基于图模型自主优化的多无人机多目标攻击

为在多无人作战飞机(unmanned combat aerial vehicle, UCAV)执行多目标攻击中适时确立决策优化的方向、改变任务优化所需的基本条件，采用图模型中的动态贝叶斯网络(dynamic Bayesian network, DBN)构建了空天威胁体感知模型，提出了基于图模型自主优化系统的分层架构和多UCAV自主协同规划方法。该方法利用数据融合形成的DBN状态转移网络及观测转移网络参数的变化表现复杂空天环境的变化，并充分利用DBN的学习和推理算法，实现了对威胁体的在线动态感知，达到了按照确定原则完成UCAV攻击目标重新分配与航迹协同等任务的目的。仿真结果表明了这种自主优化规划方法的正确性和可行性。

Autonomous optimization for multiple-target attack of multiple UCAVs based on graphical model

For the sake of changing main conditions required by multi-targets attack optimization and establishing the new direction of the decision-making optimization during attacking action executing by the multiple unmanned combat aerial vehicle(UCAV),a dynamic awareness model for threats based on a kind of graphical model,dynamic Bayesian network(DBN),is constructed.Based on this graphical model,a novel layered conceptual framework for autonomous optimization systems and an autonomous cooperative planning method ...