多域作战下的群目标意图识别与预测

Recognition and prediction of group target intention in multi-domain operations

多域作战具有实体多类、队形多变、意图多样等诸多挑战,难以综合利用多元知识,因而主要依靠人工判决,以至于自动化水平不高。为了实现计算机自动推演态势,需要解决知识的图形化建模和意图推理综合两大难题。对此,在空海域管控知识图谱的基础上,搭建了多域作战战术规则库、编队队形与场景态势的映射关系,提出了基于多实体分层贝叶斯网络的群目标意图识别与预测方法。首先,运用群内目标实体的状态和事件信息,构建目标作战实体行为推理层。其次,利用综合作战实体的时序规则、双方相对距离及航向等信息,构建同类目标元意图推理层。最后,利用实体序列协作关系及编队队形信息,构建多域作战下的群目标总意图推理层。以航母群活动仿真数据为例,验证了所提算法能够获得较为可靠的意图推理结果。

There are many challenges in multi-domain operations, such as multiple entities, changeable formations and diverse intentions, which make it difficult to comprehensively utilize multiple knowledge. Therefore, manual judgment is mainly used, resulting in a low level of automation. In order to realize the automatic deduction of the situation by the computer, two difficult problems need to be solved: the graphical modeling of knowledge and the synthesis of intention reasoning. Based on the knowledge map of airspace management and control, a multi-domain operational tactical rule base, the mapping relationship between formation and scene situation is built. A multi-entity hierarchical Bayesian network-based group target intent recognition and prediction method is proposed. Firstly, the state and event information of the target entity in the group is used to construct the behavioral reasoning layer of the target combat entity. Secondly, based on the temporal rules, relative distance and heading information of combat entities, a reasoning layer of similar target element intention is constructed. Finally, the general intention reasoning layer of group targets under multi-domain operations is constructed by using the entity sequence collaboration relationship and formation information. Taking the simulation data of aircraft carrier group activities as an example, it is verified that the algorithm proposed in this paper can obtain relatively reliable intention inference results.