Generalized Similarity Measure for Multisensor Information Fusion via Dempster-Shafer Evidence Theory
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 (RBLSM), which assesses discrepancies between the evidences by incorporating both belief and
plausibility functions. 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 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 RBLSM-based fusion method are demonstrated through
its applications in target recognition and pattern classification scenarios.
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This paper investigates the oscillatory behavior of nonlinear third-order dynamic equations
on time scales. Our main approach is to transform the equation from its semi-canonical form into a…
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…