikha

Moalla

2015

European Journal of Operational Research

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Frikha

Moalla

2015

European Journal of Operational Research

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“…The degree of consistency between two evidences reflects the degree of mutual support between the two pieces of evidence [45,46]; therefore, the support degree of each piece of evidence for Ej can be obtained by adding the rows or columns of the similarity matrix as follows [47]:Sup(mjfalse)=∑j=1ndij(i,j=1,2,…,n)…”

Section: Improvements To the Traditional Evidence Theorymentioning

confidence: 99%

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

Wang

2019

Sensors

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In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster–Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.

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Type-2 fuzzy blended improved D-S evidence theory based decision fusion for face recognition

Ghosh¹,

Dey

Kahali

2022

Applied Soft Computing

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A Modified D-S Decision-Making Algorithm for Multi-sensor Target Identification

Cited by 5 publications

References 5 publications

Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

Type-2 fuzzy blended improved D-S evidence theory based decision fusion for face recognition

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