Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends on how a BPA is constructed for each source. Existing methods often rely on heuristic mappings or restrict belief assignment to singleton hypotheses and the full set, thereby underutilizing the expressive belief structure of BPAs for representing ambiguity.This paper proposes Negation-Aware Weighted Information Fusion (NAWIF).First, NAWIF transforms the class-wise possibility distribution of each source into a set of negation-aware simple BPAs, which encode class exclusion through complementary propositions and retain residual uncertainty in the full set. Their combination produces a closed-form BPA over composite hypotheses, preserving ambiguity among the classes not excluded by the source. It further performs source-specific BPA discounting based on its reliability and uncertainty, suppressing the impact of unreliable or uninformative sources.Experiments on benchmark pattern recognition datasets demonstrate the effectiveness of NAWIF, which achieves state-of-the-art average performance with particularly pronounced advantages under noise disturbance.

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