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Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Author

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  • Shijun Xu

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

  • Yi Hou

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

  • Xinpu Deng

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

  • Kewei Ouyang

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

  • Ye Zhang

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

  • Shilin Zhou

    (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

Abstract

Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.

Suggested Citation

  • Shijun Xu & Yi Hou & Xinpu Deng & Kewei Ouyang & Ye Zhang & Shilin Zhou, 2021. "Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance," Energies, MDPI, vol. 14(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1143-:d:503262
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    References listed on IDEAS

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    1. Lanjun Wan & Hongyang Li & Yiwei Chen & Changyun Li, 2020. "Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory," Energies, MDPI, vol. 13(5), pages 1-23, March.
    2. Deyun Zhou & Yongchuan Tang & Wen Jiang, 2017. "A modified belief entropy in Dempster-Shafer framework," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
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    Cited by:

    1. Zhenen Li & Xinyan Zhang & Tusongjiang Kari & Wei Hu, 2021. "Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings," Energies, MDPI, vol. 14(15), pages 1-19, July.

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