Knowledge Based Nested Frames of Discernment for Target Integrated Identification

In the context of multiple attributive classification, different frames of discernment (FoD) are often used to address discrepant identity attribute identification for the same target. Though these frames based on different tasks and purposes employ distinct perspectives and structures to identify targets, they exhibit complementary information mapping and transformation modes, revealing nested relationships among multiple FoD. Leveraging this relationship, we can expand the information source of a single FoD and enhance the decision accuracy of integrated target identification. However, the classification results obtained from multiple FoD remain uncertain due to the unknown sensor error in the transformation relation and the randomness of information acquisition. Therefore, this paper aims to develop a integrated identification algorithm for traffic targets based on the transformation relation of nested frames of discernment (NFoD), and construct nested frames transformation matrix and basic belief assignment (BBA) mapping rules using domain rule knowledge and historical data. The nested frame mapping results are obtained by minimizing target distance transformation optimization and calculating the transformation matrix, and the original belief assignment is fused to realize effective transformation between multiple FoD. Simulation results demonstrate that the proposed algorithm can obtain more accurate results compared to the single FoD, which effectively confirms that the transformation fusion method utilizing the NFoD can improve the capability of the integrated target identification system.