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.
Micromobility is an innovative urban transport solution that can effectively tackle greenhouse gases and reduce
the use of private vehicles, especially for short-distance travel options. As…
This study presents novel and generalizable sufficient conditions for determining
the oscillatory behavior of solutions to higher-order half-linear neutral delay dynamic
equations on…
As the importance of environmental and social responsibility gains momentum, the financial sector is ever more
aware of its dynamic role in assisting the evolution to a low-carbon economy…