Evidence modeling and fusion method of conflict sensor information based on complex network
Springer Link (Chiba Institute of Technology) · 2026
Evidence modeling and fusion accuracy of multi-sensor information directly determines the target recognition 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 unreliable 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 information, 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 between evidence. Specifically, the direct weights between network nodes are modeled by evidence distance to represent the similarity between evidence bodies, and the indirect weights reflect the indirect support relationships of evidence 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 reliability.