基于动态序列贝叶斯网络的空地协同作战意图识别

Air-ground cooperative operations intention recognition based on Dynamic Series Bayesian Network

现代军事活动中,空地协同多编队样式越发重要。已有的目标意图识别方法对单一编队效果较好,但对空中和地面协同的多编队场景尚缺乏有力的解决方法。因此,采用动态序列贝叶斯网络(Dynamic Series Bayesian Network,DSBN)对空地协同编队进行意图识别。该方法首先利用DSBN构建了一个空地协同作战意图识别整体模型,用于描述空中和地面编队之间的协同行动过程,然后通过将不同战场域的事件及其相关概率关系进行融合,结合辅助战场信息,使用推理网络实现对敌方协同作战意图的识别。该方法充分考虑了空中目标的行为规则,精细描述其行为模式和趋势,更好地适用于多协同目标编队的场景。最后通过实例仿真验证了该方法的可行性和有效性。

In modern military warfare,the pattern of air-ground coordination with multi-formation has become more and more important.However,the existing target intention recognition methods are effective for single formation,but lack of effective solutions for multi-formation scenarios with air and ground coordination.In this paper,Dynamic Series Bayesian Network is used to identify the intention of air-to-ground cooperative formation.This method firstly constructs an overall model of intention recognition of air-to-ground cooperative formation by using DSBN,which is used to describe the cooperative action process between air and ground formations.Then,events in different battlefield domains and their related probability relations are fused together with auxiliary battlefield information.The inference network is used to recognize the intention of enemy cooperative formation.This method fully considers the behavior rules of the air target,describes its behavior pattern and trend in detail,and is more suitable for the scenario of multi-cooperative target formation.Finally,the feasibility and effectiveness of this method is verified by simulation example.