IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i4p1550147719841295.html
   My bibliography  Save this article

A new method to measure the divergence in evidential sensor data fusion

Author

Listed:
  • Yutong Song
  • Yong Deng

Abstract

Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other. Recent researches show that weighting the evidences with the consideration of its corresponding credibility is an efficient methodology. As a result, how to determine the weight is an important issue. In this article, a new divergence measure of BPA is proposed based on geometric mean of Deng relative entropy. The weight of each evidence is determined by the proposed divergence measure and information volume. Compared with the existing belief Jensen–Shannon divergence, the proposed method has a better performance in the convergence to the correct target. The result shows that the proposed method outperforms other related methods, giving the highest belief value 98.98% to the correct target.

Suggested Citation

  • Yutong Song & Yong Deng, 2019. "A new method to measure the divergence in evidential sensor data fusion," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719841295
    DOI: 10.1177/1550147719841295
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719841295
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719841295?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xin Guan & Haiqiao Liu & Xiao Yi & Jing Zhao, 2018. "The Improved Combination Rule of D Numbers and Its Application in Radiation Source Identification," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, September.
    2. Deng, Xinyang & Jiang, Wen & Wang, Zhen, 2019. "Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 101-112.
    3. Yin, Likang & Deng, Yong, 2018. "Toward uncertainty of weighted networks: An entropy-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 176-186.
    4. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shijun Xu & Yi Hou & Xinpu Deng & Peibo Chen & Kewei Ouyang & Ye Zhang, 2021. "A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.
    2. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Shang Gao & Yong Deng, 2019. "An evidential evaluation of nuclear safeguards," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    4. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    2. Wu, Yu’e & Zhang, Zhipeng & Yang, Guoli & Liu, Haixin & Zhang, Qingfeng, 2022. "Evolution of cooperation driven by diversity on a double-layer square lattice," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Yu Zhang & Wen Jiang & Xinyang Deng, 2019. "Fault diagnosis method based on time domain weighted data aggregation and information fusion," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    4. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
    5. Liming Mu & Yingzhi Zhang & Qiyan Zhang, 2023. "Risk Evaluation Method Based on Fault Propagation and Diffusion," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    6. Yuegang Song & Ruibing Wu, 2022. "The Impact of Financial Enterprises’ Excessive Financialization Risk Assessment for Risk Control based on Data Mining and Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1245-1267, December.
    7. Xiaozhuan Gao & Yong Deng, 2019. "The generalization negation of probability distribution and its application in target recognition based on sensor fusion," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
    8. Hamzeh Soltanali & Mehdi Khojastehpour & José Torres Farinha & José Edmundo de Almeida e Pais, 2021. "An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing," Energies, MDPI, vol. 14(22), pages 1-21, November.
    9. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. Karar, Ahmed Noaman & Labib, Ashraf & Jones, Dylan, 2023. "Post-warranty maintenance strategy selection using shape packages process," International Journal of Production Economics, Elsevier, vol. 255(C).
    11. Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    12. Liu, Gehui & Chen, Shaokuan & Jin, Hua & Liu, Shuang, 2021. "Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    14. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    15. Shang Gao & Yong Deng, 2019. "An evidential evaluation of nuclear safeguards," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    16. Deng, Xinyang & Jiang, Wen & Wang, Zhen, 2020. "An Information Source Selection Model Based on Evolutionary Game Theory," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    17. Wen Jiang & Zeyu Ma & Xinyang Deng, 2019. "An attack-defense game based reliability analysis approach for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    18. Mena, R. & Viveros, P. & Zio, E. & Campos, S., 2021. "An optimization framework for opportunistic planning of preventive maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Alexander Brem & Dominic T. J. O’Sullivan & Ken Bruton, 2021. "Advancing the Industrial Sectors Participation in Demand Response within National Electricity Grids," Energies, MDPI, vol. 14(24), pages 1-26, December.
    20. Fco. Javier García-Gómez & Víctor Fco. Rosales-Prieto & Alberto Sánchez-Lite & José Luis Fuentes-Bargues & Cristina González-Gaya, 2021. "An Approach to Sustainability Risk Assessment in Industrial Assets," Sustainability, MDPI, vol. 13(12), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719841295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.