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

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  • 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
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    References listed on IDEAS

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    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.

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