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<p>In the domain of multi-sensor data fusion, effectively handling and modeling uncertainty of information can improve information quality, thereby improving the accuracy of the fusion results. Dempster-Shafer(D-S) evidence theory offers a powerful approach due to its adaptability and potency in handling and modeling the indeterminate information. Nevertheless, due to its reliability hypothesis, further applications are limited, which reduces the accuracy of the fusion results. To address this issue, complex-valued evidence theory uses phase angle and amplitude to model random information and reliability information. But it is worth studying how to measure the difference between two mass functions, as it can improve the precision of the fusion results. This paper presents an innovative complex-valued belief divergence that considers the influence of phase angle to measure the difference of information. Additionally, we design an information fusion model based on the proposed divergence and it can be verified by some real-world datasets.</p>

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Complex-valued belief divergence measure and its application on information fusion

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Mengzhuo Zhang,
  • Yifan Sun,
  • Xiaozhuan Gao,
  • Lipeng Pan

摘要

In the domain of multi-sensor data fusion, effectively handling and modeling uncertainty of information can improve information quality, thereby improving the accuracy of the fusion results. Dempster-Shafer(D-S) evidence theory offers a powerful approach due to its adaptability and potency in handling and modeling the indeterminate information. Nevertheless, due to its reliability hypothesis, further applications are limited, which reduces the accuracy of the fusion results. To address this issue, complex-valued evidence theory uses phase angle and amplitude to model random information and reliability information. But it is worth studying how to measure the difference between two mass functions, as it can improve the precision of the fusion results. This paper presents an innovative complex-valued belief divergence that considers the influence of phase angle to measure the difference of information. Additionally, we design an information fusion model based on the proposed divergence and it can be verified by some real-world datasets.