基于复杂网络的冲突传感器信息证据建模与融合方法

Evidence modeling and fusion accuracy of multi⁃sensor information directly determines the target recogni⁃ tion performance. As a classic framework for uncertain information reasoning and fusion, Dempster⁃Shafer evidence theory has been widely applied in multi⁃source information fusion. However, when there is a strong conflict between evidence bodies, the direct application of Dempster′s combination rule often leads to counter⁃intuitive and even un⁃ reliable fusion results. Although existing improved methods alleviate the conflict problem to a certain extent, they still have limitations such as slow convergence speed, insufficient ability to suppress interference from unreliable in⁃ formation, and redundant network modeling. To address the above issues, this paper proposes an evidence modeling and fusion method based on complex networks. The method maps evidence bodies to network nodes and introduces a dual⁃weight complementary modeling mechanism of direct and indirect weights based on the interrelationships be⁃ tween evidence. Specifically, the direct weights between network nodes are modeled by evidence distance to repre⁃ sent the similarity between evidence bodies, and the indirect weights reflect the indirect support relationships of evi⁃ dence bodies in the network structure through cosine similarity. By fusing and normalizing the two types of weights, the adaptive correction of the original evidence bodies is achieved, and then Dempster′s combination rule is used to complete the fusion of conflicting uncertain information. Experimental results show that the proposed method exhibits faster convergence speed of target evidence, stronger interference suppression capability, and more effective high⁃ conflict evidence resolution performance in the multi⁃evidence fusion process, demonstrating good stability and reli⁃ ability.