New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion
Abstract Multisource information fusion technology significantly benefits from using information across various sources for decision-making, particularly by leveraging evidence theory to manage uncertain information efficiently. Nonetheless, dealing with highly conflicting evidence presents a consid...
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| Format: | Article |
| Language: | English |
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Springer
2024-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-06036-4 |
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| author | Xiaoyang Liu Cheng Xie Zhe Liu Sijia Zhu |
| author_facet | Xiaoyang Liu Cheng Xie Zhe Liu Sijia Zhu |
| author_sort | Xiaoyang Liu |
| collection | DOAJ |
| description | Abstract Multisource information fusion technology significantly benefits from using information across various sources for decision-making, particularly by leveraging evidence theory to manage uncertain information efficiently. Nonetheless, dealing with highly conflicting evidence presents a considerable challenge. To tackle this issue, this paper introduces a new belief divergence measure within the framework of evidence theory. The proposed measure, which incorporates the cosine function and pignistic probability transformation, is adept at quantifying the disparity between the evidences while maintaining key properties, such as boundedness, non-degeneracy and symmetry. Moreover, building upon the concepts of proposed belief divergence and belief entropy, this paper further proposes a new fusion method that employs a weighted evidence average prior to the application of Dempster’s rule. The performance of the proposed method is validated on several applications, and the results demonstrate its superior ability to absorb highly conflicting evidence compared with existing methods. |
| format | Article |
| id | doaj-art-ca3c64463c794bae85c382b86f9d958a |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-ca3c64463c794bae85c382b86f9d958a2025-01-12T12:35:16ZengSpringerDiscover Applied Sciences3004-92612024-06-016712110.1007/s42452-024-06036-4New belief divergence measure based on cosine function in evidence theory and application to multisource information fusionXiaoyang Liu0Cheng Xie1Zhe Liu2Sijia Zhu3School of Computer Science and Technology, Hainan UniversitySchool of Computer Science and Technology, Hainan UniversitySchool of Computer Sciences, Universiti Sains MalaysiaCw Chu College, Jiangsu Normal UniversityAbstract Multisource information fusion technology significantly benefits from using information across various sources for decision-making, particularly by leveraging evidence theory to manage uncertain information efficiently. Nonetheless, dealing with highly conflicting evidence presents a considerable challenge. To tackle this issue, this paper introduces a new belief divergence measure within the framework of evidence theory. The proposed measure, which incorporates the cosine function and pignistic probability transformation, is adept at quantifying the disparity between the evidences while maintaining key properties, such as boundedness, non-degeneracy and symmetry. Moreover, building upon the concepts of proposed belief divergence and belief entropy, this paper further proposes a new fusion method that employs a weighted evidence average prior to the application of Dempster’s rule. The performance of the proposed method is validated on several applications, and the results demonstrate its superior ability to absorb highly conflicting evidence compared with existing methods.https://doi.org/10.1007/s42452-024-06036-4Multisource information fusionDivergence measureCosine functionBasic probability assignmentEvidence theory |
| spellingShingle | Xiaoyang Liu Cheng Xie Zhe Liu Sijia Zhu New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion Discover Applied Sciences Multisource information fusion Divergence measure Cosine function Basic probability assignment Evidence theory |
| title | New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| title_full | New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| title_fullStr | New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| title_full_unstemmed | New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| title_short | New belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| title_sort | new belief divergence measure based on cosine function in evidence theory and application to multisource information fusion |
| topic | Multisource information fusion Divergence measure Cosine function Basic probability assignment Evidence theory |
| url | https://doi.org/10.1007/s42452-024-06036-4 |
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