针对无人船多传感器航迹关联中数值误差随机与认知模糊并存的混合不确定性，提出一种融合区间数与区间二维二元语义的关联方法，称之为“混合不确定性与动态加权DS融合法”。首先以区间数描述测量/系统误差，并用区间二维二元语义表达专家置信，构建数值—语义综合支持度；继而将支持度映射为区间基本概率分配（IBPA），并采用Dirichlet-蒙特卡洛采样获得自适应权重，实施动态加权的区间D-S融合。在两传感器、20个目标的海上场景中进行100次蒙特卡洛仿真，与最近邻、仅数值、仅语义、等权DS等方法对比。结果表明：本方法提升目标对的区分度与判别稳定性，平均正确关联率达96.15%，不确定性与方差明显降低，鲁棒性与可解释性更优。创新在于提出“支持度→IBPA”的区间映射与面向混合不确定性的动态加权区间DS融合，实现数值误差与语言不确定性的统一表示与融合。

To address the mixed uncertainty arising from concurrent numerical errors and cognitive vagueness in multi-sensor track association for unmanned surface vessels (USVs), we propose an association method that jointly exploits interval numbers and interval two-dimensional two-tuple (2D2T) linguistic semantics, termed the “Hybrid Uncertainty Modeling” method. Measurement and system errors are modeled as interval numbers, while expert confidence is encoded via interval 2D2T linguistic semantics to construct a hybrid numerical–linguistic support measure. This support is then mapped to an interval basic probability assignment (IBPA), and Dirichlet–Monte Carlo sampling is used to derive adaptive weights for a dynamically weighted interval Dempster–Shafer (D–S) fusion. We conduct 100 Monte Carlo simulations in a two-sensor, twenty-target maritime scenario, benchmarking against nearest-neighbor, numerical-only, linguistic-only, and equal-weight D–S baselines. The proposed approach markedly improves pairwise target separability and decision stability, achieving an average correct association rate of 96.15%, while simultaneously reducing uncertainty and variance and enhancing robustness and interpretability. The principal contributions are: (i) a unified representation and fusion framework for numerical error and linguistic uncertainty, and (ii) an interval “support → IBPA” mapping coupled with a dynamically weighted interval D–S fusion tailored to mixed uncertainty.