数 据 和 知 识 驱 动 的 空 战 目 标 集 群 类 型 综 合 识 别

Identification of cluster types is the key to judging the cognition of combat situation. However，the existing cluster type identification algorithms are mainly based on expert knowledge for manual interpretation，imposing difficulty in satisfying the needs of rapid and accurate understanding of combat situation. To address this problem，we propose a reasoning mechanism driven by data and knowledge，constructing a cluster scene recognition framework for hierarchical refined reasoning. The pre-recognition layer detects the declustering/clustering of clusters during target movement，and determines the clustering based on the design of boundary detection-based density peaks clustering. Then，according to the division of the cluster，the preliminary identification results of the cluster are obtained. In the re-identification layer，the cluster execution tasks，motion characteristics，and electromagnetic characteristics are comprehensively analyzed and further utilized to construct an inference network under the constraint of multi-knowledge on the multi-source characteristics of the cluster target. Then，the existing data is used to learn the parameters of the inference network so that it can obtain more accurate cluster type identification results. The framework integrates knowledge and data to enable coarse to fine cluster target recognition，where the multi-feature comprehensive reasoning mechanism is used to comprehensively identify target clusters. This study realizes the refined identification of the cluster type，and the two indicators of inference confidence and accuracy are better than the existing algorithms in the typical cluster combat scenario，demonstrating the effectiveness of the proposed algorithm and improving the confidence and accuracy of aerial combat target cluster type identification.