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A New Multi-source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment

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Belief Functions: Theory and Applications (BELIEF 2021)

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Abstract

Dempster-Shafer theory (DST) can effectively distinguish between imprecise information and unknown information, which is widely used in information fusion. However, when the evidence highly contradicts each other, it may lead to counter-intuitive results. In addition, the existing information fusion methods do not take the negation of BPA into consideration, which can be improved. In this paper, we propose a new information fusion method by taking into account not only the information in basic probability assignment (BPA) but also the information contained in the negation of BPA. In the method, the belief divergence measure is not only used to calculate the difference between BPA and its negative BPA to reflect the information volume carried by its initial BPA, but also to calculate the difference between BPA and other BPA to consider the discrepancy between evidence. The efficiency of the method is verified by case studies.

Supported by the National Science and Technology Major Project (Program No. 2017-V-0011-0062).

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Author information

Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Hongfei Wang, Wen Jiang, Xinyang Deng & Jie Geng

  2. Peng Cheng Laboratory, Shenzhen, 518055, China

    Wen Jiang

  3. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China

    Wen Jiang & Xinyang Deng

Authors
  1. Hongfei Wang
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  2. Wen Jiang
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  3. Xinyang Deng
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  4. Jie Geng
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Corresponding author

Correspondence to Wen Jiang .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Thierry Denœux

  2. Université d'Artois, Béthune, France

    Eric Lefèvre

  3. Northwestern Polytechnical University, Xi'an, China

    Zhunga Liu

  4. Université d'Artois, Béthune, France

    Frédéric Pichon

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Wang, H., Jiang, W., Deng, X., Geng, J. (2021). A New Multi-source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_24

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  • DOI: https://doi.org/10.1007/978-3-030-88601-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88600-4

  • Online ISBN: 978-3-030-88601-1

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