摘要
针对动态战场环境下无人机自主任务决策问题,提出一种基于时变离散动态贝叶斯网络的决策模型学习算法。在获得的前一时刻模型结构和参数基础上,通过粒子滤波算法动态更新模型结构和参数状态,利用实时获得的非平稳观测信息,完成当前时刻的任务决策推理。仿真结果表明,该模型学习算法能够在威胁联网的战场环境中保证无人机任务决策的有效性。
Aiming at the problem of unmanned aerial vehicle（UAV）mission decision-making in the dynamic battlefield environment,a learning algorithm based on time varying discrete dynamic Bayesian network（TVDDBN）was proposed.On the basis of the model structure and parameters at the previous time epoch,the states of both structure and parameters were dynamically updated by employing particle filtering,and the inference of the current mission decision-making was achieved by using the non-stationary observation information up to current time.The simulation results show that the proposed algorithm can guarantee the effectiveness of the UAV mission decision-making under the conditions of threat netting.
出处
《计算机工程与设计》
北大核心
2016年第5期1346-1351,共6页
Computer Engineering and Design
基金
国家自然科学基金项目(61440048
61162010)
国家国际科技合作专项基金项目(2015DFR10510)
海南省自然科学基金项目(614227)
海南大学青年基金项目(qnjj1243)
关键词
无人机
时变离散动态贝叶斯网络
威胁联网
任务决策
粒子滤波
unmanned aerial vehicle
time varying discrete dynamic Bayesian network（TVDDBN）
threat netting
mission decision-making
particle filtering
