| Autor: |
Zhe Liu, Ibrahim M. Hezam, Sukumar Letchmunan, Haoye Qiu, Ahmad M. Alshamrani |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 104629-104642 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3435459 |
| Popis: |
Dempster-Shafer evidence theory (DSET) stands out as a mathematical model for handling imperfect data, garnering significant interest across various domains. However, a notable limitation of DSET is Dempster’s rule, which can lead to counterintuitive outcomes in cases of highly conflicting evidence. To mitigate this issue, this paper introduces a novel reinforced belief logarithmic similarity measure ( $\mathcal {RBLSM}$ ), which assesses discrepancies between the evidences by incorporating both belief and plausibility functions. $\mathcal {RBLSM}$ exhibits several intriguing properties including boundedness, symmetry, and non-degeneracy, making it a robust tool for analysis. Furthermore, we develop a new multisensor information fusion method based on $\mathcal {RBLSM}$ . The proposed method uniquely integrates credibility weight and information volume weight, offering a more comprehensive reflection the reliability of each evidence. The effectiveness and practicality of the proposed $\mathcal {RBLSM}$ -based fusion method are demonstrated through its applications in target recognition and pattern classification scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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